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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Sun, 11 Dec 2011 05:26:16 -0500

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/2011/Dec/11/t1323599235ubh1knx0bk7d0hd.htm/, Retrieved Sun, 28 Apr 2024 20:10:30 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153661, Retrieved Sun, 28 Apr 2024 20:10:30 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

178

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

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2010-12-09 20:38:11] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
- R PD    [Recursive Partitioning (Regression Trees)] [] [2011-12-11 10:14:36] [06c08141d7d783218a8164fd2ea166f2]
-   P         [Recursive Partitioning (Regression Trees)] [] [2011-12-11 10:26:16] [ce4468323d272130d499477f5e05a6d2] [Current]
- R  D          [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-12 21:24:06] [d1ce18d003fa52f731d1c3ce8b58d5f9] 

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Dataseries X:

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Histogram

Boxplots

2	7	41	38	13	12	14	12	53	32
2	5	39	32	16	11	18	11	86	51
2	5	30	35	19	15	11	14	66	42
1	5	31	33	15	6	12	12	67	41
2	8	34	37	14	13	16	21	76	46
2	6	35	29	13	10	18	12	78	47
2	5	39	31	19	12	14	22	53	37
2	6	34	36	15	14	14	11	80	49
2	5	36	35	14	12	15	10	74	45
2	4	37	38	15	6	15	13	76	47
1	6	38	31	16	10	17	10	79	49
2	5	36	34	16	12	19	8	54	33
1	5	38	35	16	12	10	15	67	42
2	6	39	38	16	11	16	14	54	33
2	7	33	37	17	15	18	10	87	53
1	6	32	33	15	12	14	14	58	36
1	7	36	32	15	10	14	14	75	45
2	6	38	38	20	12	17	11	88	54
1	8	39	38	18	11	14	10	64	41
2	7	32	32	16	12	16	13	57	36
1	5	32	33	16	11	18	7	66	41
2	5	31	31	16	12	11	14	68	44
2	7	39	38	19	13	14	12	54	33
2	7	37	39	16	11	12	14	56	37
1	5	39	32	17	9	17	11	86	52
2	4	41	32	17	13	9	9	80	47
1	10	36	35	16	10	16	11	76	43
2	6	33	37	15	14	14	15	69	44
2	5	33	33	16	12	15	14	78	45
1	5	34	33	14	10	11	13	67	44
2	5	31	28	15	12	16	9	80	49
1	5	27	32	12	8	13	15	54	33
2	6	37	31	14	10	17	10	71	43
2	5	34	37	16	12	15	11	84	54
1	5	34	30	14	12	14	13	74	42
1	5	32	33	7	7	16	8	71	44
1	5	29	31	10	6	9	20	63	37
1	5	36	33	14	12	15	12	71	43
2	5	29	31	16	10	17	10	76	46
1	5	35	33	16	10	13	10	69	42
1	5	37	32	16	10	15	9	74	45
2	7	34	33	14	12	16	14	75	44
1	5	38	32	20	15	16	8	54	33
1	6	35	33	14	10	12	14	52	31
2	7	38	28	14	10	12	11	69	42
2	7	37	35	11	12	11	13	68	40
2	5	38	39	14	13	15	9	65	43
2	5	33	34	15	11	15	11	75	46
2	4	36	38	16	11	17	15	74	42
1	5	38	32	14	12	13	11	75	45
2	4	32	38	16	14	16	10	72	44
1	5	32	30	14	10	14	14	67	40
1	5	32	33	12	12	11	18	63	37
2	7	34	38	16	13	12	14	62	46
1	5	32	32	9	5	12	11	63	36
2	5	37	32	14	6	15	12	76	47
2	6	39	34	16	12	16	13	74	45
2	4	29	34	16	12	15	9	67	42
1	6	37	36	15	11	12	10	73	43
2	6	35	34	16	10	12	15	70	43
1	5	30	28	12	7	8	20	53	32
1	7	38	34	16	12	13	12	77	45
2	6	34	35	16	14	11	12	77	45
2	8	31	35	14	11	14	14	52	31
2	7	34	31	16	12	15	13	54	33
1	5	35	37	17	13	10	11	80	49
2	6	36	35	18	14	11	17	66	42
1	6	30	27	18	11	12	12	73	41
2	5	39	40	12	12	15	13	63	38
1	5	35	37	16	12	15	14	69	42
1	5	38	36	10	8	14	13	67	44
2	5	31	38	14	11	16	15	54	33
2	4	34	39	18	14	15	13	81	48
1	6	38	41	18	14	15	10	69	40
1	6	34	27	16	12	13	11	84	50
2	6	39	30	17	9	12	19	80	49
2	6	37	37	16	13	17	13	70	43
2	7	34	31	16	11	13	17	69	44
1	5	28	31	13	12	15	13	77	47
1	7	37	27	16	12	13	9	54	33
1	6	33	36	16	12	15	11	79	46
1	5	37	38	20	12	16	10	30	0
2	5	35	37	16	12	15	9	71	45
1	4	37	33	15	12	16	12	73	43
2	8	32	34	15	11	15	12	72	44
2	8	33	31	16	10	14	13	77	47
1	5	38	39	14	9	15	13	75	45
2	5	33	34	16	12	14	12	69	42
2	6	29	32	16	12	13	15	54	33
2	4	33	33	15	12	7	22	70	43
2	5	31	36	12	9	17	13	73	46
2	5	36	32	17	15	13	15	54	33
2	5	35	41	16	12	15	13	77	46
2	5	32	28	15	12	14	15	82	48
2	6	29	30	13	12	13	10	80	47
2	6	39	36	16	10	16	11	80	47
2	5	37	35	16	13	12	16	69	43
2	6	35	31	16	9	14	11	78	46
1	5	37	34	16	12	17	11	81	48
1	7	32	36	14	10	15	10	76	46
2	5	38	36	16	14	17	10	76	45
1	6	37	35	16	11	12	16	73	45
2	6	36	37	20	15	16	12	85	52
1	6	32	28	15	11	11	11	66	42
2	4	33	39	16	11	15	16	79	47
1	5	40	32	13	12	9	19	68	41
2	5	38	35	17	12	16	11	76	47
1	7	41	39	16	12	15	16	71	43
1	6	36	35	16	11	10	15	54	33
2	9	43	42	12	7	10	24	46	30
2	6	30	34	16	12	15	14	82	49
2	6	31	33	16	14	11	15	74	44
2	5	32	41	17	11	13	11	88	55
1	6	32	33	13	11	14	15	38	11
2	5	37	34	12	10	18	12	76	47
1	8	37	32	18	13	16	10	86	53
2	7	33	40	14	13	14	14	54	33
2	5	34	40	14	8	14	13	70	44
2	7	33	35	13	11	14	9	69	42
2	6	38	36	16	12	14	15	90	55
2	6	33	37	13	11	12	15	54	33
2	9	31	27	16	13	14	14	76	46
2	7	38	39	13	12	15	11	89	54
2	6	37	38	16	14	15	8	76	47
2	5	33	31	15	13	15	11	73	45
2	5	31	33	16	15	13	11	79	47
1	6	39	32	15	10	17	8	90	55
2	6	44	39	17	11	17	10	74	44
2	7	33	36	15	9	19	11	81	53
2	5	35	33	12	11	15	13	72	44
1	5	32	33	16	10	13	11	71	42
1	5	28	32	10	11	9	20	66	40
2	6	40	37	16	8	15	10	77	46
1	4	27	30	12	11	15	15	65	40
1	5	37	38	14	12	15	12	74	46
2	7	32	29	15	12	16	14	82	53
1	5	28	22	13	9	11	23	54	33
1	7	34	35	15	11	14	14	63	42
2	7	30	35	11	10	11	16	54	35
2	6	35	34	12	8	15	11	64	40
1	5	31	35	8	9	13	12	69	41
2	8	32	34	16	8	15	10	54	33
1	5	30	34	15	9	16	14	84	51
2	5	30	35	17	15	14	12	86	53
1	5	31	23	16	11	15	12	77	46
2	6	40	31	10	8	16	11	89	55
2	4	32	27	18	13	16	12	76	47
1	5	36	36	13	12	11	13	60	38
1	5	32	31	16	12	12	11	75	46
1	7	35	32	13	9	9	19	73	46
2	6	38	39	10	7	16	12	85	53
2	7	42	37	15	13	13	17	79	47
1	10	34	38	16	9	16	9	71	41
2	6	35	39	16	6	12	12	72	44
2	8	35	34	14	8	9	19	69	43
2	4	33	31	10	8	13	18	78	51
2	5	36	32	17	15	13	15	54	33
2	6	32	37	13	6	14	14	69	43
2	7	33	36	15	9	19	11	81	53
2	7	34	32	16	11	13	9	84	51
2	6	32	35	12	8	12	18	84	50
2	6	34	36	13	8	13	16	69	46

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	'AstonUniversity' @ aston.wessa.net

\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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153661&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153661&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153661&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	'AstonUniversity' @ aston.wessa.net

Goodness of Fit
Correlation	0.5678
R-squared	0.3224
RMSE	1.8515

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5678 \tabularnewline
R-squared & 0.3224 \tabularnewline
RMSE & 1.8515 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153661&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5678[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3224[/C][/ROW]
[ROW][C]RMSE[/C][C]1.8515[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153661&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153661&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	0.5678
R-squared	0.3224
RMSE	1.8515

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	13	15.2134831460674	-2.21348314606742
2	16	15.2134831460674	0.786516853932584
3	19	16.625	2.375
4	15	12.3636363636364	2.63636363636364
5	14	16.625	-2.625
6	13	15.2134831460674	-2.21348314606742
7	19	15.2134831460674	3.78651685393258
8	15	16.625	-1.625
9	14	15.2134831460674	-1.21348314606742
10	15	12.3636363636364	2.63636363636364
11	16	15.2134831460674	0.786516853932584
12	16	15.2134831460674	0.786516853932584
13	16	15.2134831460674	0.786516853932584
14	16	15.2134831460674	0.786516853932584
15	17	16.625	0.375
16	15	15.2134831460674	-0.213483146067416
17	15	15.2134831460674	-0.213483146067416
18	20	15.2134831460674	4.78651685393258
19	18	15.2134831460674	2.78651685393258
20	16	15.2134831460674	0.786516853932584
21	16	15.2134831460674	0.786516853932584
22	16	13.8947368421053	2.10526315789474
23	19	16.625	2.375
24	16	15.2134831460674	0.786516853932584
25	17	15.2134831460674	1.78651685393258
26	17	16.625	0.375
27	16	15.2134831460674	0.786516853932584
28	15	16.625	-1.625
29	16	15.2134831460674	0.786516853932584
30	14	15.2134831460674	-1.21348314606742
31	15	13.8947368421053	1.10526315789474
32	12	12.3636363636364	-0.363636363636363
33	14	15.2134831460674	-1.21348314606742
34	16	15.2134831460674	0.786516853932584
35	14	15.2134831460674	-1.21348314606742
36	7	12.3636363636364	-5.36363636363636
37	10	12.3636363636364	-2.36363636363636
38	14	15.2134831460674	-1.21348314606742
39	16	13.8947368421053	2.10526315789474
40	16	15.2134831460674	0.786516853932584
41	16	15.2134831460674	0.786516853932584
42	14	15.2134831460674	-1.21348314606742
43	20	16.625	3.375
44	14	15.2134831460674	-1.21348314606742
45	14	15.2134831460674	-1.21348314606742
46	11	15.2134831460674	-4.21348314606742
47	14	16.625	-2.625
48	15	15.2134831460674	-0.213483146067416
49	16	15.2134831460674	0.786516853932584
50	14	15.2134831460674	-1.21348314606742
51	16	16.625	-0.625
52	14	15.2134831460674	-1.21348314606742
53	12	15.2134831460674	-3.21348314606742
54	16	16.625	-0.625
55	9	12.3636363636364	-3.36363636363636
56	14	12.3636363636364	1.63636363636364
57	16	15.2134831460674	0.786516853932584
58	16	13.8947368421053	2.10526315789474
59	15	15.2134831460674	-0.213483146067416
60	16	15.2134831460674	0.786516853932584
61	12	12.3636363636364	-0.363636363636363
62	16	15.2134831460674	0.786516853932584
63	16	16.625	-0.625
64	14	13.8947368421053	0.105263157894736
65	16	15.2134831460674	0.786516853932584
66	17	16.625	0.375
67	18	16.625	1.375
68	18	13.8947368421053	4.10526315789474
69	12	15.2134831460674	-3.21348314606742
70	16	15.2134831460674	0.786516853932584
71	10	12.3636363636364	-2.36363636363636
72	14	13.8947368421053	0.105263157894736
73	18	16.625	1.375
74	18	16.625	1.375
75	16	15.2134831460674	0.786516853932584
76	17	15.2134831460674	1.78651685393258
77	16	16.625	-0.625
78	16	15.2134831460674	0.786516853932584
79	13	13.8947368421053	-0.894736842105264
80	16	15.2134831460674	0.786516853932584
81	16	15.2134831460674	0.786516853932584
82	20	15.2134831460674	4.78651685393258
83	16	15.2134831460674	0.786516853932584
84	15	15.2134831460674	-0.213483146067416
85	15	15.2134831460674	-0.213483146067416
86	16	15.2134831460674	0.786516853932584
87	14	15.2134831460674	-1.21348314606742
88	16	15.2134831460674	0.786516853932584
89	16	13.8947368421053	2.10526315789474
90	15	15.2134831460674	-0.213483146067416
91	12	13.8947368421053	-1.89473684210526
92	17	16.625	0.375
93	16	15.2134831460674	0.786516853932584
94	15	15.2134831460674	-0.213483146067416
95	13	13.8947368421053	-0.894736842105264
96	16	15.2134831460674	0.786516853932584
97	16	16.625	-0.625
98	16	15.2134831460674	0.786516853932584
99	16	15.2134831460674	0.786516853932584
100	14	15.2134831460674	-1.21348314606742
101	16	16.625	-0.625
102	16	15.2134831460674	0.786516853932584
103	20	16.625	3.375
104	15	15.2134831460674	-0.213483146067416
105	16	15.2134831460674	0.786516853932584
106	13	15.2134831460674	-2.21348314606742
107	17	15.2134831460674	1.78651685393258
108	16	15.2134831460674	0.786516853932584
109	16	15.2134831460674	0.786516853932584
110	12	12.3636363636364	-0.363636363636363
111	16	13.8947368421053	2.10526315789474
112	16	16.625	-0.625
113	17	15.2134831460674	1.78651685393258
114	13	15.2134831460674	-2.21348314606742
115	12	15.2134831460674	-3.21348314606742
116	18	16.625	1.375
117	14	16.625	-2.625
118	14	12.3636363636364	1.63636363636364
119	13	15.2134831460674	-2.21348314606742
120	16	15.2134831460674	0.786516853932584
121	13	15.2134831460674	-2.21348314606742
122	16	16.625	-0.625
123	13	15.2134831460674	-2.21348314606742
124	16	16.625	-0.625
125	15	16.625	-1.625
126	16	16.625	-0.625
127	15	15.2134831460674	-0.213483146067416
128	17	15.2134831460674	1.78651685393258
129	15	15.2134831460674	-0.213483146067416
130	12	15.2134831460674	-3.21348314606742
131	16	15.2134831460674	0.786516853932584
132	10	13.8947368421053	-3.89473684210526
133	16	12.3636363636364	3.63636363636364
134	12	13.8947368421053	-1.89473684210526
135	14	15.2134831460674	-1.21348314606742
136	15	15.2134831460674	-0.213483146067416
137	13	13.8947368421053	-0.894736842105264
138	15	15.2134831460674	-0.213483146067416
139	11	13.8947368421053	-2.89473684210526
140	12	12.3636363636364	-0.363636363636363
141	8	13.8947368421053	-5.89473684210526
142	16	12.3636363636364	3.63636363636364
143	15	13.8947368421053	1.10526315789474
144	17	16.625	0.375
145	16	13.8947368421053	2.10526315789474
146	10	12.3636363636364	-2.36363636363636
147	18	16.625	1.375
148	13	15.2134831460674	-2.21348314606742
149	16	15.2134831460674	0.786516853932584
150	13	15.2134831460674	-2.21348314606742
151	10	12.3636363636364	-2.36363636363636
152	15	16.625	-1.625
153	16	15.2134831460674	0.786516853932584
154	16	12.3636363636364	3.63636363636364
155	14	12.3636363636364	1.63636363636364
156	10	12.3636363636364	-2.36363636363636
157	17	16.625	0.375
158	13	12.3636363636364	0.636363636363637
159	15	15.2134831460674	-0.213483146067416
160	16	15.2134831460674	0.786516853932584
161	12	12.3636363636364	-0.363636363636363
162	13	12.3636363636364	0.636363636363637

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 13 & 15.2134831460674 & -2.21348314606742 \tabularnewline
2 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
3 & 19 & 16.625 & 2.375 \tabularnewline
4 & 15 & 12.3636363636364 & 2.63636363636364 \tabularnewline
5 & 14 & 16.625 & -2.625 \tabularnewline
6 & 13 & 15.2134831460674 & -2.21348314606742 \tabularnewline
7 & 19 & 15.2134831460674 & 3.78651685393258 \tabularnewline
8 & 15 & 16.625 & -1.625 \tabularnewline
9 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
10 & 15 & 12.3636363636364 & 2.63636363636364 \tabularnewline
11 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
12 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
13 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
14 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
15 & 17 & 16.625 & 0.375 \tabularnewline
16 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
17 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
18 & 20 & 15.2134831460674 & 4.78651685393258 \tabularnewline
19 & 18 & 15.2134831460674 & 2.78651685393258 \tabularnewline
20 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
21 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
22 & 16 & 13.8947368421053 & 2.10526315789474 \tabularnewline
23 & 19 & 16.625 & 2.375 \tabularnewline
24 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
25 & 17 & 15.2134831460674 & 1.78651685393258 \tabularnewline
26 & 17 & 16.625 & 0.375 \tabularnewline
27 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
28 & 15 & 16.625 & -1.625 \tabularnewline
29 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
30 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
31 & 15 & 13.8947368421053 & 1.10526315789474 \tabularnewline
32 & 12 & 12.3636363636364 & -0.363636363636363 \tabularnewline
33 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
34 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
35 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
36 & 7 & 12.3636363636364 & -5.36363636363636 \tabularnewline
37 & 10 & 12.3636363636364 & -2.36363636363636 \tabularnewline
38 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
39 & 16 & 13.8947368421053 & 2.10526315789474 \tabularnewline
40 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
41 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
42 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
43 & 20 & 16.625 & 3.375 \tabularnewline
44 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
45 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
46 & 11 & 15.2134831460674 & -4.21348314606742 \tabularnewline
47 & 14 & 16.625 & -2.625 \tabularnewline
48 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
49 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
50 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
51 & 16 & 16.625 & -0.625 \tabularnewline
52 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
53 & 12 & 15.2134831460674 & -3.21348314606742 \tabularnewline
54 & 16 & 16.625 & -0.625 \tabularnewline
55 & 9 & 12.3636363636364 & -3.36363636363636 \tabularnewline
56 & 14 & 12.3636363636364 & 1.63636363636364 \tabularnewline
57 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
58 & 16 & 13.8947368421053 & 2.10526315789474 \tabularnewline
59 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
60 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
61 & 12 & 12.3636363636364 & -0.363636363636363 \tabularnewline
62 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
63 & 16 & 16.625 & -0.625 \tabularnewline
64 & 14 & 13.8947368421053 & 0.105263157894736 \tabularnewline
65 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
66 & 17 & 16.625 & 0.375 \tabularnewline
67 & 18 & 16.625 & 1.375 \tabularnewline
68 & 18 & 13.8947368421053 & 4.10526315789474 \tabularnewline
69 & 12 & 15.2134831460674 & -3.21348314606742 \tabularnewline
70 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
71 & 10 & 12.3636363636364 & -2.36363636363636 \tabularnewline
72 & 14 & 13.8947368421053 & 0.105263157894736 \tabularnewline
73 & 18 & 16.625 & 1.375 \tabularnewline
74 & 18 & 16.625 & 1.375 \tabularnewline
75 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
76 & 17 & 15.2134831460674 & 1.78651685393258 \tabularnewline
77 & 16 & 16.625 & -0.625 \tabularnewline
78 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
79 & 13 & 13.8947368421053 & -0.894736842105264 \tabularnewline
80 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
81 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
82 & 20 & 15.2134831460674 & 4.78651685393258 \tabularnewline
83 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
84 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
85 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
86 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
87 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
88 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
89 & 16 & 13.8947368421053 & 2.10526315789474 \tabularnewline
90 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
91 & 12 & 13.8947368421053 & -1.89473684210526 \tabularnewline
92 & 17 & 16.625 & 0.375 \tabularnewline
93 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
94 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
95 & 13 & 13.8947368421053 & -0.894736842105264 \tabularnewline
96 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
97 & 16 & 16.625 & -0.625 \tabularnewline
98 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
99 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
100 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
101 & 16 & 16.625 & -0.625 \tabularnewline
102 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
103 & 20 & 16.625 & 3.375 \tabularnewline
104 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
105 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
106 & 13 & 15.2134831460674 & -2.21348314606742 \tabularnewline
107 & 17 & 15.2134831460674 & 1.78651685393258 \tabularnewline
108 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
109 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
110 & 12 & 12.3636363636364 & -0.363636363636363 \tabularnewline
111 & 16 & 13.8947368421053 & 2.10526315789474 \tabularnewline
112 & 16 & 16.625 & -0.625 \tabularnewline
113 & 17 & 15.2134831460674 & 1.78651685393258 \tabularnewline
114 & 13 & 15.2134831460674 & -2.21348314606742 \tabularnewline
115 & 12 & 15.2134831460674 & -3.21348314606742 \tabularnewline
116 & 18 & 16.625 & 1.375 \tabularnewline
117 & 14 & 16.625 & -2.625 \tabularnewline
118 & 14 & 12.3636363636364 & 1.63636363636364 \tabularnewline
119 & 13 & 15.2134831460674 & -2.21348314606742 \tabularnewline
120 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
121 & 13 & 15.2134831460674 & -2.21348314606742 \tabularnewline
122 & 16 & 16.625 & -0.625 \tabularnewline
123 & 13 & 15.2134831460674 & -2.21348314606742 \tabularnewline
124 & 16 & 16.625 & -0.625 \tabularnewline
125 & 15 & 16.625 & -1.625 \tabularnewline
126 & 16 & 16.625 & -0.625 \tabularnewline
127 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
128 & 17 & 15.2134831460674 & 1.78651685393258 \tabularnewline
129 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
130 & 12 & 15.2134831460674 & -3.21348314606742 \tabularnewline
131 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
132 & 10 & 13.8947368421053 & -3.89473684210526 \tabularnewline
133 & 16 & 12.3636363636364 & 3.63636363636364 \tabularnewline
134 & 12 & 13.8947368421053 & -1.89473684210526 \tabularnewline
135 & 14 & 15.2134831460674 & -1.21348314606742 \tabularnewline
136 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
137 & 13 & 13.8947368421053 & -0.894736842105264 \tabularnewline
138 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
139 & 11 & 13.8947368421053 & -2.89473684210526 \tabularnewline
140 & 12 & 12.3636363636364 & -0.363636363636363 \tabularnewline
141 & 8 & 13.8947368421053 & -5.89473684210526 \tabularnewline
142 & 16 & 12.3636363636364 & 3.63636363636364 \tabularnewline
143 & 15 & 13.8947368421053 & 1.10526315789474 \tabularnewline
144 & 17 & 16.625 & 0.375 \tabularnewline
145 & 16 & 13.8947368421053 & 2.10526315789474 \tabularnewline
146 & 10 & 12.3636363636364 & -2.36363636363636 \tabularnewline
147 & 18 & 16.625 & 1.375 \tabularnewline
148 & 13 & 15.2134831460674 & -2.21348314606742 \tabularnewline
149 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
150 & 13 & 15.2134831460674 & -2.21348314606742 \tabularnewline
151 & 10 & 12.3636363636364 & -2.36363636363636 \tabularnewline
152 & 15 & 16.625 & -1.625 \tabularnewline
153 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
154 & 16 & 12.3636363636364 & 3.63636363636364 \tabularnewline
155 & 14 & 12.3636363636364 & 1.63636363636364 \tabularnewline
156 & 10 & 12.3636363636364 & -2.36363636363636 \tabularnewline
157 & 17 & 16.625 & 0.375 \tabularnewline
158 & 13 & 12.3636363636364 & 0.636363636363637 \tabularnewline
159 & 15 & 15.2134831460674 & -0.213483146067416 \tabularnewline
160 & 16 & 15.2134831460674 & 0.786516853932584 \tabularnewline
161 & 12 & 12.3636363636364 & -0.363636363636363 \tabularnewline
162 & 13 & 12.3636363636364 & 0.636363636363637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153661&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]13[/C][C]15.2134831460674[/C][C]-2.21348314606742[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.625[/C][C]2.375[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.3636363636364[/C][C]2.63636363636364[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.625[/C][C]-2.625[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.2134831460674[/C][C]-2.21348314606742[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.2134831460674[/C][C]3.78651685393258[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.625[/C][C]-1.625[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.3636363636364[/C][C]2.63636363636364[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]16.625[/C][C]0.375[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]15.2134831460674[/C][C]4.78651685393258[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.2134831460674[/C][C]2.78651685393258[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]13.8947368421053[/C][C]2.10526315789474[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.625[/C][C]2.375[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.2134831460674[/C][C]1.78651685393258[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.625[/C][C]0.375[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.625[/C][C]-1.625[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]13.8947368421053[/C][C]1.10526315789474[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.3636363636364[/C][C]-0.363636363636363[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.3636363636364[/C][C]-5.36363636363636[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.3636363636364[/C][C]-2.36363636363636[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]13.8947368421053[/C][C]2.10526315789474[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]16.625[/C][C]3.375[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.2134831460674[/C][C]-4.21348314606742[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.625[/C][C]-2.625[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.625[/C][C]-0.625[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]15.2134831460674[/C][C]-3.21348314606742[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]16.625[/C][C]-0.625[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]12.3636363636364[/C][C]-3.36363636363636[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.3636363636364[/C][C]1.63636363636364[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]13.8947368421053[/C][C]2.10526315789474[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]12.3636363636364[/C][C]-0.363636363636363[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.625[/C][C]-0.625[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.8947368421053[/C][C]0.105263157894736[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.625[/C][C]0.375[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.625[/C][C]1.375[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]13.8947368421053[/C][C]4.10526315789474[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.2134831460674[/C][C]-3.21348314606742[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]12.3636363636364[/C][C]-2.36363636363636[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]13.8947368421053[/C][C]0.105263157894736[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.625[/C][C]1.375[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.625[/C][C]1.375[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]15.2134831460674[/C][C]1.78651685393258[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.625[/C][C]-0.625[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]13.8947368421053[/C][C]-0.894736842105264[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.2134831460674[/C][C]4.78651685393258[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]13.8947368421053[/C][C]2.10526315789474[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.8947368421053[/C][C]-1.89473684210526[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.625[/C][C]0.375[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]13.8947368421053[/C][C]-0.894736842105264[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]16.625[/C][C]-0.625[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]16.625[/C][C]-0.625[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]16.625[/C][C]3.375[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.2134831460674[/C][C]-2.21348314606742[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.2134831460674[/C][C]1.78651685393258[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3636363636364[/C][C]-0.363636363636363[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]13.8947368421053[/C][C]2.10526315789474[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]16.625[/C][C]-0.625[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]15.2134831460674[/C][C]1.78651685393258[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]15.2134831460674[/C][C]-2.21348314606742[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]15.2134831460674[/C][C]-3.21348314606742[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.625[/C][C]1.375[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]16.625[/C][C]-2.625[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.3636363636364[/C][C]1.63636363636364[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.2134831460674[/C][C]-2.21348314606742[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]15.2134831460674[/C][C]-2.21348314606742[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]16.625[/C][C]-0.625[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.2134831460674[/C][C]-2.21348314606742[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.625[/C][C]-0.625[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.625[/C][C]-1.625[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.625[/C][C]-0.625[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.2134831460674[/C][C]1.78651685393258[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.2134831460674[/C][C]-3.21348314606742[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.8947368421053[/C][C]-3.89473684210526[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]12.3636363636364[/C][C]3.63636363636364[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8947368421053[/C][C]-1.89473684210526[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.2134831460674[/C][C]-1.21348314606742[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]13.8947368421053[/C][C]-0.894736842105264[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.8947368421053[/C][C]-2.89473684210526[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.3636363636364[/C][C]-0.363636363636363[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.8947368421053[/C][C]-5.89473684210526[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.3636363636364[/C][C]3.63636363636364[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.8947368421053[/C][C]1.10526315789474[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.625[/C][C]0.375[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]13.8947368421053[/C][C]2.10526315789474[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]12.3636363636364[/C][C]-2.36363636363636[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]16.625[/C][C]1.375[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.2134831460674[/C][C]-2.21348314606742[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]15.2134831460674[/C][C]-2.21348314606742[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.3636363636364[/C][C]-2.36363636363636[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.625[/C][C]-1.625[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.3636363636364[/C][C]3.63636363636364[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.3636363636364[/C][C]1.63636363636364[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.3636363636364[/C][C]-2.36363636363636[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.625[/C][C]0.375[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]12.3636363636364[/C][C]0.636363636363637[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]15.2134831460674[/C][C]-0.213483146067416[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.2134831460674[/C][C]0.786516853932584[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.3636363636364[/C][C]-0.363636363636363[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.3636363636364[/C][C]0.636363636363637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153661&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153661&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	13	15.2134831460674	-2.21348314606742
2	16	15.2134831460674	0.786516853932584
3	19	16.625	2.375
4	15	12.3636363636364	2.63636363636364
5	14	16.625	-2.625
6	13	15.2134831460674	-2.21348314606742
7	19	15.2134831460674	3.78651685393258
8	15	16.625	-1.625
9	14	15.2134831460674	-1.21348314606742
10	15	12.3636363636364	2.63636363636364
11	16	15.2134831460674	0.786516853932584
12	16	15.2134831460674	0.786516853932584
13	16	15.2134831460674	0.786516853932584
14	16	15.2134831460674	0.786516853932584
15	17	16.625	0.375
16	15	15.2134831460674	-0.213483146067416
17	15	15.2134831460674	-0.213483146067416
18	20	15.2134831460674	4.78651685393258
19	18	15.2134831460674	2.78651685393258
20	16	15.2134831460674	0.786516853932584
21	16	15.2134831460674	0.786516853932584
22	16	13.8947368421053	2.10526315789474
23	19	16.625	2.375
24	16	15.2134831460674	0.786516853932584
25	17	15.2134831460674	1.78651685393258
26	17	16.625	0.375
27	16	15.2134831460674	0.786516853932584
28	15	16.625	-1.625
29	16	15.2134831460674	0.786516853932584
30	14	15.2134831460674	-1.21348314606742
31	15	13.8947368421053	1.10526315789474
32	12	12.3636363636364	-0.363636363636363
33	14	15.2134831460674	-1.21348314606742
34	16	15.2134831460674	0.786516853932584
35	14	15.2134831460674	-1.21348314606742
36	7	12.3636363636364	-5.36363636363636
37	10	12.3636363636364	-2.36363636363636
38	14	15.2134831460674	-1.21348314606742
39	16	13.8947368421053	2.10526315789474
40	16	15.2134831460674	0.786516853932584
41	16	15.2134831460674	0.786516853932584
42	14	15.2134831460674	-1.21348314606742
43	20	16.625	3.375
44	14	15.2134831460674	-1.21348314606742
45	14	15.2134831460674	-1.21348314606742
46	11	15.2134831460674	-4.21348314606742
47	14	16.625	-2.625
48	15	15.2134831460674	-0.213483146067416
49	16	15.2134831460674	0.786516853932584
50	14	15.2134831460674	-1.21348314606742
51	16	16.625	-0.625
52	14	15.2134831460674	-1.21348314606742
53	12	15.2134831460674	-3.21348314606742
54	16	16.625	-0.625
55	9	12.3636363636364	-3.36363636363636
56	14	12.3636363636364	1.63636363636364
57	16	15.2134831460674	0.786516853932584
58	16	13.8947368421053	2.10526315789474
59	15	15.2134831460674	-0.213483146067416
60	16	15.2134831460674	0.786516853932584
61	12	12.3636363636364	-0.363636363636363
62	16	15.2134831460674	0.786516853932584
63	16	16.625	-0.625
64	14	13.8947368421053	0.105263157894736
65	16	15.2134831460674	0.786516853932584
66	17	16.625	0.375
67	18	16.625	1.375
68	18	13.8947368421053	4.10526315789474
69	12	15.2134831460674	-3.21348314606742
70	16	15.2134831460674	0.786516853932584
71	10	12.3636363636364	-2.36363636363636
72	14	13.8947368421053	0.105263157894736
73	18	16.625	1.375
74	18	16.625	1.375
75	16	15.2134831460674	0.786516853932584
76	17	15.2134831460674	1.78651685393258
77	16	16.625	-0.625
78	16	15.2134831460674	0.786516853932584
79	13	13.8947368421053	-0.894736842105264
80	16	15.2134831460674	0.786516853932584
81	16	15.2134831460674	0.786516853932584
82	20	15.2134831460674	4.78651685393258
83	16	15.2134831460674	0.786516853932584
84	15	15.2134831460674	-0.213483146067416
85	15	15.2134831460674	-0.213483146067416
86	16	15.2134831460674	0.786516853932584
87	14	15.2134831460674	-1.21348314606742
88	16	15.2134831460674	0.786516853932584
89	16	13.8947368421053	2.10526315789474
90	15	15.2134831460674	-0.213483146067416
91	12	13.8947368421053	-1.89473684210526
92	17	16.625	0.375
93	16	15.2134831460674	0.786516853932584
94	15	15.2134831460674	-0.213483146067416
95	13	13.8947368421053	-0.894736842105264
96	16	15.2134831460674	0.786516853932584
97	16	16.625	-0.625
98	16	15.2134831460674	0.786516853932584
99	16	15.2134831460674	0.786516853932584
100	14	15.2134831460674	-1.21348314606742
101	16	16.625	-0.625
102	16	15.2134831460674	0.786516853932584
103	20	16.625	3.375
104	15	15.2134831460674	-0.213483146067416
105	16	15.2134831460674	0.786516853932584
106	13	15.2134831460674	-2.21348314606742
107	17	15.2134831460674	1.78651685393258
108	16	15.2134831460674	0.786516853932584
109	16	15.2134831460674	0.786516853932584
110	12	12.3636363636364	-0.363636363636363
111	16	13.8947368421053	2.10526315789474
112	16	16.625	-0.625
113	17	15.2134831460674	1.78651685393258
114	13	15.2134831460674	-2.21348314606742
115	12	15.2134831460674	-3.21348314606742
116	18	16.625	1.375
117	14	16.625	-2.625
118	14	12.3636363636364	1.63636363636364
119	13	15.2134831460674	-2.21348314606742
120	16	15.2134831460674	0.786516853932584
121	13	15.2134831460674	-2.21348314606742
122	16	16.625	-0.625
123	13	15.2134831460674	-2.21348314606742
124	16	16.625	-0.625
125	15	16.625	-1.625
126	16	16.625	-0.625
127	15	15.2134831460674	-0.213483146067416
128	17	15.2134831460674	1.78651685393258
129	15	15.2134831460674	-0.213483146067416
130	12	15.2134831460674	-3.21348314606742
131	16	15.2134831460674	0.786516853932584
132	10	13.8947368421053	-3.89473684210526
133	16	12.3636363636364	3.63636363636364
134	12	13.8947368421053	-1.89473684210526
135	14	15.2134831460674	-1.21348314606742
136	15	15.2134831460674	-0.213483146067416
137	13	13.8947368421053	-0.894736842105264
138	15	15.2134831460674	-0.213483146067416
139	11	13.8947368421053	-2.89473684210526
140	12	12.3636363636364	-0.363636363636363
141	8	13.8947368421053	-5.89473684210526
142	16	12.3636363636364	3.63636363636364
143	15	13.8947368421053	1.10526315789474
144	17	16.625	0.375
145	16	13.8947368421053	2.10526315789474
146	10	12.3636363636364	-2.36363636363636
147	18	16.625	1.375
148	13	15.2134831460674	-2.21348314606742
149	16	15.2134831460674	0.786516853932584
150	13	15.2134831460674	-2.21348314606742
151	10	12.3636363636364	-2.36363636363636
152	15	16.625	-1.625
153	16	15.2134831460674	0.786516853932584
154	16	12.3636363636364	3.63636363636364
155	14	12.3636363636364	1.63636363636364
156	10	12.3636363636364	-2.36363636363636
157	17	16.625	0.375
158	13	12.3636363636364	0.636363636363637
159	15	15.2134831460674	-0.213483146067416
160	16	15.2134831460674	0.786516853932584
161	12	12.3636363636364	-0.363636363636363
162	13	12.3636363636364	0.636363636363637

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