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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

Thu, 06 Dec 2012 07:54:20 -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/2012/Dec/06/t13547985608736d63w19ntm09.htm/, Retrieved Tue, 23 Apr 2024 16:04:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197056, Retrieved Tue, 23 Apr 2024 16:04:51 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

138

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 20:21:33] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [] [2012-12-06 12:48:07] [8ab8078357d7493428921287469fd527]
-           [Recursive Partitioning (Regression Trees)] [] [2012-12-06 12:54:20] [eace0511beeaae09dbb51bfebd62c02b] [Current]

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Dataseries X:

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Histogram

Boxplots

41	38	7	53	145	56
39	32	5	86	101	56
30	35	5	66	98	54
31	33	5	67	132	89
34	37	8	76	60	40
35	29	6	78	38	25
39	31	5	53	144	92
34	36	6	80	5	18
36	35	5	74	28	63
37	38	4	76	84	44
38	31	6	79	79	33
36	34	5	54	127	84
38	35	5	67	78	88
39	38	6	54	60	55
33	37	7	87	131	60
32	33	6	58	84	66
36	32	7	75	133	154
38	38	6	88	150	53
39	38	8	64	91	119
32	32	7	57	132	41
32	33	5	66	136	61
31	31	5	68	124	58
39	38	7	54	118	75
37	39	7	56	70	33
39	32	5	86	107	40
41	32	4	80	119	92
36	35	10	76	89	100
33	37	6	69	112	112
33	33	5	78	108	73
34	33	5	67	52	40
31	28	5	80	112	45
27	32	5	54	116	60
37	31	6	71	123	62
34	37	5	84	125	75
34	30	5	74	27	31
32	33	5	71	162	77
29	31	5	63	32	34
36	33	5	71	64	46
29	31	5	76	92	99
35	33	5	69	0	17
37	32	5	74	83	66
34	33	7	75	41	30
38	32	5	54	47	76
35	33	6	52	120	146
38	28	7	69	105	67
37	35	7	68	79	56
38	39	5	65	65	107
33	34	5	75	70	58
36	38	4	74	55	34
38	32	5	75	39	61
32	38	4	72	67	119
32	30	5	67	21	42
32	33	5	63	127	66
34	38	7	62	152	89
32	32	5	63	113	44
37	32	5	76	99	66
39	34	6	74	7	24
29	34	4	67	141	259
37	36	6	73	21	17
35	34	6	70	35	64
30	28	5	53	109	41
38	34	7	77	133	68
34	35	6	77	123	168
31	35	8	52	26	43
34	31	7	54	230	132
35	37	5	80	166	105
36	35	6	66	68	71
30	27	6	73	147	112
39	40	5	63	179	94
35	37	5	69	61	82
38	36	5	67	101	70
31	38	5	54	108	57
34	39	4	81	90	53
38	41	6	69	114	103
34	27	6	84	103	121
39	30	6	80	142	62
37	37	6	70	79	52
34	31	7	69	88	52
28	31	5	77	25	32
37	27	7	54	83	62
33	36	6	79	113	45
37	38	5	30	118	46
35	37	5	71	110	63
37	33	4	73	129	75
32	34	8	72	51	88
33	31	8	77	93	46
38	39	5	75	76	53
33	34	5	69	49	37
29	32	6	54	118	90
33	33	4	70	38	63
31	36	5	73	141	78
36	32	5	54	58	25
35	41	5	77	27	45
32	28	5	82	91	46
29	30	6	80	48	41
39	36	6	80	63	144
37	35	5	69	56	82
35	31	6	78	144	91
37	34	5	81	73	71
32	36	7	76	168	63
38	36	5	76	64	53
37	35	6	73	97	62
36	37	6	85	117	63
32	28	6	66	100	32
33	39	4	79	149	39
40	32	5	68	187	62
38	35	5	76	127	117
41	39	7	71	37	34
36	35	6	54	245	92
43	42	9	46	87	93
30	34	6	82	177	54
31	33	6	74	49	144
32	41	5	88	49	14
32	33	6	38	73	61
37	34	5	76	177	109
37	32	8	86	94	38
33	40	7	54	117	73
34	40	5	70	60	75
33	35	7	69	55	50
38	36	6	90	39	61
33	37	6	54	64	55
31	27	9	76	26	77
38	39	7	89	64	75
37	38	6	76	58	72
33	31	5	73	95	50
31	33	5	79	25	32
39	32	6	90	26	53
44	39	6	74	76	42
33	36	7	81	129	71
35	33	5	72	11	10
32	33	5	71	2	35
28	32	5	66	101	65
40	37	6	77	28	25
27	30	4	65	36	66
37	38	5	74	89	41
32	29	7	82	193	86
28	22	5	54	4	16
34	35	7	63	84	42
30	35	7	54	23	19
35	34	6	64	39	19
31	35	5	69	14	45
32	34	8	54	78	65
30	34	5	84	14	35
30	35	5	86	101	95
31	23	5	77	82	49
40	31	6	89	24	37
32	27	4	76	36	64
36	36	5	60	75	38
32	31	5	75	16	34
35	32	7	73	55	32
38	39	6	85	131	65
42	37	7	79	131	52
34	38	10	71	39	62
35	39	6	72	144	65
35	34	8	69	139	83
33	31	4	78	211	95
36	32	5	54	78	29
32	37	6	69	50	18
33	36	7	81	39	33
34	32	7	84	90	247
32	35	6	84	166	139
34	36	6	69	12	29

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ yule.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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197056&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197056&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197056&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Goodness of Fit
Correlation	0.3168
R-squared	0.1004
RMSE	3.1914

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.3168 \tabularnewline
R-squared & 0.1004 \tabularnewline
RMSE & 3.1914 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197056&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.3168[/C][/ROW]
[ROW][C]R-squared[/C][C]0.1004[/C][/ROW]
[ROW][C]RMSE[/C][C]3.1914[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197056&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197056&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	0.3168
R-squared	0.1004
RMSE	3.1914

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	41	36.0701754385965	4.92982456140351
2	39	33.8380952380952	5.16190476190476
3	30	33.8380952380952	-3.83809523809524
4	31	33.8380952380952	-2.83809523809524
5	34	36.0701754385965	-2.07017543859649
6	35	33.8380952380952	1.16190476190476
7	39	33.8380952380952	5.16190476190476
8	34	36.0701754385965	-2.07017543859649
9	36	33.8380952380952	2.16190476190476
10	37	36.0701754385965	0.929824561403507
11	38	33.8380952380952	4.16190476190476
12	36	33.8380952380952	2.16190476190476
13	38	33.8380952380952	4.16190476190476
14	39	36.0701754385965	2.92982456140351
15	33	36.0701754385965	-3.07017543859649
16	32	33.8380952380952	-1.83809523809524
17	36	33.8380952380952	2.16190476190476
18	38	36.0701754385965	1.92982456140351
19	39	36.0701754385965	2.92982456140351
20	32	33.8380952380952	-1.83809523809524
21	32	33.8380952380952	-1.83809523809524
22	31	33.8380952380952	-2.83809523809524
23	39	36.0701754385965	2.92982456140351
24	37	36.0701754385965	0.929824561403507
25	39	33.8380952380952	5.16190476190476
26	41	33.8380952380952	7.16190476190476
27	36	33.8380952380952	2.16190476190476
28	33	36.0701754385965	-3.07017543859649
29	33	33.8380952380952	-0.838095238095235
30	34	33.8380952380952	0.161904761904765
31	31	33.8380952380952	-2.83809523809524
32	27	33.8380952380952	-6.83809523809524
33	37	33.8380952380952	3.16190476190476
34	34	36.0701754385965	-2.07017543859649
35	34	33.8380952380952	0.161904761904765
36	32	33.8380952380952	-1.83809523809524
37	29	33.8380952380952	-4.83809523809524
38	36	33.8380952380952	2.16190476190476
39	29	33.8380952380952	-4.83809523809524
40	35	33.8380952380952	1.16190476190476
41	37	33.8380952380952	3.16190476190476
42	34	33.8380952380952	0.161904761904765
43	38	33.8380952380952	4.16190476190476
44	35	33.8380952380952	1.16190476190476
45	38	33.8380952380952	4.16190476190476
46	37	33.8380952380952	3.16190476190476
47	38	36.0701754385965	1.92982456140351
48	33	33.8380952380952	-0.838095238095235
49	36	36.0701754385965	-0.0701754385964932
50	38	33.8380952380952	4.16190476190476
51	32	36.0701754385965	-4.07017543859649
52	32	33.8380952380952	-1.83809523809524
53	32	33.8380952380952	-1.83809523809524
54	34	36.0701754385965	-2.07017543859649
55	32	33.8380952380952	-1.83809523809524
56	37	33.8380952380952	3.16190476190476
57	39	33.8380952380952	5.16190476190476
58	29	33.8380952380952	-4.83809523809524
59	37	36.0701754385965	0.929824561403507
60	35	33.8380952380952	1.16190476190476
61	30	33.8380952380952	-3.83809523809524
62	38	33.8380952380952	4.16190476190476
63	34	33.8380952380952	0.161904761904765
64	31	33.8380952380952	-2.83809523809524
65	34	33.8380952380952	0.161904761904765
66	35	36.0701754385965	-1.07017543859649
67	36	33.8380952380952	2.16190476190476
68	30	33.8380952380952	-3.83809523809524
69	39	36.0701754385965	2.92982456140351
70	35	36.0701754385965	-1.07017543859649
71	38	36.0701754385965	1.92982456140351
72	31	36.0701754385965	-5.07017543859649
73	34	36.0701754385965	-2.07017543859649
74	38	36.0701754385965	1.92982456140351
75	34	33.8380952380952	0.161904761904765
76	39	33.8380952380952	5.16190476190476
77	37	36.0701754385965	0.929824561403507
78	34	33.8380952380952	0.161904761904765
79	28	33.8380952380952	-5.83809523809524
80	37	33.8380952380952	3.16190476190476
81	33	36.0701754385965	-3.07017543859649
82	37	36.0701754385965	0.929824561403507
83	35	36.0701754385965	-1.07017543859649
84	37	33.8380952380952	3.16190476190476
85	32	33.8380952380952	-1.83809523809524
86	33	33.8380952380952	-0.838095238095235
87	38	36.0701754385965	1.92982456140351
88	33	33.8380952380952	-0.838095238095235
89	29	33.8380952380952	-4.83809523809524
90	33	33.8380952380952	-0.838095238095235
91	31	36.0701754385965	-5.07017543859649
92	36	33.8380952380952	2.16190476190476
93	35	36.0701754385965	-1.07017543859649
94	32	33.8380952380952	-1.83809523809524
95	29	33.8380952380952	-4.83809523809524
96	39	36.0701754385965	2.92982456140351
97	37	33.8380952380952	3.16190476190476
98	35	33.8380952380952	1.16190476190476
99	37	33.8380952380952	3.16190476190476
100	32	36.0701754385965	-4.07017543859649
101	38	36.0701754385965	1.92982456140351
102	37	33.8380952380952	3.16190476190476
103	36	36.0701754385965	-0.0701754385964932
104	32	33.8380952380952	-1.83809523809524
105	33	36.0701754385965	-3.07017543859649
106	40	33.8380952380952	6.16190476190476
107	38	33.8380952380952	4.16190476190476
108	41	36.0701754385965	4.92982456140351
109	36	33.8380952380952	2.16190476190476
110	43	36.0701754385965	6.92982456140351
111	30	33.8380952380952	-3.83809523809524
112	31	33.8380952380952	-2.83809523809524
113	32	36.0701754385965	-4.07017543859649
114	32	33.8380952380952	-1.83809523809524
115	37	33.8380952380952	3.16190476190476
116	37	33.8380952380952	3.16190476190476
117	33	36.0701754385965	-3.07017543859649
118	34	36.0701754385965	-2.07017543859649
119	33	33.8380952380952	-0.838095238095235
120	38	36.0701754385965	1.92982456140351
121	33	36.0701754385965	-3.07017543859649
122	31	33.8380952380952	-2.83809523809524
123	38	36.0701754385965	1.92982456140351
124	37	36.0701754385965	0.929824561403507
125	33	33.8380952380952	-0.838095238095235
126	31	33.8380952380952	-2.83809523809524
127	39	33.8380952380952	5.16190476190476
128	44	36.0701754385965	7.92982456140351
129	33	36.0701754385965	-3.07017543859649
130	35	33.8380952380952	1.16190476190476
131	32	33.8380952380952	-1.83809523809524
132	28	33.8380952380952	-5.83809523809524
133	40	36.0701754385965	3.92982456140351
134	27	33.8380952380952	-6.83809523809524
135	37	36.0701754385965	0.929824561403507
136	32	33.8380952380952	-1.83809523809524
137	28	33.8380952380952	-5.83809523809524
138	34	33.8380952380952	0.161904761904765
139	30	33.8380952380952	-3.83809523809524
140	35	33.8380952380952	1.16190476190476
141	31	33.8380952380952	-2.83809523809524
142	32	33.8380952380952	-1.83809523809524
143	30	33.8380952380952	-3.83809523809524
144	30	33.8380952380952	-3.83809523809524
145	31	33.8380952380952	-2.83809523809524
146	40	33.8380952380952	6.16190476190476
147	32	33.8380952380952	-1.83809523809524
148	36	36.0701754385965	-0.0701754385964932
149	32	33.8380952380952	-1.83809523809524
150	35	33.8380952380952	1.16190476190476
151	38	36.0701754385965	1.92982456140351
152	42	36.0701754385965	5.92982456140351
153	34	36.0701754385965	-2.07017543859649
154	35	36.0701754385965	-1.07017543859649
155	35	33.8380952380952	1.16190476190476
156	33	33.8380952380952	-0.838095238095235
157	36	33.8380952380952	2.16190476190476
158	32	36.0701754385965	-4.07017543859649
159	33	36.0701754385965	-3.07017543859649
160	34	33.8380952380952	0.161904761904765
161	32	33.8380952380952	-1.83809523809524
162	34	36.0701754385965	-2.07017543859649

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 41 & 36.0701754385965 & 4.92982456140351 \tabularnewline
2 & 39 & 33.8380952380952 & 5.16190476190476 \tabularnewline
3 & 30 & 33.8380952380952 & -3.83809523809524 \tabularnewline
4 & 31 & 33.8380952380952 & -2.83809523809524 \tabularnewline
5 & 34 & 36.0701754385965 & -2.07017543859649 \tabularnewline
6 & 35 & 33.8380952380952 & 1.16190476190476 \tabularnewline
7 & 39 & 33.8380952380952 & 5.16190476190476 \tabularnewline
8 & 34 & 36.0701754385965 & -2.07017543859649 \tabularnewline
9 & 36 & 33.8380952380952 & 2.16190476190476 \tabularnewline
10 & 37 & 36.0701754385965 & 0.929824561403507 \tabularnewline
11 & 38 & 33.8380952380952 & 4.16190476190476 \tabularnewline
12 & 36 & 33.8380952380952 & 2.16190476190476 \tabularnewline
13 & 38 & 33.8380952380952 & 4.16190476190476 \tabularnewline
14 & 39 & 36.0701754385965 & 2.92982456140351 \tabularnewline
15 & 33 & 36.0701754385965 & -3.07017543859649 \tabularnewline
16 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
17 & 36 & 33.8380952380952 & 2.16190476190476 \tabularnewline
18 & 38 & 36.0701754385965 & 1.92982456140351 \tabularnewline
19 & 39 & 36.0701754385965 & 2.92982456140351 \tabularnewline
20 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
21 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
22 & 31 & 33.8380952380952 & -2.83809523809524 \tabularnewline
23 & 39 & 36.0701754385965 & 2.92982456140351 \tabularnewline
24 & 37 & 36.0701754385965 & 0.929824561403507 \tabularnewline
25 & 39 & 33.8380952380952 & 5.16190476190476 \tabularnewline
26 & 41 & 33.8380952380952 & 7.16190476190476 \tabularnewline
27 & 36 & 33.8380952380952 & 2.16190476190476 \tabularnewline
28 & 33 & 36.0701754385965 & -3.07017543859649 \tabularnewline
29 & 33 & 33.8380952380952 & -0.838095238095235 \tabularnewline
30 & 34 & 33.8380952380952 & 0.161904761904765 \tabularnewline
31 & 31 & 33.8380952380952 & -2.83809523809524 \tabularnewline
32 & 27 & 33.8380952380952 & -6.83809523809524 \tabularnewline
33 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
34 & 34 & 36.0701754385965 & -2.07017543859649 \tabularnewline
35 & 34 & 33.8380952380952 & 0.161904761904765 \tabularnewline
36 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
37 & 29 & 33.8380952380952 & -4.83809523809524 \tabularnewline
38 & 36 & 33.8380952380952 & 2.16190476190476 \tabularnewline
39 & 29 & 33.8380952380952 & -4.83809523809524 \tabularnewline
40 & 35 & 33.8380952380952 & 1.16190476190476 \tabularnewline
41 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
42 & 34 & 33.8380952380952 & 0.161904761904765 \tabularnewline
43 & 38 & 33.8380952380952 & 4.16190476190476 \tabularnewline
44 & 35 & 33.8380952380952 & 1.16190476190476 \tabularnewline
45 & 38 & 33.8380952380952 & 4.16190476190476 \tabularnewline
46 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
47 & 38 & 36.0701754385965 & 1.92982456140351 \tabularnewline
48 & 33 & 33.8380952380952 & -0.838095238095235 \tabularnewline
49 & 36 & 36.0701754385965 & -0.0701754385964932 \tabularnewline
50 & 38 & 33.8380952380952 & 4.16190476190476 \tabularnewline
51 & 32 & 36.0701754385965 & -4.07017543859649 \tabularnewline
52 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
53 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
54 & 34 & 36.0701754385965 & -2.07017543859649 \tabularnewline
55 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
56 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
57 & 39 & 33.8380952380952 & 5.16190476190476 \tabularnewline
58 & 29 & 33.8380952380952 & -4.83809523809524 \tabularnewline
59 & 37 & 36.0701754385965 & 0.929824561403507 \tabularnewline
60 & 35 & 33.8380952380952 & 1.16190476190476 \tabularnewline
61 & 30 & 33.8380952380952 & -3.83809523809524 \tabularnewline
62 & 38 & 33.8380952380952 & 4.16190476190476 \tabularnewline
63 & 34 & 33.8380952380952 & 0.161904761904765 \tabularnewline
64 & 31 & 33.8380952380952 & -2.83809523809524 \tabularnewline
65 & 34 & 33.8380952380952 & 0.161904761904765 \tabularnewline
66 & 35 & 36.0701754385965 & -1.07017543859649 \tabularnewline
67 & 36 & 33.8380952380952 & 2.16190476190476 \tabularnewline
68 & 30 & 33.8380952380952 & -3.83809523809524 \tabularnewline
69 & 39 & 36.0701754385965 & 2.92982456140351 \tabularnewline
70 & 35 & 36.0701754385965 & -1.07017543859649 \tabularnewline
71 & 38 & 36.0701754385965 & 1.92982456140351 \tabularnewline
72 & 31 & 36.0701754385965 & -5.07017543859649 \tabularnewline
73 & 34 & 36.0701754385965 & -2.07017543859649 \tabularnewline
74 & 38 & 36.0701754385965 & 1.92982456140351 \tabularnewline
75 & 34 & 33.8380952380952 & 0.161904761904765 \tabularnewline
76 & 39 & 33.8380952380952 & 5.16190476190476 \tabularnewline
77 & 37 & 36.0701754385965 & 0.929824561403507 \tabularnewline
78 & 34 & 33.8380952380952 & 0.161904761904765 \tabularnewline
79 & 28 & 33.8380952380952 & -5.83809523809524 \tabularnewline
80 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
81 & 33 & 36.0701754385965 & -3.07017543859649 \tabularnewline
82 & 37 & 36.0701754385965 & 0.929824561403507 \tabularnewline
83 & 35 & 36.0701754385965 & -1.07017543859649 \tabularnewline
84 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
85 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
86 & 33 & 33.8380952380952 & -0.838095238095235 \tabularnewline
87 & 38 & 36.0701754385965 & 1.92982456140351 \tabularnewline
88 & 33 & 33.8380952380952 & -0.838095238095235 \tabularnewline
89 & 29 & 33.8380952380952 & -4.83809523809524 \tabularnewline
90 & 33 & 33.8380952380952 & -0.838095238095235 \tabularnewline
91 & 31 & 36.0701754385965 & -5.07017543859649 \tabularnewline
92 & 36 & 33.8380952380952 & 2.16190476190476 \tabularnewline
93 & 35 & 36.0701754385965 & -1.07017543859649 \tabularnewline
94 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
95 & 29 & 33.8380952380952 & -4.83809523809524 \tabularnewline
96 & 39 & 36.0701754385965 & 2.92982456140351 \tabularnewline
97 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
98 & 35 & 33.8380952380952 & 1.16190476190476 \tabularnewline
99 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
100 & 32 & 36.0701754385965 & -4.07017543859649 \tabularnewline
101 & 38 & 36.0701754385965 & 1.92982456140351 \tabularnewline
102 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
103 & 36 & 36.0701754385965 & -0.0701754385964932 \tabularnewline
104 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
105 & 33 & 36.0701754385965 & -3.07017543859649 \tabularnewline
106 & 40 & 33.8380952380952 & 6.16190476190476 \tabularnewline
107 & 38 & 33.8380952380952 & 4.16190476190476 \tabularnewline
108 & 41 & 36.0701754385965 & 4.92982456140351 \tabularnewline
109 & 36 & 33.8380952380952 & 2.16190476190476 \tabularnewline
110 & 43 & 36.0701754385965 & 6.92982456140351 \tabularnewline
111 & 30 & 33.8380952380952 & -3.83809523809524 \tabularnewline
112 & 31 & 33.8380952380952 & -2.83809523809524 \tabularnewline
113 & 32 & 36.0701754385965 & -4.07017543859649 \tabularnewline
114 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
115 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
116 & 37 & 33.8380952380952 & 3.16190476190476 \tabularnewline
117 & 33 & 36.0701754385965 & -3.07017543859649 \tabularnewline
118 & 34 & 36.0701754385965 & -2.07017543859649 \tabularnewline
119 & 33 & 33.8380952380952 & -0.838095238095235 \tabularnewline
120 & 38 & 36.0701754385965 & 1.92982456140351 \tabularnewline
121 & 33 & 36.0701754385965 & -3.07017543859649 \tabularnewline
122 & 31 & 33.8380952380952 & -2.83809523809524 \tabularnewline
123 & 38 & 36.0701754385965 & 1.92982456140351 \tabularnewline
124 & 37 & 36.0701754385965 & 0.929824561403507 \tabularnewline
125 & 33 & 33.8380952380952 & -0.838095238095235 \tabularnewline
126 & 31 & 33.8380952380952 & -2.83809523809524 \tabularnewline
127 & 39 & 33.8380952380952 & 5.16190476190476 \tabularnewline
128 & 44 & 36.0701754385965 & 7.92982456140351 \tabularnewline
129 & 33 & 36.0701754385965 & -3.07017543859649 \tabularnewline
130 & 35 & 33.8380952380952 & 1.16190476190476 \tabularnewline
131 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
132 & 28 & 33.8380952380952 & -5.83809523809524 \tabularnewline
133 & 40 & 36.0701754385965 & 3.92982456140351 \tabularnewline
134 & 27 & 33.8380952380952 & -6.83809523809524 \tabularnewline
135 & 37 & 36.0701754385965 & 0.929824561403507 \tabularnewline
136 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
137 & 28 & 33.8380952380952 & -5.83809523809524 \tabularnewline
138 & 34 & 33.8380952380952 & 0.161904761904765 \tabularnewline
139 & 30 & 33.8380952380952 & -3.83809523809524 \tabularnewline
140 & 35 & 33.8380952380952 & 1.16190476190476 \tabularnewline
141 & 31 & 33.8380952380952 & -2.83809523809524 \tabularnewline
142 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
143 & 30 & 33.8380952380952 & -3.83809523809524 \tabularnewline
144 & 30 & 33.8380952380952 & -3.83809523809524 \tabularnewline
145 & 31 & 33.8380952380952 & -2.83809523809524 \tabularnewline
146 & 40 & 33.8380952380952 & 6.16190476190476 \tabularnewline
147 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
148 & 36 & 36.0701754385965 & -0.0701754385964932 \tabularnewline
149 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
150 & 35 & 33.8380952380952 & 1.16190476190476 \tabularnewline
151 & 38 & 36.0701754385965 & 1.92982456140351 \tabularnewline
152 & 42 & 36.0701754385965 & 5.92982456140351 \tabularnewline
153 & 34 & 36.0701754385965 & -2.07017543859649 \tabularnewline
154 & 35 & 36.0701754385965 & -1.07017543859649 \tabularnewline
155 & 35 & 33.8380952380952 & 1.16190476190476 \tabularnewline
156 & 33 & 33.8380952380952 & -0.838095238095235 \tabularnewline
157 & 36 & 33.8380952380952 & 2.16190476190476 \tabularnewline
158 & 32 & 36.0701754385965 & -4.07017543859649 \tabularnewline
159 & 33 & 36.0701754385965 & -3.07017543859649 \tabularnewline
160 & 34 & 33.8380952380952 & 0.161904761904765 \tabularnewline
161 & 32 & 33.8380952380952 & -1.83809523809524 \tabularnewline
162 & 34 & 36.0701754385965 & -2.07017543859649 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197056&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]41[/C][C]36.0701754385965[/C][C]4.92982456140351[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]33.8380952380952[/C][C]5.16190476190476[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]33.8380952380952[/C][C]-3.83809523809524[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]33.8380952380952[/C][C]-2.83809523809524[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]36.0701754385965[/C][C]-2.07017543859649[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]33.8380952380952[/C][C]1.16190476190476[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]33.8380952380952[/C][C]5.16190476190476[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]36.0701754385965[/C][C]-2.07017543859649[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]33.8380952380952[/C][C]2.16190476190476[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.0701754385965[/C][C]0.929824561403507[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]33.8380952380952[/C][C]4.16190476190476[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]33.8380952380952[/C][C]2.16190476190476[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]33.8380952380952[/C][C]4.16190476190476[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]36.0701754385965[/C][C]2.92982456140351[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.0701754385965[/C][C]-3.07017543859649[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]33.8380952380952[/C][C]2.16190476190476[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]36.0701754385965[/C][C]1.92982456140351[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.0701754385965[/C][C]2.92982456140351[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.8380952380952[/C][C]-2.83809523809524[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.0701754385965[/C][C]2.92982456140351[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.0701754385965[/C][C]0.929824561403507[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]33.8380952380952[/C][C]5.16190476190476[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]33.8380952380952[/C][C]7.16190476190476[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]33.8380952380952[/C][C]2.16190476190476[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]36.0701754385965[/C][C]-3.07017543859649[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]33.8380952380952[/C][C]-0.838095238095235[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.8380952380952[/C][C]0.161904761904765[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]33.8380952380952[/C][C]-2.83809523809524[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.8380952380952[/C][C]-6.83809523809524[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]36.0701754385965[/C][C]-2.07017543859649[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]33.8380952380952[/C][C]0.161904761904765[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]33.8380952380952[/C][C]-4.83809523809524[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]33.8380952380952[/C][C]2.16190476190476[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]33.8380952380952[/C][C]-4.83809523809524[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]33.8380952380952[/C][C]1.16190476190476[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]33.8380952380952[/C][C]0.161904761904765[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]33.8380952380952[/C][C]4.16190476190476[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]33.8380952380952[/C][C]1.16190476190476[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]33.8380952380952[/C][C]4.16190476190476[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]36.0701754385965[/C][C]1.92982456140351[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]33.8380952380952[/C][C]-0.838095238095235[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.0701754385965[/C][C]-0.0701754385964932[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.8380952380952[/C][C]4.16190476190476[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.0701754385965[/C][C]-4.07017543859649[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]36.0701754385965[/C][C]-2.07017543859649[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]33.8380952380952[/C][C]5.16190476190476[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]33.8380952380952[/C][C]-4.83809523809524[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]36.0701754385965[/C][C]0.929824561403507[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]33.8380952380952[/C][C]1.16190476190476[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]33.8380952380952[/C][C]-3.83809523809524[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]33.8380952380952[/C][C]4.16190476190476[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]33.8380952380952[/C][C]0.161904761904765[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]33.8380952380952[/C][C]-2.83809523809524[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.8380952380952[/C][C]0.161904761904765[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]36.0701754385965[/C][C]-1.07017543859649[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]33.8380952380952[/C][C]2.16190476190476[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]33.8380952380952[/C][C]-3.83809523809524[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]36.0701754385965[/C][C]2.92982456140351[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]36.0701754385965[/C][C]-1.07017543859649[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]36.0701754385965[/C][C]1.92982456140351[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]36.0701754385965[/C][C]-5.07017543859649[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.0701754385965[/C][C]-2.07017543859649[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]36.0701754385965[/C][C]1.92982456140351[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]33.8380952380952[/C][C]0.161904761904765[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]33.8380952380952[/C][C]5.16190476190476[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]36.0701754385965[/C][C]0.929824561403507[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.8380952380952[/C][C]0.161904761904765[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]33.8380952380952[/C][C]-5.83809523809524[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]36.0701754385965[/C][C]-3.07017543859649[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]36.0701754385965[/C][C]0.929824561403507[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]36.0701754385965[/C][C]-1.07017543859649[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]33.8380952380952[/C][C]-0.838095238095235[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.0701754385965[/C][C]1.92982456140351[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]33.8380952380952[/C][C]-0.838095238095235[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.8380952380952[/C][C]-4.83809523809524[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.8380952380952[/C][C]-0.838095238095235[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]36.0701754385965[/C][C]-5.07017543859649[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.8380952380952[/C][C]2.16190476190476[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]36.0701754385965[/C][C]-1.07017543859649[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]33.8380952380952[/C][C]-4.83809523809524[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]36.0701754385965[/C][C]2.92982456140351[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]33.8380952380952[/C][C]1.16190476190476[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]36.0701754385965[/C][C]-4.07017543859649[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]36.0701754385965[/C][C]1.92982456140351[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]36.0701754385965[/C][C]-0.0701754385964932[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.0701754385965[/C][C]-3.07017543859649[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]33.8380952380952[/C][C]6.16190476190476[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]33.8380952380952[/C][C]4.16190476190476[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.0701754385965[/C][C]4.92982456140351[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]33.8380952380952[/C][C]2.16190476190476[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]36.0701754385965[/C][C]6.92982456140351[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]33.8380952380952[/C][C]-3.83809523809524[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]33.8380952380952[/C][C]-2.83809523809524[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]36.0701754385965[/C][C]-4.07017543859649[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]33.8380952380952[/C][C]3.16190476190476[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]36.0701754385965[/C][C]-3.07017543859649[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.0701754385965[/C][C]-2.07017543859649[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]33.8380952380952[/C][C]-0.838095238095235[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]36.0701754385965[/C][C]1.92982456140351[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]36.0701754385965[/C][C]-3.07017543859649[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]33.8380952380952[/C][C]-2.83809523809524[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]36.0701754385965[/C][C]1.92982456140351[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.0701754385965[/C][C]0.929824561403507[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.8380952380952[/C][C]-0.838095238095235[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]33.8380952380952[/C][C]-2.83809523809524[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]33.8380952380952[/C][C]5.16190476190476[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]36.0701754385965[/C][C]7.92982456140351[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]36.0701754385965[/C][C]-3.07017543859649[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.8380952380952[/C][C]1.16190476190476[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]33.8380952380952[/C][C]-5.83809523809524[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.0701754385965[/C][C]3.92982456140351[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]33.8380952380952[/C][C]-6.83809523809524[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]36.0701754385965[/C][C]0.929824561403507[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]33.8380952380952[/C][C]-5.83809523809524[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]33.8380952380952[/C][C]0.161904761904765[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.8380952380952[/C][C]-3.83809523809524[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]33.8380952380952[/C][C]1.16190476190476[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]33.8380952380952[/C][C]-2.83809523809524[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]33.8380952380952[/C][C]-3.83809523809524[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]33.8380952380952[/C][C]-3.83809523809524[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]33.8380952380952[/C][C]-2.83809523809524[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]33.8380952380952[/C][C]6.16190476190476[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]36.0701754385965[/C][C]-0.0701754385964932[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.8380952380952[/C][C]1.16190476190476[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]36.0701754385965[/C][C]1.92982456140351[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]36.0701754385965[/C][C]5.92982456140351[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.0701754385965[/C][C]-2.07017543859649[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]36.0701754385965[/C][C]-1.07017543859649[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]33.8380952380952[/C][C]1.16190476190476[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]33.8380952380952[/C][C]-0.838095238095235[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.8380952380952[/C][C]2.16190476190476[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]36.0701754385965[/C][C]-4.07017543859649[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]36.0701754385965[/C][C]-3.07017543859649[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]33.8380952380952[/C][C]0.161904761904765[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]33.8380952380952[/C][C]-1.83809523809524[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]36.0701754385965[/C][C]-2.07017543859649[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197056&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197056&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	41	36.0701754385965	4.92982456140351
2	39	33.8380952380952	5.16190476190476
3	30	33.8380952380952	-3.83809523809524
4	31	33.8380952380952	-2.83809523809524
5	34	36.0701754385965	-2.07017543859649
6	35	33.8380952380952	1.16190476190476
7	39	33.8380952380952	5.16190476190476
8	34	36.0701754385965	-2.07017543859649
9	36	33.8380952380952	2.16190476190476
10	37	36.0701754385965	0.929824561403507
11	38	33.8380952380952	4.16190476190476
12	36	33.8380952380952	2.16190476190476
13	38	33.8380952380952	4.16190476190476
14	39	36.0701754385965	2.92982456140351
15	33	36.0701754385965	-3.07017543859649
16	32	33.8380952380952	-1.83809523809524
17	36	33.8380952380952	2.16190476190476
18	38	36.0701754385965	1.92982456140351
19	39	36.0701754385965	2.92982456140351
20	32	33.8380952380952	-1.83809523809524
21	32	33.8380952380952	-1.83809523809524
22	31	33.8380952380952	-2.83809523809524
23	39	36.0701754385965	2.92982456140351
24	37	36.0701754385965	0.929824561403507
25	39	33.8380952380952	5.16190476190476
26	41	33.8380952380952	7.16190476190476
27	36	33.8380952380952	2.16190476190476
28	33	36.0701754385965	-3.07017543859649
29	33	33.8380952380952	-0.838095238095235
30	34	33.8380952380952	0.161904761904765
31	31	33.8380952380952	-2.83809523809524
32	27	33.8380952380952	-6.83809523809524
33	37	33.8380952380952	3.16190476190476
34	34	36.0701754385965	-2.07017543859649
35	34	33.8380952380952	0.161904761904765
36	32	33.8380952380952	-1.83809523809524
37	29	33.8380952380952	-4.83809523809524
38	36	33.8380952380952	2.16190476190476
39	29	33.8380952380952	-4.83809523809524
40	35	33.8380952380952	1.16190476190476
41	37	33.8380952380952	3.16190476190476
42	34	33.8380952380952	0.161904761904765
43	38	33.8380952380952	4.16190476190476
44	35	33.8380952380952	1.16190476190476
45	38	33.8380952380952	4.16190476190476
46	37	33.8380952380952	3.16190476190476
47	38	36.0701754385965	1.92982456140351
48	33	33.8380952380952	-0.838095238095235
49	36	36.0701754385965	-0.0701754385964932
50	38	33.8380952380952	4.16190476190476
51	32	36.0701754385965	-4.07017543859649
52	32	33.8380952380952	-1.83809523809524
53	32	33.8380952380952	-1.83809523809524
54	34	36.0701754385965	-2.07017543859649
55	32	33.8380952380952	-1.83809523809524
56	37	33.8380952380952	3.16190476190476
57	39	33.8380952380952	5.16190476190476
58	29	33.8380952380952	-4.83809523809524
59	37	36.0701754385965	0.929824561403507
60	35	33.8380952380952	1.16190476190476
61	30	33.8380952380952	-3.83809523809524
62	38	33.8380952380952	4.16190476190476
63	34	33.8380952380952	0.161904761904765
64	31	33.8380952380952	-2.83809523809524
65	34	33.8380952380952	0.161904761904765
66	35	36.0701754385965	-1.07017543859649
67	36	33.8380952380952	2.16190476190476
68	30	33.8380952380952	-3.83809523809524
69	39	36.0701754385965	2.92982456140351
70	35	36.0701754385965	-1.07017543859649
71	38	36.0701754385965	1.92982456140351
72	31	36.0701754385965	-5.07017543859649
73	34	36.0701754385965	-2.07017543859649
74	38	36.0701754385965	1.92982456140351
75	34	33.8380952380952	0.161904761904765
76	39	33.8380952380952	5.16190476190476
77	37	36.0701754385965	0.929824561403507
78	34	33.8380952380952	0.161904761904765
79	28	33.8380952380952	-5.83809523809524
80	37	33.8380952380952	3.16190476190476
81	33	36.0701754385965	-3.07017543859649
82	37	36.0701754385965	0.929824561403507
83	35	36.0701754385965	-1.07017543859649
84	37	33.8380952380952	3.16190476190476
85	32	33.8380952380952	-1.83809523809524
86	33	33.8380952380952	-0.838095238095235
87	38	36.0701754385965	1.92982456140351
88	33	33.8380952380952	-0.838095238095235
89	29	33.8380952380952	-4.83809523809524
90	33	33.8380952380952	-0.838095238095235
91	31	36.0701754385965	-5.07017543859649
92	36	33.8380952380952	2.16190476190476
93	35	36.0701754385965	-1.07017543859649
94	32	33.8380952380952	-1.83809523809524
95	29	33.8380952380952	-4.83809523809524
96	39	36.0701754385965	2.92982456140351
97	37	33.8380952380952	3.16190476190476
98	35	33.8380952380952	1.16190476190476
99	37	33.8380952380952	3.16190476190476
100	32	36.0701754385965	-4.07017543859649
101	38	36.0701754385965	1.92982456140351
102	37	33.8380952380952	3.16190476190476
103	36	36.0701754385965	-0.0701754385964932
104	32	33.8380952380952	-1.83809523809524
105	33	36.0701754385965	-3.07017543859649
106	40	33.8380952380952	6.16190476190476
107	38	33.8380952380952	4.16190476190476
108	41	36.0701754385965	4.92982456140351
109	36	33.8380952380952	2.16190476190476
110	43	36.0701754385965	6.92982456140351
111	30	33.8380952380952	-3.83809523809524
112	31	33.8380952380952	-2.83809523809524
113	32	36.0701754385965	-4.07017543859649
114	32	33.8380952380952	-1.83809523809524
115	37	33.8380952380952	3.16190476190476
116	37	33.8380952380952	3.16190476190476
117	33	36.0701754385965	-3.07017543859649
118	34	36.0701754385965	-2.07017543859649
119	33	33.8380952380952	-0.838095238095235
120	38	36.0701754385965	1.92982456140351
121	33	36.0701754385965	-3.07017543859649
122	31	33.8380952380952	-2.83809523809524
123	38	36.0701754385965	1.92982456140351
124	37	36.0701754385965	0.929824561403507
125	33	33.8380952380952	-0.838095238095235
126	31	33.8380952380952	-2.83809523809524
127	39	33.8380952380952	5.16190476190476
128	44	36.0701754385965	7.92982456140351
129	33	36.0701754385965	-3.07017543859649
130	35	33.8380952380952	1.16190476190476
131	32	33.8380952380952	-1.83809523809524
132	28	33.8380952380952	-5.83809523809524
133	40	36.0701754385965	3.92982456140351
134	27	33.8380952380952	-6.83809523809524
135	37	36.0701754385965	0.929824561403507
136	32	33.8380952380952	-1.83809523809524
137	28	33.8380952380952	-5.83809523809524
138	34	33.8380952380952	0.161904761904765
139	30	33.8380952380952	-3.83809523809524
140	35	33.8380952380952	1.16190476190476
141	31	33.8380952380952	-2.83809523809524
142	32	33.8380952380952	-1.83809523809524
143	30	33.8380952380952	-3.83809523809524
144	30	33.8380952380952	-3.83809523809524
145	31	33.8380952380952	-2.83809523809524
146	40	33.8380952380952	6.16190476190476
147	32	33.8380952380952	-1.83809523809524
148	36	36.0701754385965	-0.0701754385964932
149	32	33.8380952380952	-1.83809523809524
150	35	33.8380952380952	1.16190476190476
151	38	36.0701754385965	1.92982456140351
152	42	36.0701754385965	5.92982456140351
153	34	36.0701754385965	-2.07017543859649
154	35	36.0701754385965	-1.07017543859649
155	35	33.8380952380952	1.16190476190476
156	33	33.8380952380952	-0.838095238095235
157	36	33.8380952380952	2.16190476190476
158	32	36.0701754385965	-4.07017543859649
159	33	36.0701754385965	-3.07017543859649
160	34	33.8380952380952	0.161904761904765
161	32	33.8380952380952	-1.83809523809524
162	34	36.0701754385965	-2.07017543859649

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 = 6 ; par2 = none ; par3 = 5 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 5 ; 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