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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

Tue, 16 Dec 2014 17:22:32 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/16/t14187506803uqqdlcwnufagif.htm/, Retrieved Thu, 16 May 2024 15:34:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269861, Retrieved Thu, 16 May 2024 15:34:08 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Regression Trees Totale score geen categorisatie

Estimated Impact

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

-       [Recursive Partitioning (Regression Trees)] [Paper data] [2014-12-16 17:22:32] [99d5c1073827aabbadf7ab1e7da1d584] [Current]

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Dataseries X:

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Histogram

Boxplots

48	41	23	12	34	4.35
50	146	16	45	61	12.7
150	182	33	37	70	18.1
154	192	32	37	69	17.85
109	263	37	108	145	16.6
68	35	14	10	23	12.6
194	439	52	68	120	17.1
158	214	75	72	147	19.1
159	341	72	143	215	16.1
67	58	15	9	24	13.35
147	292	29	55	84	18.4
39	85	13	17	30	14.7
100	200	40	37	77	10.6
111	158	19	27	46	12.6
138	199	24	37	61	16.2
101	297	121	58	178	13.6
131	227	93	66	160	18.9
101	108	36	21	57	14.1
114	86	23	19	42	14.5
165	302	85	78	163	16.15
114	148	41	35	75	14.75
111	178	46	48	94	14.8
75	120	18	27	45	12.45
82	207	35	43	78	12.65
121	157	17	30	47	17.35
32	128	4	25	29	8.6
150	296	28	69	97	18.4
117	323	44	72	116	16.1
71	79	10	23	32	11.6
165	70	38	13	50	17.75
154	146	57	61	118	15.25
126	246	23	43	66	17.65
138	145	26	22	48	15.6
149	196	36	51	86	16.35
145	199	22	67	89	17.65
120	127	40	36	76	13.6
138	91	18	21	39	11.7
109	153	31	44	75	14.35
132	299	11	45	57	14.75
172	228	38	34	72	18.25
169	190	24	36	60	9.9
114	180	37	72	109	16
156	212	37	39	76	18.25
172	269	22	43	65	16.85
68	130	15	25	40	14.6
89	179	2	56	58	13.85
167	243	43	80	123	18.95
113	190	31	40	71	15.6
115	299	29	73	102	14.85
78	121	45	34	80	11.75
118	137	25	72	97	18.45
87	305	4	42	46	15.9
173	157	31	61	93	17.1
2	96	-4	23	19	16.1
162	183	66	74	140	19.9
49	52	61	16	78	10.95
122	238	32	66	98	18.45
96	40	31	9	40	15.1
100	226	39	41	80	15
82	190	19	57	76	11.35
100	214	31	48	79	15.95
115	145	36	51	87	18.1
141	119	42	53	95	14.6
165	222	21	29	49	15.4
165	222	21	29	49	15.4
110	159	25	55	80	17.6
118	165	32	54	86	13.35
158	249	26	43	69	19.1
146	125	28	51	79	15.35
49	122	32	20	52	7.6
90	186	41	79	120	13.4
121	148	29	39	69	13.9
155	274	33	61	94	19.1
104	172	17	55	72	15.25
147	84	13	30	43	12.9
110	168	32	55	87	16.1
108	102	30	22	52	17.35
113	106	34	37	71	13.15
115	2	59	2	61	12.15
61	139	13	38	51	12.6
60	95	23	27	50	10.35
109	130	10	56	67	15.4
68	72	5	25	30	9.6
111	141	31	39	70	18.2
77	113	19	33	52	13.6
73	206	32	43	75	14.85
151	268	30	57	87	14.75
89	175	25	43	69	14.1
78	77	48	23	72	14.9
110	125	35	44	79	16.25
220	255	67	54	121	19.25
65	111	15	28	43	13.6
141	132	22	36	58	13.6
117	211	18	39	57	15.65
122	92	33	16	50	12.75
63	76	46	23	69	14.6
44	171	24	40	64	9.85
52	83	14	24	38	12.65
62	119	23	29	53	11.9
131	266	12	78	90	19.2
101	186	38	57	96	16.6
42	50	12	37	49	11.2
152	117	28	27	56	15.25
107	219	41	61	102	11.9
77	246	12	27	40	13.2
154	279	31	69	100	16.35
103	148	33	34	67	12.4
96	137	34	44	78	15.85
154	130	41	21	62	14.35
175	181	21	34	55	18.15
57	98	20	39	59	11.15
112	226	44	51	96	15.65
143	234	52	34	86	17.75
49	138	7	31	38	7.65
110	85	29	13	43	12.35
131	66	11	12	23	15.6
167	236	26	51	77	19.3
56	106	24	24	48	15.2
137	135	7	19	26	17.1
86	122	60	30	91	15.6
121	218	13	81	94	18.4
149	199	20	42	62	19.05
168	112	52	22	74	18.55
140	278	28	85	114	19.1
88	94	25	27	52	13.1
168	113	39	25	64	12.85
94	84	9	22	31	9.5
51	86	19	19	38	4.5
48	62	13	14	27	11.85
145	222	60	45	105	13.6
66	167	19	45	64	11.7
85	82	34	28	62	12.4
109	207	14	51	65	13.35
63	184	17	41	58	11.4
102	83	45	31	76	14.9
162	183	66	74	140	19.9
128	85	24	24	48	17.75
86	89	48	19	68	11.2
114	225	29	51	80	14.6
164	237	-2	73	71	17.6
119	102	51	24	76	14.05
126	221	2	61	63	16.1
132	128	24	23	46	13.35
142	91	40	14	53	11.85
83	198	20	54	74	11.95
94	204	19	51	70	14.75
81	158	16	62	78	15.15
166	138	20	36	56	13.2
110	226	40	59	100	16.85
64	44	27	24	51	7.85
93	196	25	26	52	7.7
104	83	49	54	102	12.6
105	79	39	39	78	7.85
49	52	61	16	78	10.95
88	105	19	36	55	12.35
95	116	67	31	98	9.95
102	83	45	31	76	14.9
99	196	30	42	73	16.65
63	153	8	39	47	13.4
76	157	19	25	45	13.95
109	75	52	31	83	15.7
117	106	22	38	60	16.85
57	58	17	31	48	10.95
120	75	33	17	50	15.35
73	74	34	22	56	12.2
91	185	22	55	77	15.1
108	265	30	62	91	17.75
105	131	25	51	76	15.2
117	139	38	30	68	14.6
119	196	26	49	74	16.65
31	78	13	16	29	8.1

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	'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269861&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269861&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269861&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	'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Goodness of Fit
Correlation	0.735
R-squared	0.5402
RMSE	2.0476

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.735 \tabularnewline
R-squared & 0.5402 \tabularnewline
RMSE & 2.0476 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269861&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.735[/C][/ROW]
[ROW][C]R-squared[/C][C]0.5402[/C][/ROW]
[ROW][C]RMSE[/C][C]2.0476[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269861&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269861&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	0.735
R-squared	0.5402
RMSE	2.0476

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	4.35	9.93571428571428	-5.58571428571428
2	12.7	9.93571428571428	2.76428571428572
3	18.1	17.84375	0.256249999999998
4	17.85	17.84375	0.00624999999999787
5	16.6	17.6208333333333	-1.02083333333333
6	12.6	13.0237704918033	-0.423770491803287
7	17.1	17.84375	-0.743750000000002
8	19.1	17.84375	1.25625
9	16.1	17.84375	-1.74375
10	13.35	13.0237704918033	0.326229508196713
11	18.4	17.84375	0.556249999999995
12	14.7	9.93571428571428	4.76428571428572
13	10.6	13.0237704918033	-2.42377049180329
14	12.6	14.8044117647059	-2.20441176470589
15	16.2	15.5634615384615	0.636538461538459
16	13.6	13.0237704918033	0.576229508196713
17	18.9	17.6208333333333	1.27916666666667
18	14.1	13.0237704918033	1.07622950819671
19	14.5	14.8044117647059	-0.304411764705888
20	16.15	17.84375	-1.69375
21	14.75	14.8044117647059	-0.0544117647058879
22	14.8	15.5634615384615	-0.76346153846154
23	12.45	13.0237704918033	-0.573770491803288
24	12.65	13.0237704918033	-0.373770491803286
25	17.35	14.8044117647059	2.54558823529411
26	8.6	9.93571428571428	-1.33571428571428
27	18.4	17.84375	0.556249999999995
28	16.1	17.6208333333333	-1.52083333333333
29	11.6	13.0237704918033	-1.42377049180329
30	17.75	14.8044117647059	2.94558823529411
31	15.25	17.84375	-2.59375
32	17.65	15.5634615384615	2.08653846153846
33	15.6	14.8044117647059	0.795588235294112
34	16.35	17.84375	-1.49375
35	17.65	17.6208333333333	0.029166666666665
36	13.6	14.8044117647059	-1.20441176470589
37	11.7	14.8044117647059	-3.10441176470589
38	14.35	15.5634615384615	-1.21346153846154
39	14.75	15.5634615384615	-0.81346153846154
40	18.25	14.8044117647059	3.44558823529411
41	9.9	14.8044117647059	-4.90441176470589
42	16	17.6208333333333	-1.62083333333333
43	18.25	17.84375	0.406249999999996
44	16.85	17.84375	-0.993750000000002
45	14.6	13.0237704918033	1.57622950819671
46	13.85	13.0237704918033	0.826229508196713
47	18.95	17.84375	1.10625
48	15.6	15.5634615384615	0.0365384615384592
49	14.85	17.6208333333333	-2.77083333333333
50	11.75	13.0237704918033	-1.27377049180329
51	18.45	17.6208333333333	0.829166666666666
52	15.9	13.0237704918033	2.87622950819671
53	17.1	17.84375	-0.743750000000002
54	16.1	9.93571428571428	6.16428571428572
55	19.9	17.84375	2.05625
56	10.95	9.93571428571428	1.01428571428572
57	18.45	17.6208333333333	0.829166666666666
58	15.1	13.0237704918033	2.07622950819671
59	15	13.0237704918033	1.97622950819671
60	11.35	13.0237704918033	-1.67377049180329
61	15.95	13.0237704918033	2.92622950819671
62	18.1	15.5634615384615	2.53653846153846
63	14.6	15.5634615384615	-0.963461538461541
64	15.4	14.8044117647059	0.595588235294112
65	15.4	14.8044117647059	0.595588235294112
66	17.6	15.5634615384615	2.03653846153846
67	13.35	15.5634615384615	-2.21346153846154
68	19.1	17.84375	1.25625
69	15.35	15.5634615384615	-0.213461538461541
70	7.6	9.93571428571428	-2.33571428571428
71	13.4	13.0237704918033	0.376229508196714
72	13.9	15.5634615384615	-1.66346153846154
73	19.1	17.84375	1.25625
74	15.25	13.0237704918033	2.22622950819671
75	12.9	14.8044117647059	-1.90441176470589
76	16.1	15.5634615384615	0.536538461538461
77	17.35	14.8044117647059	2.54558823529411
78	13.15	15.5634615384615	-2.41346153846154
79	12.15	14.8044117647059	-2.65441176470589
80	12.6	13.0237704918033	-0.423770491803287
81	10.35	13.0237704918033	-2.67377049180329
82	15.4	15.5634615384615	-0.16346153846154
83	9.6	13.0237704918033	-3.42377049180329
84	18.2	15.5634615384615	2.63653846153846
85	13.6	13.0237704918033	0.576229508196713
86	14.85	13.0237704918033	1.82622950819671
87	14.75	17.84375	-3.09375
88	14.1	13.0237704918033	1.07622950819671
89	14.9	13.0237704918033	1.87622950819671
90	16.25	15.5634615384615	0.68653846153846
91	19.25	17.84375	1.40625
92	13.6	13.0237704918033	0.576229508196713
93	13.6	14.8044117647059	-1.20441176470589
94	15.65	15.5634615384615	0.0865384615384599
95	12.75	14.8044117647059	-2.05441176470589
96	14.6	13.0237704918033	1.57622950819671
97	9.85	9.93571428571428	-0.0857142857142836
98	12.65	13.0237704918033	-0.373770491803286
99	11.9	13.0237704918033	-1.12377049180329
100	19.2	17.6208333333333	1.57916666666667
101	16.6	13.0237704918033	3.57622950819671
102	11.2	9.93571428571428	1.26428571428572
103	15.25	14.8044117647059	0.445588235294112
104	11.9	13.0237704918033	-1.12377049180329
105	13.2	13.0237704918033	0.176229508196712
106	16.35	17.84375	-1.49375
107	12.4	13.0237704918033	-0.623770491803286
108	15.85	13.0237704918033	2.82622950819671
109	14.35	14.8044117647059	-0.454411764705888
110	18.15	14.8044117647059	3.34558823529411
111	11.15	13.0237704918033	-1.87377049180329
112	15.65	15.5634615384615	0.0865384615384599
113	17.75	14.8044117647059	2.94558823529411
114	7.65	9.93571428571428	-2.28571428571428
115	12.35	14.8044117647059	-2.45441176470589
116	15.6	14.8044117647059	0.795588235294112
117	19.3	17.84375	1.45625
118	15.2	13.0237704918033	2.17622950819671
119	17.1	14.8044117647059	2.29558823529411
120	15.6	13.0237704918033	2.57622950819671
121	18.4	17.6208333333333	0.779166666666665
122	19.05	17.84375	1.20625
123	18.55	14.8044117647059	3.74558823529411
124	19.1	17.6208333333333	1.47916666666667
125	13.1	13.0237704918033	0.0762295081967128
126	12.85	14.8044117647059	-1.95441176470589
127	9.5	13.0237704918033	-3.52377049180329
128	4.5	9.93571428571428	-5.43571428571428
129	11.85	9.93571428571428	1.91428571428572
130	13.6	15.5634615384615	-1.96346153846154
131	11.7	13.0237704918033	-1.32377049180329
132	12.4	13.0237704918033	-0.623770491803286
133	13.35	15.5634615384615	-2.21346153846154
134	11.4	13.0237704918033	-1.62377049180329
135	14.9	13.0237704918033	1.87622950819671
136	19.9	17.84375	2.05625
137	17.75	14.8044117647059	2.94558823529411
138	11.2	13.0237704918033	-1.82377049180329
139	14.6	15.5634615384615	-0.963461538461541
140	17.6	17.84375	-0.243750000000002
141	14.05	14.8044117647059	-0.754411764705887
142	16.1	15.5634615384615	0.536538461538461
143	13.35	14.8044117647059	-1.45441176470589
144	11.85	14.8044117647059	-2.95441176470589
145	11.95	13.0237704918033	-1.07377049180329
146	14.75	13.0237704918033	1.72622950819671
147	15.15	13.0237704918033	2.12622950819671
148	13.2	14.8044117647059	-1.60441176470589
149	16.85	15.5634615384615	1.28653846153846
150	7.85	13.0237704918033	-5.17377049180329
151	7.7	13.0237704918033	-5.32377049180329
152	12.6	13.0237704918033	-0.423770491803287
153	7.85	13.0237704918033	-5.17377049180329
154	10.95	9.93571428571428	1.01428571428572
155	12.35	13.0237704918033	-0.673770491803287
156	9.95	13.0237704918033	-3.07377049180329
157	14.9	13.0237704918033	1.87622950819671
158	16.65	13.0237704918033	3.62622950819671
159	13.4	13.0237704918033	0.376229508196714
160	13.95	13.0237704918033	0.926229508196712
161	15.7	14.8044117647059	0.895588235294111
162	16.85	15.5634615384615	1.28653846153846
163	10.95	13.0237704918033	-2.07377049180329
164	15.35	14.8044117647059	0.545588235294112
165	12.2	13.0237704918033	-0.823770491803288
166	15.1	13.0237704918033	2.07622950819671
167	17.75	17.6208333333333	0.129166666666666
168	15.2	13.0237704918033	2.17622950819671
169	14.6	14.8044117647059	-0.204411764705888
170	16.65	15.5634615384615	1.08653846153846
171	8.1	9.93571428571428	-1.83571428571428

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 4.35 & 9.93571428571428 & -5.58571428571428 \tabularnewline
2 & 12.7 & 9.93571428571428 & 2.76428571428572 \tabularnewline
3 & 18.1 & 17.84375 & 0.256249999999998 \tabularnewline
4 & 17.85 & 17.84375 & 0.00624999999999787 \tabularnewline
5 & 16.6 & 17.6208333333333 & -1.02083333333333 \tabularnewline
6 & 12.6 & 13.0237704918033 & -0.423770491803287 \tabularnewline
7 & 17.1 & 17.84375 & -0.743750000000002 \tabularnewline
8 & 19.1 & 17.84375 & 1.25625 \tabularnewline
9 & 16.1 & 17.84375 & -1.74375 \tabularnewline
10 & 13.35 & 13.0237704918033 & 0.326229508196713 \tabularnewline
11 & 18.4 & 17.84375 & 0.556249999999995 \tabularnewline
12 & 14.7 & 9.93571428571428 & 4.76428571428572 \tabularnewline
13 & 10.6 & 13.0237704918033 & -2.42377049180329 \tabularnewline
14 & 12.6 & 14.8044117647059 & -2.20441176470589 \tabularnewline
15 & 16.2 & 15.5634615384615 & 0.636538461538459 \tabularnewline
16 & 13.6 & 13.0237704918033 & 0.576229508196713 \tabularnewline
17 & 18.9 & 17.6208333333333 & 1.27916666666667 \tabularnewline
18 & 14.1 & 13.0237704918033 & 1.07622950819671 \tabularnewline
19 & 14.5 & 14.8044117647059 & -0.304411764705888 \tabularnewline
20 & 16.15 & 17.84375 & -1.69375 \tabularnewline
21 & 14.75 & 14.8044117647059 & -0.0544117647058879 \tabularnewline
22 & 14.8 & 15.5634615384615 & -0.76346153846154 \tabularnewline
23 & 12.45 & 13.0237704918033 & -0.573770491803288 \tabularnewline
24 & 12.65 & 13.0237704918033 & -0.373770491803286 \tabularnewline
25 & 17.35 & 14.8044117647059 & 2.54558823529411 \tabularnewline
26 & 8.6 & 9.93571428571428 & -1.33571428571428 \tabularnewline
27 & 18.4 & 17.84375 & 0.556249999999995 \tabularnewline
28 & 16.1 & 17.6208333333333 & -1.52083333333333 \tabularnewline
29 & 11.6 & 13.0237704918033 & -1.42377049180329 \tabularnewline
30 & 17.75 & 14.8044117647059 & 2.94558823529411 \tabularnewline
31 & 15.25 & 17.84375 & -2.59375 \tabularnewline
32 & 17.65 & 15.5634615384615 & 2.08653846153846 \tabularnewline
33 & 15.6 & 14.8044117647059 & 0.795588235294112 \tabularnewline
34 & 16.35 & 17.84375 & -1.49375 \tabularnewline
35 & 17.65 & 17.6208333333333 & 0.029166666666665 \tabularnewline
36 & 13.6 & 14.8044117647059 & -1.20441176470589 \tabularnewline
37 & 11.7 & 14.8044117647059 & -3.10441176470589 \tabularnewline
38 & 14.35 & 15.5634615384615 & -1.21346153846154 \tabularnewline
39 & 14.75 & 15.5634615384615 & -0.81346153846154 \tabularnewline
40 & 18.25 & 14.8044117647059 & 3.44558823529411 \tabularnewline
41 & 9.9 & 14.8044117647059 & -4.90441176470589 \tabularnewline
42 & 16 & 17.6208333333333 & -1.62083333333333 \tabularnewline
43 & 18.25 & 17.84375 & 0.406249999999996 \tabularnewline
44 & 16.85 & 17.84375 & -0.993750000000002 \tabularnewline
45 & 14.6 & 13.0237704918033 & 1.57622950819671 \tabularnewline
46 & 13.85 & 13.0237704918033 & 0.826229508196713 \tabularnewline
47 & 18.95 & 17.84375 & 1.10625 \tabularnewline
48 & 15.6 & 15.5634615384615 & 0.0365384615384592 \tabularnewline
49 & 14.85 & 17.6208333333333 & -2.77083333333333 \tabularnewline
50 & 11.75 & 13.0237704918033 & -1.27377049180329 \tabularnewline
51 & 18.45 & 17.6208333333333 & 0.829166666666666 \tabularnewline
52 & 15.9 & 13.0237704918033 & 2.87622950819671 \tabularnewline
53 & 17.1 & 17.84375 & -0.743750000000002 \tabularnewline
54 & 16.1 & 9.93571428571428 & 6.16428571428572 \tabularnewline
55 & 19.9 & 17.84375 & 2.05625 \tabularnewline
56 & 10.95 & 9.93571428571428 & 1.01428571428572 \tabularnewline
57 & 18.45 & 17.6208333333333 & 0.829166666666666 \tabularnewline
58 & 15.1 & 13.0237704918033 & 2.07622950819671 \tabularnewline
59 & 15 & 13.0237704918033 & 1.97622950819671 \tabularnewline
60 & 11.35 & 13.0237704918033 & -1.67377049180329 \tabularnewline
61 & 15.95 & 13.0237704918033 & 2.92622950819671 \tabularnewline
62 & 18.1 & 15.5634615384615 & 2.53653846153846 \tabularnewline
63 & 14.6 & 15.5634615384615 & -0.963461538461541 \tabularnewline
64 & 15.4 & 14.8044117647059 & 0.595588235294112 \tabularnewline
65 & 15.4 & 14.8044117647059 & 0.595588235294112 \tabularnewline
66 & 17.6 & 15.5634615384615 & 2.03653846153846 \tabularnewline
67 & 13.35 & 15.5634615384615 & -2.21346153846154 \tabularnewline
68 & 19.1 & 17.84375 & 1.25625 \tabularnewline
69 & 15.35 & 15.5634615384615 & -0.213461538461541 \tabularnewline
70 & 7.6 & 9.93571428571428 & -2.33571428571428 \tabularnewline
71 & 13.4 & 13.0237704918033 & 0.376229508196714 \tabularnewline
72 & 13.9 & 15.5634615384615 & -1.66346153846154 \tabularnewline
73 & 19.1 & 17.84375 & 1.25625 \tabularnewline
74 & 15.25 & 13.0237704918033 & 2.22622950819671 \tabularnewline
75 & 12.9 & 14.8044117647059 & -1.90441176470589 \tabularnewline
76 & 16.1 & 15.5634615384615 & 0.536538461538461 \tabularnewline
77 & 17.35 & 14.8044117647059 & 2.54558823529411 \tabularnewline
78 & 13.15 & 15.5634615384615 & -2.41346153846154 \tabularnewline
79 & 12.15 & 14.8044117647059 & -2.65441176470589 \tabularnewline
80 & 12.6 & 13.0237704918033 & -0.423770491803287 \tabularnewline
81 & 10.35 & 13.0237704918033 & -2.67377049180329 \tabularnewline
82 & 15.4 & 15.5634615384615 & -0.16346153846154 \tabularnewline
83 & 9.6 & 13.0237704918033 & -3.42377049180329 \tabularnewline
84 & 18.2 & 15.5634615384615 & 2.63653846153846 \tabularnewline
85 & 13.6 & 13.0237704918033 & 0.576229508196713 \tabularnewline
86 & 14.85 & 13.0237704918033 & 1.82622950819671 \tabularnewline
87 & 14.75 & 17.84375 & -3.09375 \tabularnewline
88 & 14.1 & 13.0237704918033 & 1.07622950819671 \tabularnewline
89 & 14.9 & 13.0237704918033 & 1.87622950819671 \tabularnewline
90 & 16.25 & 15.5634615384615 & 0.68653846153846 \tabularnewline
91 & 19.25 & 17.84375 & 1.40625 \tabularnewline
92 & 13.6 & 13.0237704918033 & 0.576229508196713 \tabularnewline
93 & 13.6 & 14.8044117647059 & -1.20441176470589 \tabularnewline
94 & 15.65 & 15.5634615384615 & 0.0865384615384599 \tabularnewline
95 & 12.75 & 14.8044117647059 & -2.05441176470589 \tabularnewline
96 & 14.6 & 13.0237704918033 & 1.57622950819671 \tabularnewline
97 & 9.85 & 9.93571428571428 & -0.0857142857142836 \tabularnewline
98 & 12.65 & 13.0237704918033 & -0.373770491803286 \tabularnewline
99 & 11.9 & 13.0237704918033 & -1.12377049180329 \tabularnewline
100 & 19.2 & 17.6208333333333 & 1.57916666666667 \tabularnewline
101 & 16.6 & 13.0237704918033 & 3.57622950819671 \tabularnewline
102 & 11.2 & 9.93571428571428 & 1.26428571428572 \tabularnewline
103 & 15.25 & 14.8044117647059 & 0.445588235294112 \tabularnewline
104 & 11.9 & 13.0237704918033 & -1.12377049180329 \tabularnewline
105 & 13.2 & 13.0237704918033 & 0.176229508196712 \tabularnewline
106 & 16.35 & 17.84375 & -1.49375 \tabularnewline
107 & 12.4 & 13.0237704918033 & -0.623770491803286 \tabularnewline
108 & 15.85 & 13.0237704918033 & 2.82622950819671 \tabularnewline
109 & 14.35 & 14.8044117647059 & -0.454411764705888 \tabularnewline
110 & 18.15 & 14.8044117647059 & 3.34558823529411 \tabularnewline
111 & 11.15 & 13.0237704918033 & -1.87377049180329 \tabularnewline
112 & 15.65 & 15.5634615384615 & 0.0865384615384599 \tabularnewline
113 & 17.75 & 14.8044117647059 & 2.94558823529411 \tabularnewline
114 & 7.65 & 9.93571428571428 & -2.28571428571428 \tabularnewline
115 & 12.35 & 14.8044117647059 & -2.45441176470589 \tabularnewline
116 & 15.6 & 14.8044117647059 & 0.795588235294112 \tabularnewline
117 & 19.3 & 17.84375 & 1.45625 \tabularnewline
118 & 15.2 & 13.0237704918033 & 2.17622950819671 \tabularnewline
119 & 17.1 & 14.8044117647059 & 2.29558823529411 \tabularnewline
120 & 15.6 & 13.0237704918033 & 2.57622950819671 \tabularnewline
121 & 18.4 & 17.6208333333333 & 0.779166666666665 \tabularnewline
122 & 19.05 & 17.84375 & 1.20625 \tabularnewline
123 & 18.55 & 14.8044117647059 & 3.74558823529411 \tabularnewline
124 & 19.1 & 17.6208333333333 & 1.47916666666667 \tabularnewline
125 & 13.1 & 13.0237704918033 & 0.0762295081967128 \tabularnewline
126 & 12.85 & 14.8044117647059 & -1.95441176470589 \tabularnewline
127 & 9.5 & 13.0237704918033 & -3.52377049180329 \tabularnewline
128 & 4.5 & 9.93571428571428 & -5.43571428571428 \tabularnewline
129 & 11.85 & 9.93571428571428 & 1.91428571428572 \tabularnewline
130 & 13.6 & 15.5634615384615 & -1.96346153846154 \tabularnewline
131 & 11.7 & 13.0237704918033 & -1.32377049180329 \tabularnewline
132 & 12.4 & 13.0237704918033 & -0.623770491803286 \tabularnewline
133 & 13.35 & 15.5634615384615 & -2.21346153846154 \tabularnewline
134 & 11.4 & 13.0237704918033 & -1.62377049180329 \tabularnewline
135 & 14.9 & 13.0237704918033 & 1.87622950819671 \tabularnewline
136 & 19.9 & 17.84375 & 2.05625 \tabularnewline
137 & 17.75 & 14.8044117647059 & 2.94558823529411 \tabularnewline
138 & 11.2 & 13.0237704918033 & -1.82377049180329 \tabularnewline
139 & 14.6 & 15.5634615384615 & -0.963461538461541 \tabularnewline
140 & 17.6 & 17.84375 & -0.243750000000002 \tabularnewline
141 & 14.05 & 14.8044117647059 & -0.754411764705887 \tabularnewline
142 & 16.1 & 15.5634615384615 & 0.536538461538461 \tabularnewline
143 & 13.35 & 14.8044117647059 & -1.45441176470589 \tabularnewline
144 & 11.85 & 14.8044117647059 & -2.95441176470589 \tabularnewline
145 & 11.95 & 13.0237704918033 & -1.07377049180329 \tabularnewline
146 & 14.75 & 13.0237704918033 & 1.72622950819671 \tabularnewline
147 & 15.15 & 13.0237704918033 & 2.12622950819671 \tabularnewline
148 & 13.2 & 14.8044117647059 & -1.60441176470589 \tabularnewline
149 & 16.85 & 15.5634615384615 & 1.28653846153846 \tabularnewline
150 & 7.85 & 13.0237704918033 & -5.17377049180329 \tabularnewline
151 & 7.7 & 13.0237704918033 & -5.32377049180329 \tabularnewline
152 & 12.6 & 13.0237704918033 & -0.423770491803287 \tabularnewline
153 & 7.85 & 13.0237704918033 & -5.17377049180329 \tabularnewline
154 & 10.95 & 9.93571428571428 & 1.01428571428572 \tabularnewline
155 & 12.35 & 13.0237704918033 & -0.673770491803287 \tabularnewline
156 & 9.95 & 13.0237704918033 & -3.07377049180329 \tabularnewline
157 & 14.9 & 13.0237704918033 & 1.87622950819671 \tabularnewline
158 & 16.65 & 13.0237704918033 & 3.62622950819671 \tabularnewline
159 & 13.4 & 13.0237704918033 & 0.376229508196714 \tabularnewline
160 & 13.95 & 13.0237704918033 & 0.926229508196712 \tabularnewline
161 & 15.7 & 14.8044117647059 & 0.895588235294111 \tabularnewline
162 & 16.85 & 15.5634615384615 & 1.28653846153846 \tabularnewline
163 & 10.95 & 13.0237704918033 & -2.07377049180329 \tabularnewline
164 & 15.35 & 14.8044117647059 & 0.545588235294112 \tabularnewline
165 & 12.2 & 13.0237704918033 & -0.823770491803288 \tabularnewline
166 & 15.1 & 13.0237704918033 & 2.07622950819671 \tabularnewline
167 & 17.75 & 17.6208333333333 & 0.129166666666666 \tabularnewline
168 & 15.2 & 13.0237704918033 & 2.17622950819671 \tabularnewline
169 & 14.6 & 14.8044117647059 & -0.204411764705888 \tabularnewline
170 & 16.65 & 15.5634615384615 & 1.08653846153846 \tabularnewline
171 & 8.1 & 9.93571428571428 & -1.83571428571428 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269861&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]4.35[/C][C]9.93571428571428[/C][C]-5.58571428571428[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]9.93571428571428[/C][C]2.76428571428572[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]17.84375[/C][C]0.256249999999998[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]17.84375[/C][C]0.00624999999999787[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]17.6208333333333[/C][C]-1.02083333333333[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]13.0237704918033[/C][C]-0.423770491803287[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]17.84375[/C][C]-0.743750000000002[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]17.84375[/C][C]1.25625[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]17.84375[/C][C]-1.74375[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]13.0237704918033[/C][C]0.326229508196713[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]17.84375[/C][C]0.556249999999995[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]9.93571428571428[/C][C]4.76428571428572[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]13.0237704918033[/C][C]-2.42377049180329[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]14.8044117647059[/C][C]-2.20441176470589[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]15.5634615384615[/C][C]0.636538461538459[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.0237704918033[/C][C]0.576229508196713[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]17.6208333333333[/C][C]1.27916666666667[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]13.0237704918033[/C][C]1.07622950819671[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]14.8044117647059[/C][C]-0.304411764705888[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.84375[/C][C]-1.69375[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]14.8044117647059[/C][C]-0.0544117647058879[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]15.5634615384615[/C][C]-0.76346153846154[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]13.0237704918033[/C][C]-0.573770491803288[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]13.0237704918033[/C][C]-0.373770491803286[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.8044117647059[/C][C]2.54558823529411[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]9.93571428571428[/C][C]-1.33571428571428[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.84375[/C][C]0.556249999999995[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]17.6208333333333[/C][C]-1.52083333333333[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]13.0237704918033[/C][C]-1.42377049180329[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]14.8044117647059[/C][C]2.94558823529411[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]17.84375[/C][C]-2.59375[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.5634615384615[/C][C]2.08653846153846[/C][/ROW]
[ROW][C]33[/C][C]15.6[/C][C]14.8044117647059[/C][C]0.795588235294112[/C][/ROW]
[ROW][C]34[/C][C]16.35[/C][C]17.84375[/C][C]-1.49375[/C][/ROW]
[ROW][C]35[/C][C]17.65[/C][C]17.6208333333333[/C][C]0.029166666666665[/C][/ROW]
[ROW][C]36[/C][C]13.6[/C][C]14.8044117647059[/C][C]-1.20441176470589[/C][/ROW]
[ROW][C]37[/C][C]11.7[/C][C]14.8044117647059[/C][C]-3.10441176470589[/C][/ROW]
[ROW][C]38[/C][C]14.35[/C][C]15.5634615384615[/C][C]-1.21346153846154[/C][/ROW]
[ROW][C]39[/C][C]14.75[/C][C]15.5634615384615[/C][C]-0.81346153846154[/C][/ROW]
[ROW][C]40[/C][C]18.25[/C][C]14.8044117647059[/C][C]3.44558823529411[/C][/ROW]
[ROW][C]41[/C][C]9.9[/C][C]14.8044117647059[/C][C]-4.90441176470589[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]17.6208333333333[/C][C]-1.62083333333333[/C][/ROW]
[ROW][C]43[/C][C]18.25[/C][C]17.84375[/C][C]0.406249999999996[/C][/ROW]
[ROW][C]44[/C][C]16.85[/C][C]17.84375[/C][C]-0.993750000000002[/C][/ROW]
[ROW][C]45[/C][C]14.6[/C][C]13.0237704918033[/C][C]1.57622950819671[/C][/ROW]
[ROW][C]46[/C][C]13.85[/C][C]13.0237704918033[/C][C]0.826229508196713[/C][/ROW]
[ROW][C]47[/C][C]18.95[/C][C]17.84375[/C][C]1.10625[/C][/ROW]
[ROW][C]48[/C][C]15.6[/C][C]15.5634615384615[/C][C]0.0365384615384592[/C][/ROW]
[ROW][C]49[/C][C]14.85[/C][C]17.6208333333333[/C][C]-2.77083333333333[/C][/ROW]
[ROW][C]50[/C][C]11.75[/C][C]13.0237704918033[/C][C]-1.27377049180329[/C][/ROW]
[ROW][C]51[/C][C]18.45[/C][C]17.6208333333333[/C][C]0.829166666666666[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]13.0237704918033[/C][C]2.87622950819671[/C][/ROW]
[ROW][C]53[/C][C]17.1[/C][C]17.84375[/C][C]-0.743750000000002[/C][/ROW]
[ROW][C]54[/C][C]16.1[/C][C]9.93571428571428[/C][C]6.16428571428572[/C][/ROW]
[ROW][C]55[/C][C]19.9[/C][C]17.84375[/C][C]2.05625[/C][/ROW]
[ROW][C]56[/C][C]10.95[/C][C]9.93571428571428[/C][C]1.01428571428572[/C][/ROW]
[ROW][C]57[/C][C]18.45[/C][C]17.6208333333333[/C][C]0.829166666666666[/C][/ROW]
[ROW][C]58[/C][C]15.1[/C][C]13.0237704918033[/C][C]2.07622950819671[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]13.0237704918033[/C][C]1.97622950819671[/C][/ROW]
[ROW][C]60[/C][C]11.35[/C][C]13.0237704918033[/C][C]-1.67377049180329[/C][/ROW]
[ROW][C]61[/C][C]15.95[/C][C]13.0237704918033[/C][C]2.92622950819671[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]15.5634615384615[/C][C]2.53653846153846[/C][/ROW]
[ROW][C]63[/C][C]14.6[/C][C]15.5634615384615[/C][C]-0.963461538461541[/C][/ROW]
[ROW][C]64[/C][C]15.4[/C][C]14.8044117647059[/C][C]0.595588235294112[/C][/ROW]
[ROW][C]65[/C][C]15.4[/C][C]14.8044117647059[/C][C]0.595588235294112[/C][/ROW]
[ROW][C]66[/C][C]17.6[/C][C]15.5634615384615[/C][C]2.03653846153846[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]15.5634615384615[/C][C]-2.21346153846154[/C][/ROW]
[ROW][C]68[/C][C]19.1[/C][C]17.84375[/C][C]1.25625[/C][/ROW]
[ROW][C]69[/C][C]15.35[/C][C]15.5634615384615[/C][C]-0.213461538461541[/C][/ROW]
[ROW][C]70[/C][C]7.6[/C][C]9.93571428571428[/C][C]-2.33571428571428[/C][/ROW]
[ROW][C]71[/C][C]13.4[/C][C]13.0237704918033[/C][C]0.376229508196714[/C][/ROW]
[ROW][C]72[/C][C]13.9[/C][C]15.5634615384615[/C][C]-1.66346153846154[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]17.84375[/C][C]1.25625[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]13.0237704918033[/C][C]2.22622950819671[/C][/ROW]
[ROW][C]75[/C][C]12.9[/C][C]14.8044117647059[/C][C]-1.90441176470589[/C][/ROW]
[ROW][C]76[/C][C]16.1[/C][C]15.5634615384615[/C][C]0.536538461538461[/C][/ROW]
[ROW][C]77[/C][C]17.35[/C][C]14.8044117647059[/C][C]2.54558823529411[/C][/ROW]
[ROW][C]78[/C][C]13.15[/C][C]15.5634615384615[/C][C]-2.41346153846154[/C][/ROW]
[ROW][C]79[/C][C]12.15[/C][C]14.8044117647059[/C][C]-2.65441176470589[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]13.0237704918033[/C][C]-0.423770491803287[/C][/ROW]
[ROW][C]81[/C][C]10.35[/C][C]13.0237704918033[/C][C]-2.67377049180329[/C][/ROW]
[ROW][C]82[/C][C]15.4[/C][C]15.5634615384615[/C][C]-0.16346153846154[/C][/ROW]
[ROW][C]83[/C][C]9.6[/C][C]13.0237704918033[/C][C]-3.42377049180329[/C][/ROW]
[ROW][C]84[/C][C]18.2[/C][C]15.5634615384615[/C][C]2.63653846153846[/C][/ROW]
[ROW][C]85[/C][C]13.6[/C][C]13.0237704918033[/C][C]0.576229508196713[/C][/ROW]
[ROW][C]86[/C][C]14.85[/C][C]13.0237704918033[/C][C]1.82622950819671[/C][/ROW]
[ROW][C]87[/C][C]14.75[/C][C]17.84375[/C][C]-3.09375[/C][/ROW]
[ROW][C]88[/C][C]14.1[/C][C]13.0237704918033[/C][C]1.07622950819671[/C][/ROW]
[ROW][C]89[/C][C]14.9[/C][C]13.0237704918033[/C][C]1.87622950819671[/C][/ROW]
[ROW][C]90[/C][C]16.25[/C][C]15.5634615384615[/C][C]0.68653846153846[/C][/ROW]
[ROW][C]91[/C][C]19.25[/C][C]17.84375[/C][C]1.40625[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]13.0237704918033[/C][C]0.576229508196713[/C][/ROW]
[ROW][C]93[/C][C]13.6[/C][C]14.8044117647059[/C][C]-1.20441176470589[/C][/ROW]
[ROW][C]94[/C][C]15.65[/C][C]15.5634615384615[/C][C]0.0865384615384599[/C][/ROW]
[ROW][C]95[/C][C]12.75[/C][C]14.8044117647059[/C][C]-2.05441176470589[/C][/ROW]
[ROW][C]96[/C][C]14.6[/C][C]13.0237704918033[/C][C]1.57622950819671[/C][/ROW]
[ROW][C]97[/C][C]9.85[/C][C]9.93571428571428[/C][C]-0.0857142857142836[/C][/ROW]
[ROW][C]98[/C][C]12.65[/C][C]13.0237704918033[/C][C]-0.373770491803286[/C][/ROW]
[ROW][C]99[/C][C]11.9[/C][C]13.0237704918033[/C][C]-1.12377049180329[/C][/ROW]
[ROW][C]100[/C][C]19.2[/C][C]17.6208333333333[/C][C]1.57916666666667[/C][/ROW]
[ROW][C]101[/C][C]16.6[/C][C]13.0237704918033[/C][C]3.57622950819671[/C][/ROW]
[ROW][C]102[/C][C]11.2[/C][C]9.93571428571428[/C][C]1.26428571428572[/C][/ROW]
[ROW][C]103[/C][C]15.25[/C][C]14.8044117647059[/C][C]0.445588235294112[/C][/ROW]
[ROW][C]104[/C][C]11.9[/C][C]13.0237704918033[/C][C]-1.12377049180329[/C][/ROW]
[ROW][C]105[/C][C]13.2[/C][C]13.0237704918033[/C][C]0.176229508196712[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]17.84375[/C][C]-1.49375[/C][/ROW]
[ROW][C]107[/C][C]12.4[/C][C]13.0237704918033[/C][C]-0.623770491803286[/C][/ROW]
[ROW][C]108[/C][C]15.85[/C][C]13.0237704918033[/C][C]2.82622950819671[/C][/ROW]
[ROW][C]109[/C][C]14.35[/C][C]14.8044117647059[/C][C]-0.454411764705888[/C][/ROW]
[ROW][C]110[/C][C]18.15[/C][C]14.8044117647059[/C][C]3.34558823529411[/C][/ROW]
[ROW][C]111[/C][C]11.15[/C][C]13.0237704918033[/C][C]-1.87377049180329[/C][/ROW]
[ROW][C]112[/C][C]15.65[/C][C]15.5634615384615[/C][C]0.0865384615384599[/C][/ROW]
[ROW][C]113[/C][C]17.75[/C][C]14.8044117647059[/C][C]2.94558823529411[/C][/ROW]
[ROW][C]114[/C][C]7.65[/C][C]9.93571428571428[/C][C]-2.28571428571428[/C][/ROW]
[ROW][C]115[/C][C]12.35[/C][C]14.8044117647059[/C][C]-2.45441176470589[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]14.8044117647059[/C][C]0.795588235294112[/C][/ROW]
[ROW][C]117[/C][C]19.3[/C][C]17.84375[/C][C]1.45625[/C][/ROW]
[ROW][C]118[/C][C]15.2[/C][C]13.0237704918033[/C][C]2.17622950819671[/C][/ROW]
[ROW][C]119[/C][C]17.1[/C][C]14.8044117647059[/C][C]2.29558823529411[/C][/ROW]
[ROW][C]120[/C][C]15.6[/C][C]13.0237704918033[/C][C]2.57622950819671[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]17.6208333333333[/C][C]0.779166666666665[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]17.84375[/C][C]1.20625[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]14.8044117647059[/C][C]3.74558823529411[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]17.6208333333333[/C][C]1.47916666666667[/C][/ROW]
[ROW][C]125[/C][C]13.1[/C][C]13.0237704918033[/C][C]0.0762295081967128[/C][/ROW]
[ROW][C]126[/C][C]12.85[/C][C]14.8044117647059[/C][C]-1.95441176470589[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]13.0237704918033[/C][C]-3.52377049180329[/C][/ROW]
[ROW][C]128[/C][C]4.5[/C][C]9.93571428571428[/C][C]-5.43571428571428[/C][/ROW]
[ROW][C]129[/C][C]11.85[/C][C]9.93571428571428[/C][C]1.91428571428572[/C][/ROW]
[ROW][C]130[/C][C]13.6[/C][C]15.5634615384615[/C][C]-1.96346153846154[/C][/ROW]
[ROW][C]131[/C][C]11.7[/C][C]13.0237704918033[/C][C]-1.32377049180329[/C][/ROW]
[ROW][C]132[/C][C]12.4[/C][C]13.0237704918033[/C][C]-0.623770491803286[/C][/ROW]
[ROW][C]133[/C][C]13.35[/C][C]15.5634615384615[/C][C]-2.21346153846154[/C][/ROW]
[ROW][C]134[/C][C]11.4[/C][C]13.0237704918033[/C][C]-1.62377049180329[/C][/ROW]
[ROW][C]135[/C][C]14.9[/C][C]13.0237704918033[/C][C]1.87622950819671[/C][/ROW]
[ROW][C]136[/C][C]19.9[/C][C]17.84375[/C][C]2.05625[/C][/ROW]
[ROW][C]137[/C][C]17.75[/C][C]14.8044117647059[/C][C]2.94558823529411[/C][/ROW]
[ROW][C]138[/C][C]11.2[/C][C]13.0237704918033[/C][C]-1.82377049180329[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]15.5634615384615[/C][C]-0.963461538461541[/C][/ROW]
[ROW][C]140[/C][C]17.6[/C][C]17.84375[/C][C]-0.243750000000002[/C][/ROW]
[ROW][C]141[/C][C]14.05[/C][C]14.8044117647059[/C][C]-0.754411764705887[/C][/ROW]
[ROW][C]142[/C][C]16.1[/C][C]15.5634615384615[/C][C]0.536538461538461[/C][/ROW]
[ROW][C]143[/C][C]13.35[/C][C]14.8044117647059[/C][C]-1.45441176470589[/C][/ROW]
[ROW][C]144[/C][C]11.85[/C][C]14.8044117647059[/C][C]-2.95441176470589[/C][/ROW]
[ROW][C]145[/C][C]11.95[/C][C]13.0237704918033[/C][C]-1.07377049180329[/C][/ROW]
[ROW][C]146[/C][C]14.75[/C][C]13.0237704918033[/C][C]1.72622950819671[/C][/ROW]
[ROW][C]147[/C][C]15.15[/C][C]13.0237704918033[/C][C]2.12622950819671[/C][/ROW]
[ROW][C]148[/C][C]13.2[/C][C]14.8044117647059[/C][C]-1.60441176470589[/C][/ROW]
[ROW][C]149[/C][C]16.85[/C][C]15.5634615384615[/C][C]1.28653846153846[/C][/ROW]
[ROW][C]150[/C][C]7.85[/C][C]13.0237704918033[/C][C]-5.17377049180329[/C][/ROW]
[ROW][C]151[/C][C]7.7[/C][C]13.0237704918033[/C][C]-5.32377049180329[/C][/ROW]
[ROW][C]152[/C][C]12.6[/C][C]13.0237704918033[/C][C]-0.423770491803287[/C][/ROW]
[ROW][C]153[/C][C]7.85[/C][C]13.0237704918033[/C][C]-5.17377049180329[/C][/ROW]
[ROW][C]154[/C][C]10.95[/C][C]9.93571428571428[/C][C]1.01428571428572[/C][/ROW]
[ROW][C]155[/C][C]12.35[/C][C]13.0237704918033[/C][C]-0.673770491803287[/C][/ROW]
[ROW][C]156[/C][C]9.95[/C][C]13.0237704918033[/C][C]-3.07377049180329[/C][/ROW]
[ROW][C]157[/C][C]14.9[/C][C]13.0237704918033[/C][C]1.87622950819671[/C][/ROW]
[ROW][C]158[/C][C]16.65[/C][C]13.0237704918033[/C][C]3.62622950819671[/C][/ROW]
[ROW][C]159[/C][C]13.4[/C][C]13.0237704918033[/C][C]0.376229508196714[/C][/ROW]
[ROW][C]160[/C][C]13.95[/C][C]13.0237704918033[/C][C]0.926229508196712[/C][/ROW]
[ROW][C]161[/C][C]15.7[/C][C]14.8044117647059[/C][C]0.895588235294111[/C][/ROW]
[ROW][C]162[/C][C]16.85[/C][C]15.5634615384615[/C][C]1.28653846153846[/C][/ROW]
[ROW][C]163[/C][C]10.95[/C][C]13.0237704918033[/C][C]-2.07377049180329[/C][/ROW]
[ROW][C]164[/C][C]15.35[/C][C]14.8044117647059[/C][C]0.545588235294112[/C][/ROW]
[ROW][C]165[/C][C]12.2[/C][C]13.0237704918033[/C][C]-0.823770491803288[/C][/ROW]
[ROW][C]166[/C][C]15.1[/C][C]13.0237704918033[/C][C]2.07622950819671[/C][/ROW]
[ROW][C]167[/C][C]17.75[/C][C]17.6208333333333[/C][C]0.129166666666666[/C][/ROW]
[ROW][C]168[/C][C]15.2[/C][C]13.0237704918033[/C][C]2.17622950819671[/C][/ROW]
[ROW][C]169[/C][C]14.6[/C][C]14.8044117647059[/C][C]-0.204411764705888[/C][/ROW]
[ROW][C]170[/C][C]16.65[/C][C]15.5634615384615[/C][C]1.08653846153846[/C][/ROW]
[ROW][C]171[/C][C]8.1[/C][C]9.93571428571428[/C][C]-1.83571428571428[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269861&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269861&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	4.35	9.93571428571428	-5.58571428571428
2	12.7	9.93571428571428	2.76428571428572
3	18.1	17.84375	0.256249999999998
4	17.85	17.84375	0.00624999999999787
5	16.6	17.6208333333333	-1.02083333333333
6	12.6	13.0237704918033	-0.423770491803287
7	17.1	17.84375	-0.743750000000002
8	19.1	17.84375	1.25625
9	16.1	17.84375	-1.74375
10	13.35	13.0237704918033	0.326229508196713
11	18.4	17.84375	0.556249999999995
12	14.7	9.93571428571428	4.76428571428572
13	10.6	13.0237704918033	-2.42377049180329
14	12.6	14.8044117647059	-2.20441176470589
15	16.2	15.5634615384615	0.636538461538459
16	13.6	13.0237704918033	0.576229508196713
17	18.9	17.6208333333333	1.27916666666667
18	14.1	13.0237704918033	1.07622950819671
19	14.5	14.8044117647059	-0.304411764705888
20	16.15	17.84375	-1.69375
21	14.75	14.8044117647059	-0.0544117647058879
22	14.8	15.5634615384615	-0.76346153846154
23	12.45	13.0237704918033	-0.573770491803288
24	12.65	13.0237704918033	-0.373770491803286
25	17.35	14.8044117647059	2.54558823529411
26	8.6	9.93571428571428	-1.33571428571428
27	18.4	17.84375	0.556249999999995
28	16.1	17.6208333333333	-1.52083333333333
29	11.6	13.0237704918033	-1.42377049180329
30	17.75	14.8044117647059	2.94558823529411
31	15.25	17.84375	-2.59375
32	17.65	15.5634615384615	2.08653846153846
33	15.6	14.8044117647059	0.795588235294112
34	16.35	17.84375	-1.49375
35	17.65	17.6208333333333	0.029166666666665
36	13.6	14.8044117647059	-1.20441176470589
37	11.7	14.8044117647059	-3.10441176470589
38	14.35	15.5634615384615	-1.21346153846154
39	14.75	15.5634615384615	-0.81346153846154
40	18.25	14.8044117647059	3.44558823529411
41	9.9	14.8044117647059	-4.90441176470589
42	16	17.6208333333333	-1.62083333333333
43	18.25	17.84375	0.406249999999996
44	16.85	17.84375	-0.993750000000002
45	14.6	13.0237704918033	1.57622950819671
46	13.85	13.0237704918033	0.826229508196713
47	18.95	17.84375	1.10625
48	15.6	15.5634615384615	0.0365384615384592
49	14.85	17.6208333333333	-2.77083333333333
50	11.75	13.0237704918033	-1.27377049180329
51	18.45	17.6208333333333	0.829166666666666
52	15.9	13.0237704918033	2.87622950819671
53	17.1	17.84375	-0.743750000000002
54	16.1	9.93571428571428	6.16428571428572
55	19.9	17.84375	2.05625
56	10.95	9.93571428571428	1.01428571428572
57	18.45	17.6208333333333	0.829166666666666
58	15.1	13.0237704918033	2.07622950819671
59	15	13.0237704918033	1.97622950819671
60	11.35	13.0237704918033	-1.67377049180329
61	15.95	13.0237704918033	2.92622950819671
62	18.1	15.5634615384615	2.53653846153846
63	14.6	15.5634615384615	-0.963461538461541
64	15.4	14.8044117647059	0.595588235294112
65	15.4	14.8044117647059	0.595588235294112
66	17.6	15.5634615384615	2.03653846153846
67	13.35	15.5634615384615	-2.21346153846154
68	19.1	17.84375	1.25625
69	15.35	15.5634615384615	-0.213461538461541
70	7.6	9.93571428571428	-2.33571428571428
71	13.4	13.0237704918033	0.376229508196714
72	13.9	15.5634615384615	-1.66346153846154
73	19.1	17.84375	1.25625
74	15.25	13.0237704918033	2.22622950819671
75	12.9	14.8044117647059	-1.90441176470589
76	16.1	15.5634615384615	0.536538461538461
77	17.35	14.8044117647059	2.54558823529411
78	13.15	15.5634615384615	-2.41346153846154
79	12.15	14.8044117647059	-2.65441176470589
80	12.6	13.0237704918033	-0.423770491803287
81	10.35	13.0237704918033	-2.67377049180329
82	15.4	15.5634615384615	-0.16346153846154
83	9.6	13.0237704918033	-3.42377049180329
84	18.2	15.5634615384615	2.63653846153846
85	13.6	13.0237704918033	0.576229508196713
86	14.85	13.0237704918033	1.82622950819671
87	14.75	17.84375	-3.09375
88	14.1	13.0237704918033	1.07622950819671
89	14.9	13.0237704918033	1.87622950819671
90	16.25	15.5634615384615	0.68653846153846
91	19.25	17.84375	1.40625
92	13.6	13.0237704918033	0.576229508196713
93	13.6	14.8044117647059	-1.20441176470589
94	15.65	15.5634615384615	0.0865384615384599
95	12.75	14.8044117647059	-2.05441176470589
96	14.6	13.0237704918033	1.57622950819671
97	9.85	9.93571428571428	-0.0857142857142836
98	12.65	13.0237704918033	-0.373770491803286
99	11.9	13.0237704918033	-1.12377049180329
100	19.2	17.6208333333333	1.57916666666667
101	16.6	13.0237704918033	3.57622950819671
102	11.2	9.93571428571428	1.26428571428572
103	15.25	14.8044117647059	0.445588235294112
104	11.9	13.0237704918033	-1.12377049180329
105	13.2	13.0237704918033	0.176229508196712
106	16.35	17.84375	-1.49375
107	12.4	13.0237704918033	-0.623770491803286
108	15.85	13.0237704918033	2.82622950819671
109	14.35	14.8044117647059	-0.454411764705888
110	18.15	14.8044117647059	3.34558823529411
111	11.15	13.0237704918033	-1.87377049180329
112	15.65	15.5634615384615	0.0865384615384599
113	17.75	14.8044117647059	2.94558823529411
114	7.65	9.93571428571428	-2.28571428571428
115	12.35	14.8044117647059	-2.45441176470589
116	15.6	14.8044117647059	0.795588235294112
117	19.3	17.84375	1.45625
118	15.2	13.0237704918033	2.17622950819671
119	17.1	14.8044117647059	2.29558823529411
120	15.6	13.0237704918033	2.57622950819671
121	18.4	17.6208333333333	0.779166666666665
122	19.05	17.84375	1.20625
123	18.55	14.8044117647059	3.74558823529411
124	19.1	17.6208333333333	1.47916666666667
125	13.1	13.0237704918033	0.0762295081967128
126	12.85	14.8044117647059	-1.95441176470589
127	9.5	13.0237704918033	-3.52377049180329
128	4.5	9.93571428571428	-5.43571428571428
129	11.85	9.93571428571428	1.91428571428572
130	13.6	15.5634615384615	-1.96346153846154
131	11.7	13.0237704918033	-1.32377049180329
132	12.4	13.0237704918033	-0.623770491803286
133	13.35	15.5634615384615	-2.21346153846154
134	11.4	13.0237704918033	-1.62377049180329
135	14.9	13.0237704918033	1.87622950819671
136	19.9	17.84375	2.05625
137	17.75	14.8044117647059	2.94558823529411
138	11.2	13.0237704918033	-1.82377049180329
139	14.6	15.5634615384615	-0.963461538461541
140	17.6	17.84375	-0.243750000000002
141	14.05	14.8044117647059	-0.754411764705887
142	16.1	15.5634615384615	0.536538461538461
143	13.35	14.8044117647059	-1.45441176470589
144	11.85	14.8044117647059	-2.95441176470589
145	11.95	13.0237704918033	-1.07377049180329
146	14.75	13.0237704918033	1.72622950819671
147	15.15	13.0237704918033	2.12622950819671
148	13.2	14.8044117647059	-1.60441176470589
149	16.85	15.5634615384615	1.28653846153846
150	7.85	13.0237704918033	-5.17377049180329
151	7.7	13.0237704918033	-5.32377049180329
152	12.6	13.0237704918033	-0.423770491803287
153	7.85	13.0237704918033	-5.17377049180329
154	10.95	9.93571428571428	1.01428571428572
155	12.35	13.0237704918033	-0.673770491803287
156	9.95	13.0237704918033	-3.07377049180329
157	14.9	13.0237704918033	1.87622950819671
158	16.65	13.0237704918033	3.62622950819671
159	13.4	13.0237704918033	0.376229508196714
160	13.95	13.0237704918033	0.926229508196712
161	15.7	14.8044117647059	0.895588235294111
162	16.85	15.5634615384615	1.28653846153846
163	10.95	13.0237704918033	-2.07377049180329
164	15.35	14.8044117647059	0.545588235294112
165	12.2	13.0237704918033	-0.823770491803288
166	15.1	13.0237704918033	2.07622950819671
167	17.75	17.6208333333333	0.129166666666666
168	15.2	13.0237704918033	2.17622950819671
169	14.6	14.8044117647059	-0.204411764705888
170	16.65	15.5634615384615	1.08653846153846
171	8.1	9.93571428571428	-1.83571428571428

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 = 3 ; par4 = no ;

Parameters (R input):

par1 = 6 ; par2 = none ; par3 = 3 ; par4 = no ;

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

par4 <- 'no'
par3 <- '2'
par2 <- 'none'
par1 <- '6'
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