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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Fri, 24 Dec 2010 11:01:47 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/24/t12931887429jddmf5rns0h355.htm/, Retrieved Tue, 30 Apr 2024 05:23:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114761, Retrieved Tue, 30 Apr 2024 05:23:43 +0000

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Estimated Impact

132

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-       [Recursive Partitioning (Regression Trees)] [] [2010-12-24 11:01:47] [40b262140b988d7b8204c4955f8b7651] [Current]
- RMPD    [Recursive Partitioning (Regression Trees)] [] [2010-12-24 21:12:10] [cc61d4f8286f3f36f43e751ed98b6d78] 

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9,1	4,5	1,0	-1,0	1989,3
9,0	4,3	1,0	3,0	2097,8
9,0	4,3	1,3	2,0	2154,9
8,9	4,2	1,1	3,0	2152,2
8,8	4,0	0,8	5,0	2250,3
8,7	3,8	0,7	5,0	2346,9
8,5	4,1	0,7	3,0	2525,6
8,3	4,2	0,9	2,0	2409,4
8,1	4,0	1,3	1,0	2394,4
7,9	4,3	1,4	-4,0	2401,3
7,8	4,7	1,6	1,0	2354,3
7,6	5,0	2,1	1,0	2450,4
7,4	5,1	0,3	6,0	2504,7
7,2	5,4	2,1	3,0	2661,4
7,0	5,4	2,5	2,0	2880,4
7,0	5,4	2,3	2,0	3064,4
6,8	5,5	2,4	2,0	3141,1
6,8	5,8	3,0	-8,0	3327,7
6,7	5,7	1,7	0,0	3565,0
6,8	5,5	3,5	-2,0	3403,1
6,7	5,6	4,0	3,0	3149,9
6,7	5,6	3,7	5,0	3006,8
6,7	5,5	3,7	8,0	3230,7
6,5	5,5	3,0	8,0	3361,1
6,3	5,7	2,7	9,0	3484,7
6,3	5,6	2,5	11,0	3411,1
6,3	5,6	2,2	13,0	3288,2
6,5	5,4	2,9	12,0	3280,4
6,6	5,2	3,1	13,0	3174,0
6,5	5,1	3,0	15,0	3165,3
6,3	5,1	2,8	13,0	3092,7
6,3	5,0	2,5	16,0	3053,1
6,5	5,3	1,9	10,0	3182,0
7,0	5,4	1,9	14,0	2999,9
7,1	5,3	1,8	14,0	3249,6
7,3	5,1	2,0	15,0	3210,5
7,3	5,0	2,6	13,0	3030,3
7,4	5,0	2,5	8,0	2803,5
7,4	4,6	2,5	7,0	2767,6
7,3	4,8	1,6	3,0	2882,6
7,4	5,1	1,4	3,0	2863,4
7,5	5,1	0,8	4,0	2897,1
7,7	5,1	1,1	4,0	3012,6
7,7	5,4	1,3	0,0	3143,0
7,7	5,3	1,2	-4,0	3032,9
7,7	5,3	1,3	-14,0	3045,8
7,7	5,1	1,1	-18,0	3110,5
7,8	4,9	1,3	-8,0	3013,2
8,0	4,7	1,2	-1,0	2987,1
8,1	4,4	1,6	1,0	2995,6
8,1	4,6	1,7	2,0	2833,2
8,2	4,5	1,5	0,0	2849,0
8,2	4,2	0,9	1,0	2794,8
8,2	4,0	1,5	0,0	2845,3
8,1	3,9	1,4	-1,0	2915,0
8,1	4,1	1,6	-3,0	2892,6
8,2	4,1	1,7	-3,0	2604,4
8,3	3,7	1,4	-3,0	2641,7
8,3	3,8	1,8	-4,0	2659,8
8,4	4,1	1,7	-8,0	2638,5
8,5	4,1	1,4	-9,0	2720,3
8,5	4,0	1,2	-13,0	2745,9
8,4	4,3	1,0	-18,0	2735,7
8,0	4,4	1,7	-11,0	2811,7
7,9	4,2	2,4	-9,0	2799,4
8,1	4,2	2,0	-10,0	2555,3
8,5	4,0	2,1	-13,0	2305,0
8,8	4,0	2,0	-11,0	2215,0
8,8	4,3	1,8	-5,0	2065,8
8,6	4,4	2,7	-15,0	1940,5
8,3	4,4	2,3	-6,0	2042,0
8,3	4,3	1,9	-6,0	1995,4
8,3	4,1	2,0	-3,0	1946,8
8,4	4,1	2,3	-1,0	1765,9
8,4	3,9	2,8	-3,0	1635,3
8,5	3,8	2,4	-4,0	1833,4
8,6	3,7	2,3	-6,0	1910,4
8,6	3,5	2,7	0,0	1959,7
8,6	3,7	2,7	-4,0	1969,6
8,6	3,7	2,9	-2,0	2061,4
8,6	3,5	3,0	-2,0	2093,5
8,5	3,3	2,2	-6,0	2120,9
8,4	3,2	2,3	-7,0	2174,6
8,4	3,3	2,8	-6,0	2196,7
8,4	3,1	2,8	-6,0	2350,4
8,5	3,2	2,8	-3,0	2440,3
8,5	3,4	2,2	-2,0	2408,6
8,6	3,5	2,6	-5,0	2472,8
8,6	3,3	2,8	-11,0	2407,6
8,4	3,5	2,5	-11,0	2454,6
8,2	3,5	2,4	-11,0	2448,1
8,0	3,8	2,3	-10,0	2497,8
8,0	4,0	1,9	-14,0	2645,6
8,0	4,0	1,7	-8,0	2756,8
8,0	4,1	2,0	-9,0	2849,3
7,9	4,0	2,1	-5,0	2921,4
7,9	3,8	1,7	-1,0	2981,9
7,8	3,7	1,8	-2,0	3080,6
7,8	3,8	1,8	-5,0	3106,2
8,0	3,7	1,8	-4,0	3119,3
7,8	4,0	1,3	-6,0	3061,3
7,4	4,2	1,3	-2,0	3097,3
7,2	4,0	1,3	-2,0	3161,7
7,0	4,1	1,2	-2,0	3257,2
7,0	4,2	1,4	-2,0	3277,0
7,2	4,5	2,2	2,0	3295,3
7,2	4,6	2,9	1,0	3364,0
7,2	4,5	3,1	-8,0	3494,2
7,0	4,5	3,5	-1,0	3667,0
6,9	4,5	3,6	1,0	3813,1
6,8	4,4	4,4	-1,0	3918,0
6,8	4,3	4,1	2,0	3895,5
6,8	4,5	5,1	2,0	3801,1
6,9	4,1	5,8	1,0	3570,1
7,2	4,1	5,9	-1,0	3701,6
7,2	4,3	5,4	-2,0	3862,3
7,2	4,4	5,5	-2,0	3970,1
7,1	4,7	4,8	-1,0	4138,5
7,2	5,0	3,2	-8,0	4199,8
7,3	4,7	2,7	-4,0	4290,9
7,5	4,5	2,1	-6,0	4443,9
7,6	4,5	1,9	-3,0	4502,6
7,7	4,5	0,6	-3,0	4357,0
7,7	5,5	0,7	-7,0	4591,3
7,7	4,5	-0,2	-9,0	4697,0
7,8	4,4	-1,0	-11,0	4621,4
8,0	4,2	-1,7	-13,0	4562,8
8,1	3,9	-0,7	-11,0	4202,5
8,1	3,9	-1,0	-9,0	4296,5
8,0	4,2	-0,9	-17,0	4435,2
8,1	4,0	0,0	-22,0	4105,2
8,2	3,8	0,3	-25,0	4116,7
8,3	3,7	0,8	-20,0	3844,5
8,4	3,7	0,8	-24,0	3721,0
8,4	3,7	1,9	-24,0	3674,4
8,4	3,7	2,1	-22,0	3857,6
8,5	3,7	2,5	-19,0	3801,1
8,5	3,8	2,7	-18,0	3504,4
8,6	3,7	2,4	-17,0	3032,6
8,6	3,5	2,4	-11,0	3047,0
8,5	3,5	2,9	-11,0	2962,3
8,5	3,1	3,1	-12,0	2197,8

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114761&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114761&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114761&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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.9236
R-squared	0.8531
RMSE	0.2726

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9236 \tabularnewline
R-squared & 0.8531 \tabularnewline
RMSE & 0.2726 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114761&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9236[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8531[/C][/ROW]
[ROW][C]RMSE[/C][C]0.2726[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114761&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114761&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	0.9236
R-squared	0.8531
RMSE	0.2726

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	9.1	7.73157894736842	1.36842105263158
2	9	8.78571428571429	0.214285714285714
3	9	8.78571428571429	0.214285714285714
4	8.9	8.78571428571429	0.114285714285714
5	8.8	8.78571428571429	0.0142857142857142
6	8.7	8.78571428571429	-0.0857142857142872
7	8.5	8.27	0.23
8	8.3	8.27	0.0300000000000011
9	8.1	8.27	-0.17
10	7.9	8.27	-0.369999999999999
11	7.8	7.73157894736842	0.0684210526315789
12	7.6	7.1875	0.4125
13	7.4	7.73157894736842	-0.331578947368421
14	7.2	6.70952380952381	0.490476190476191
15	7	6.70952380952381	0.29047619047619
16	7	6.70952380952381	0.29047619047619
17	6.8	6.70952380952381	0.0904761904761902
18	6.8	6.70952380952381	0.0904761904761902
19	6.7	7.73157894736842	-1.03157894736842
20	6.8	6.70952380952381	0.0904761904761902
21	6.7	6.70952380952381	-0.0095238095238095
22	6.7	6.70952380952381	-0.0095238095238095
23	6.7	6.70952380952381	-0.0095238095238095
24	6.5	6.70952380952381	-0.20952380952381
25	6.3	6.70952380952381	-0.40952380952381
26	6.3	6.70952380952381	-0.40952380952381
27	6.3	6.70952380952381	-0.40952380952381
28	6.5	6.70952380952381	-0.20952380952381
29	6.6	6.70952380952381	-0.10952380952381
30	6.5	6.70952380952381	-0.20952380952381
31	6.3	6.70952380952381	-0.40952380952381
32	6.3	7.1875	-0.8875
33	6.5	6.70952380952381	-0.20952380952381
34	7	6.70952380952381	0.29047619047619
35	7.1	6.70952380952381	0.39047619047619
36	7.3	6.70952380952381	0.59047619047619
37	7.3	7.1875	0.1125
38	7.4	7.1875	0.2125
39	7.4	7.1875	0.2125
40	7.3	7.73157894736842	-0.431578947368421
41	7.4	7.73157894736842	-0.331578947368421
42	7.5	7.73157894736842	-0.231578947368421
43	7.7	7.73157894736842	-0.0315789473684207
44	7.7	7.73157894736842	-0.0315789473684207
45	7.7	7.73157894736842	-0.0315789473684207
46	7.7	7.73157894736842	-0.0315789473684207
47	7.7	7.73157894736842	-0.0315789473684207
48	7.8	7.73157894736842	0.0684210526315789
49	8	7.73157894736842	0.268421052631579
50	8.1	8.06428571428571	0.0357142857142847
51	8.1	7.73157894736842	0.368421052631579
52	8.2	7.73157894736842	0.468421052631578
53	8.2	8.27	-0.0700000000000003
54	8.2	8.27	-0.0700000000000003
55	8.1	8.27	-0.17
56	8.1	8.06428571428571	0.0357142857142847
57	8.2	8.06428571428571	0.135714285714284
58	8.3	8.47272727272727	-0.172727272727272
59	8.3	8.06428571428571	0.235714285714286
60	8.4	8.06428571428571	0.335714285714285
61	8.5	8.27	0.23
62	8.5	8.27	0.23
63	8.4	8.27	0.130000000000001
64	8	8.06428571428571	-0.064285714285715
65	7.9	8.06428571428571	-0.164285714285715
66	8.1	8.06428571428571	0.0357142857142847
67	8.5	8.50588235294118	-0.00588235294117645
68	8.8	8.50588235294118	0.294117647058824
69	8.8	8.78571428571429	0.0142857142857142
70	8.6	8.50588235294118	0.0941176470588232
71	8.3	8.50588235294118	-0.205882352941176
72	8.3	8.78571428571429	-0.485714285714286
73	8.3	8.50588235294118	-0.205882352941176
74	8.4	8.50588235294118	-0.105882352941176
75	8.4	8.50588235294118	-0.105882352941176
76	8.5	8.50588235294118	-0.00588235294117645
77	8.6	8.50588235294118	0.0941176470588232
78	8.6	8.50588235294118	0.0941176470588232
79	8.6	8.50588235294118	0.0941176470588232
80	8.6	8.50588235294118	0.0941176470588232
81	8.6	8.50588235294118	0.0941176470588232
82	8.5	8.50588235294118	-0.00588235294117645
83	8.4	8.50588235294118	-0.105882352941176
84	8.4	8.50588235294118	-0.105882352941176
85	8.4	8.47272727272727	-0.0727272727272723
86	8.5	8.47272727272727	0.0272727272727273
87	8.5	8.47272727272727	0.0272727272727273
88	8.6	8.47272727272727	0.127272727272727
89	8.6	8.47272727272727	0.127272727272727
90	8.4	8.47272727272727	-0.0727272727272723
91	8.2	8.47272727272727	-0.272727272727273
92	8	8.06428571428571	-0.064285714285715
93	8	8.06428571428571	-0.064285714285715
94	8	8.06428571428571	-0.064285714285715
95	8	8.06428571428571	-0.064285714285715
96	7.9	8.06428571428571	-0.164285714285715
97	7.9	8.06428571428571	-0.164285714285715
98	7.8	7.13636363636364	0.663636363636363
99	7.8	8.15	-0.350000000000001
100	8	8.15	-0.15
101	7.8	8.15	-0.350000000000001
102	7.4	7.13636363636364	0.263636363636364
103	7.2	7.13636363636364	0.0636363636363635
104	7	7.13636363636364	-0.136363636363637
105	7	7.13636363636364	-0.136363636363637
106	7.2	7.1875	0.0125000000000002
107	7.2	7.1875	0.0125000000000002
108	7.2	7.1875	0.0125000000000002
109	7	7.1875	-0.1875
110	6.9	7.1875	-0.2875
111	6.8	7.13636363636364	-0.336363636363637
112	6.8	7.13636363636364	-0.336363636363637
113	6.8	7.1875	-0.3875
114	6.9	7.13636363636364	-0.236363636363636
115	7.2	7.13636363636364	0.0636363636363635
116	7.2	7.13636363636364	0.0636363636363635
117	7.2	7.13636363636364	0.0636363636363635
118	7.1	7.1875	-0.0875000000000004
119	7.2	7.1875	0.0125000000000002
120	7.3	7.1875	0.1125
121	7.5	7.1875	0.3125
122	7.6	7.1875	0.4125
123	7.7	7.73157894736842	-0.0315789473684207
124	7.7	7.73157894736842	-0.0315789473684207
125	7.7	7.73157894736842	-0.0315789473684207
126	7.8	8.15	-0.350000000000001
127	8	8.15	-0.15
128	8.1	8.15	-0.0500000000000007
129	8.1	8.15	-0.0500000000000007
130	8	8.15	-0.15
131	8.1	8.15	-0.0500000000000007
132	8.2	8.15	0.0499999999999989
133	8.3	8.15	0.15
134	8.4	8.15	0.25
135	8.4	8.15	0.25
136	8.4	8.15	0.25
137	8.5	8.15	0.35
138	8.5	8.15	0.35
139	8.6	8.47272727272727	0.127272727272727
140	8.6	8.47272727272727	0.127272727272727
141	8.5	8.47272727272727	0.0272727272727273
142	8.5	8.50588235294118	-0.00588235294117645

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 9.1 & 7.73157894736842 & 1.36842105263158 \tabularnewline
2 & 9 & 8.78571428571429 & 0.214285714285714 \tabularnewline
3 & 9 & 8.78571428571429 & 0.214285714285714 \tabularnewline
4 & 8.9 & 8.78571428571429 & 0.114285714285714 \tabularnewline
5 & 8.8 & 8.78571428571429 & 0.0142857142857142 \tabularnewline
6 & 8.7 & 8.78571428571429 & -0.0857142857142872 \tabularnewline
7 & 8.5 & 8.27 & 0.23 \tabularnewline
8 & 8.3 & 8.27 & 0.0300000000000011 \tabularnewline
9 & 8.1 & 8.27 & -0.17 \tabularnewline
10 & 7.9 & 8.27 & -0.369999999999999 \tabularnewline
11 & 7.8 & 7.73157894736842 & 0.0684210526315789 \tabularnewline
12 & 7.6 & 7.1875 & 0.4125 \tabularnewline
13 & 7.4 & 7.73157894736842 & -0.331578947368421 \tabularnewline
14 & 7.2 & 6.70952380952381 & 0.490476190476191 \tabularnewline
15 & 7 & 6.70952380952381 & 0.29047619047619 \tabularnewline
16 & 7 & 6.70952380952381 & 0.29047619047619 \tabularnewline
17 & 6.8 & 6.70952380952381 & 0.0904761904761902 \tabularnewline
18 & 6.8 & 6.70952380952381 & 0.0904761904761902 \tabularnewline
19 & 6.7 & 7.73157894736842 & -1.03157894736842 \tabularnewline
20 & 6.8 & 6.70952380952381 & 0.0904761904761902 \tabularnewline
21 & 6.7 & 6.70952380952381 & -0.0095238095238095 \tabularnewline
22 & 6.7 & 6.70952380952381 & -0.0095238095238095 \tabularnewline
23 & 6.7 & 6.70952380952381 & -0.0095238095238095 \tabularnewline
24 & 6.5 & 6.70952380952381 & -0.20952380952381 \tabularnewline
25 & 6.3 & 6.70952380952381 & -0.40952380952381 \tabularnewline
26 & 6.3 & 6.70952380952381 & -0.40952380952381 \tabularnewline
27 & 6.3 & 6.70952380952381 & -0.40952380952381 \tabularnewline
28 & 6.5 & 6.70952380952381 & -0.20952380952381 \tabularnewline
29 & 6.6 & 6.70952380952381 & -0.10952380952381 \tabularnewline
30 & 6.5 & 6.70952380952381 & -0.20952380952381 \tabularnewline
31 & 6.3 & 6.70952380952381 & -0.40952380952381 \tabularnewline
32 & 6.3 & 7.1875 & -0.8875 \tabularnewline
33 & 6.5 & 6.70952380952381 & -0.20952380952381 \tabularnewline
34 & 7 & 6.70952380952381 & 0.29047619047619 \tabularnewline
35 & 7.1 & 6.70952380952381 & 0.39047619047619 \tabularnewline
36 & 7.3 & 6.70952380952381 & 0.59047619047619 \tabularnewline
37 & 7.3 & 7.1875 & 0.1125 \tabularnewline
38 & 7.4 & 7.1875 & 0.2125 \tabularnewline
39 & 7.4 & 7.1875 & 0.2125 \tabularnewline
40 & 7.3 & 7.73157894736842 & -0.431578947368421 \tabularnewline
41 & 7.4 & 7.73157894736842 & -0.331578947368421 \tabularnewline
42 & 7.5 & 7.73157894736842 & -0.231578947368421 \tabularnewline
43 & 7.7 & 7.73157894736842 & -0.0315789473684207 \tabularnewline
44 & 7.7 & 7.73157894736842 & -0.0315789473684207 \tabularnewline
45 & 7.7 & 7.73157894736842 & -0.0315789473684207 \tabularnewline
46 & 7.7 & 7.73157894736842 & -0.0315789473684207 \tabularnewline
47 & 7.7 & 7.73157894736842 & -0.0315789473684207 \tabularnewline
48 & 7.8 & 7.73157894736842 & 0.0684210526315789 \tabularnewline
49 & 8 & 7.73157894736842 & 0.268421052631579 \tabularnewline
50 & 8.1 & 8.06428571428571 & 0.0357142857142847 \tabularnewline
51 & 8.1 & 7.73157894736842 & 0.368421052631579 \tabularnewline
52 & 8.2 & 7.73157894736842 & 0.468421052631578 \tabularnewline
53 & 8.2 & 8.27 & -0.0700000000000003 \tabularnewline
54 & 8.2 & 8.27 & -0.0700000000000003 \tabularnewline
55 & 8.1 & 8.27 & -0.17 \tabularnewline
56 & 8.1 & 8.06428571428571 & 0.0357142857142847 \tabularnewline
57 & 8.2 & 8.06428571428571 & 0.135714285714284 \tabularnewline
58 & 8.3 & 8.47272727272727 & -0.172727272727272 \tabularnewline
59 & 8.3 & 8.06428571428571 & 0.235714285714286 \tabularnewline
60 & 8.4 & 8.06428571428571 & 0.335714285714285 \tabularnewline
61 & 8.5 & 8.27 & 0.23 \tabularnewline
62 & 8.5 & 8.27 & 0.23 \tabularnewline
63 & 8.4 & 8.27 & 0.130000000000001 \tabularnewline
64 & 8 & 8.06428571428571 & -0.064285714285715 \tabularnewline
65 & 7.9 & 8.06428571428571 & -0.164285714285715 \tabularnewline
66 & 8.1 & 8.06428571428571 & 0.0357142857142847 \tabularnewline
67 & 8.5 & 8.50588235294118 & -0.00588235294117645 \tabularnewline
68 & 8.8 & 8.50588235294118 & 0.294117647058824 \tabularnewline
69 & 8.8 & 8.78571428571429 & 0.0142857142857142 \tabularnewline
70 & 8.6 & 8.50588235294118 & 0.0941176470588232 \tabularnewline
71 & 8.3 & 8.50588235294118 & -0.205882352941176 \tabularnewline
72 & 8.3 & 8.78571428571429 & -0.485714285714286 \tabularnewline
73 & 8.3 & 8.50588235294118 & -0.205882352941176 \tabularnewline
74 & 8.4 & 8.50588235294118 & -0.105882352941176 \tabularnewline
75 & 8.4 & 8.50588235294118 & -0.105882352941176 \tabularnewline
76 & 8.5 & 8.50588235294118 & -0.00588235294117645 \tabularnewline
77 & 8.6 & 8.50588235294118 & 0.0941176470588232 \tabularnewline
78 & 8.6 & 8.50588235294118 & 0.0941176470588232 \tabularnewline
79 & 8.6 & 8.50588235294118 & 0.0941176470588232 \tabularnewline
80 & 8.6 & 8.50588235294118 & 0.0941176470588232 \tabularnewline
81 & 8.6 & 8.50588235294118 & 0.0941176470588232 \tabularnewline
82 & 8.5 & 8.50588235294118 & -0.00588235294117645 \tabularnewline
83 & 8.4 & 8.50588235294118 & -0.105882352941176 \tabularnewline
84 & 8.4 & 8.50588235294118 & -0.105882352941176 \tabularnewline
85 & 8.4 & 8.47272727272727 & -0.0727272727272723 \tabularnewline
86 & 8.5 & 8.47272727272727 & 0.0272727272727273 \tabularnewline
87 & 8.5 & 8.47272727272727 & 0.0272727272727273 \tabularnewline
88 & 8.6 & 8.47272727272727 & 0.127272727272727 \tabularnewline
89 & 8.6 & 8.47272727272727 & 0.127272727272727 \tabularnewline
90 & 8.4 & 8.47272727272727 & -0.0727272727272723 \tabularnewline
91 & 8.2 & 8.47272727272727 & -0.272727272727273 \tabularnewline
92 & 8 & 8.06428571428571 & -0.064285714285715 \tabularnewline
93 & 8 & 8.06428571428571 & -0.064285714285715 \tabularnewline
94 & 8 & 8.06428571428571 & -0.064285714285715 \tabularnewline
95 & 8 & 8.06428571428571 & -0.064285714285715 \tabularnewline
96 & 7.9 & 8.06428571428571 & -0.164285714285715 \tabularnewline
97 & 7.9 & 8.06428571428571 & -0.164285714285715 \tabularnewline
98 & 7.8 & 7.13636363636364 & 0.663636363636363 \tabularnewline
99 & 7.8 & 8.15 & -0.350000000000001 \tabularnewline
100 & 8 & 8.15 & -0.15 \tabularnewline
101 & 7.8 & 8.15 & -0.350000000000001 \tabularnewline
102 & 7.4 & 7.13636363636364 & 0.263636363636364 \tabularnewline
103 & 7.2 & 7.13636363636364 & 0.0636363636363635 \tabularnewline
104 & 7 & 7.13636363636364 & -0.136363636363637 \tabularnewline
105 & 7 & 7.13636363636364 & -0.136363636363637 \tabularnewline
106 & 7.2 & 7.1875 & 0.0125000000000002 \tabularnewline
107 & 7.2 & 7.1875 & 0.0125000000000002 \tabularnewline
108 & 7.2 & 7.1875 & 0.0125000000000002 \tabularnewline
109 & 7 & 7.1875 & -0.1875 \tabularnewline
110 & 6.9 & 7.1875 & -0.2875 \tabularnewline
111 & 6.8 & 7.13636363636364 & -0.336363636363637 \tabularnewline
112 & 6.8 & 7.13636363636364 & -0.336363636363637 \tabularnewline
113 & 6.8 & 7.1875 & -0.3875 \tabularnewline
114 & 6.9 & 7.13636363636364 & -0.236363636363636 \tabularnewline
115 & 7.2 & 7.13636363636364 & 0.0636363636363635 \tabularnewline
116 & 7.2 & 7.13636363636364 & 0.0636363636363635 \tabularnewline
117 & 7.2 & 7.13636363636364 & 0.0636363636363635 \tabularnewline
118 & 7.1 & 7.1875 & -0.0875000000000004 \tabularnewline
119 & 7.2 & 7.1875 & 0.0125000000000002 \tabularnewline
120 & 7.3 & 7.1875 & 0.1125 \tabularnewline
121 & 7.5 & 7.1875 & 0.3125 \tabularnewline
122 & 7.6 & 7.1875 & 0.4125 \tabularnewline
123 & 7.7 & 7.73157894736842 & -0.0315789473684207 \tabularnewline
124 & 7.7 & 7.73157894736842 & -0.0315789473684207 \tabularnewline
125 & 7.7 & 7.73157894736842 & -0.0315789473684207 \tabularnewline
126 & 7.8 & 8.15 & -0.350000000000001 \tabularnewline
127 & 8 & 8.15 & -0.15 \tabularnewline
128 & 8.1 & 8.15 & -0.0500000000000007 \tabularnewline
129 & 8.1 & 8.15 & -0.0500000000000007 \tabularnewline
130 & 8 & 8.15 & -0.15 \tabularnewline
131 & 8.1 & 8.15 & -0.0500000000000007 \tabularnewline
132 & 8.2 & 8.15 & 0.0499999999999989 \tabularnewline
133 & 8.3 & 8.15 & 0.15 \tabularnewline
134 & 8.4 & 8.15 & 0.25 \tabularnewline
135 & 8.4 & 8.15 & 0.25 \tabularnewline
136 & 8.4 & 8.15 & 0.25 \tabularnewline
137 & 8.5 & 8.15 & 0.35 \tabularnewline
138 & 8.5 & 8.15 & 0.35 \tabularnewline
139 & 8.6 & 8.47272727272727 & 0.127272727272727 \tabularnewline
140 & 8.6 & 8.47272727272727 & 0.127272727272727 \tabularnewline
141 & 8.5 & 8.47272727272727 & 0.0272727272727273 \tabularnewline
142 & 8.5 & 8.50588235294118 & -0.00588235294117645 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114761&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]9.1[/C][C]7.73157894736842[/C][C]1.36842105263158[/C][/ROW]
[ROW][C]2[/C][C]9[/C][C]8.78571428571429[/C][C]0.214285714285714[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]8.78571428571429[/C][C]0.214285714285714[/C][/ROW]
[ROW][C]4[/C][C]8.9[/C][C]8.78571428571429[/C][C]0.114285714285714[/C][/ROW]
[ROW][C]5[/C][C]8.8[/C][C]8.78571428571429[/C][C]0.0142857142857142[/C][/ROW]
[ROW][C]6[/C][C]8.7[/C][C]8.78571428571429[/C][C]-0.0857142857142872[/C][/ROW]
[ROW][C]7[/C][C]8.5[/C][C]8.27[/C][C]0.23[/C][/ROW]
[ROW][C]8[/C][C]8.3[/C][C]8.27[/C][C]0.0300000000000011[/C][/ROW]
[ROW][C]9[/C][C]8.1[/C][C]8.27[/C][C]-0.17[/C][/ROW]
[ROW][C]10[/C][C]7.9[/C][C]8.27[/C][C]-0.369999999999999[/C][/ROW]
[ROW][C]11[/C][C]7.8[/C][C]7.73157894736842[/C][C]0.0684210526315789[/C][/ROW]
[ROW][C]12[/C][C]7.6[/C][C]7.1875[/C][C]0.4125[/C][/ROW]
[ROW][C]13[/C][C]7.4[/C][C]7.73157894736842[/C][C]-0.331578947368421[/C][/ROW]
[ROW][C]14[/C][C]7.2[/C][C]6.70952380952381[/C][C]0.490476190476191[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]6.70952380952381[/C][C]0.29047619047619[/C][/ROW]
[ROW][C]16[/C][C]7[/C][C]6.70952380952381[/C][C]0.29047619047619[/C][/ROW]
[ROW][C]17[/C][C]6.8[/C][C]6.70952380952381[/C][C]0.0904761904761902[/C][/ROW]
[ROW][C]18[/C][C]6.8[/C][C]6.70952380952381[/C][C]0.0904761904761902[/C][/ROW]
[ROW][C]19[/C][C]6.7[/C][C]7.73157894736842[/C][C]-1.03157894736842[/C][/ROW]
[ROW][C]20[/C][C]6.8[/C][C]6.70952380952381[/C][C]0.0904761904761902[/C][/ROW]
[ROW][C]21[/C][C]6.7[/C][C]6.70952380952381[/C][C]-0.0095238095238095[/C][/ROW]
[ROW][C]22[/C][C]6.7[/C][C]6.70952380952381[/C][C]-0.0095238095238095[/C][/ROW]
[ROW][C]23[/C][C]6.7[/C][C]6.70952380952381[/C][C]-0.0095238095238095[/C][/ROW]
[ROW][C]24[/C][C]6.5[/C][C]6.70952380952381[/C][C]-0.20952380952381[/C][/ROW]
[ROW][C]25[/C][C]6.3[/C][C]6.70952380952381[/C][C]-0.40952380952381[/C][/ROW]
[ROW][C]26[/C][C]6.3[/C][C]6.70952380952381[/C][C]-0.40952380952381[/C][/ROW]
[ROW][C]27[/C][C]6.3[/C][C]6.70952380952381[/C][C]-0.40952380952381[/C][/ROW]
[ROW][C]28[/C][C]6.5[/C][C]6.70952380952381[/C][C]-0.20952380952381[/C][/ROW]
[ROW][C]29[/C][C]6.6[/C][C]6.70952380952381[/C][C]-0.10952380952381[/C][/ROW]
[ROW][C]30[/C][C]6.5[/C][C]6.70952380952381[/C][C]-0.20952380952381[/C][/ROW]
[ROW][C]31[/C][C]6.3[/C][C]6.70952380952381[/C][C]-0.40952380952381[/C][/ROW]
[ROW][C]32[/C][C]6.3[/C][C]7.1875[/C][C]-0.8875[/C][/ROW]
[ROW][C]33[/C][C]6.5[/C][C]6.70952380952381[/C][C]-0.20952380952381[/C][/ROW]
[ROW][C]34[/C][C]7[/C][C]6.70952380952381[/C][C]0.29047619047619[/C][/ROW]
[ROW][C]35[/C][C]7.1[/C][C]6.70952380952381[/C][C]0.39047619047619[/C][/ROW]
[ROW][C]36[/C][C]7.3[/C][C]6.70952380952381[/C][C]0.59047619047619[/C][/ROW]
[ROW][C]37[/C][C]7.3[/C][C]7.1875[/C][C]0.1125[/C][/ROW]
[ROW][C]38[/C][C]7.4[/C][C]7.1875[/C][C]0.2125[/C][/ROW]
[ROW][C]39[/C][C]7.4[/C][C]7.1875[/C][C]0.2125[/C][/ROW]
[ROW][C]40[/C][C]7.3[/C][C]7.73157894736842[/C][C]-0.431578947368421[/C][/ROW]
[ROW][C]41[/C][C]7.4[/C][C]7.73157894736842[/C][C]-0.331578947368421[/C][/ROW]
[ROW][C]42[/C][C]7.5[/C][C]7.73157894736842[/C][C]-0.231578947368421[/C][/ROW]
[ROW][C]43[/C][C]7.7[/C][C]7.73157894736842[/C][C]-0.0315789473684207[/C][/ROW]
[ROW][C]44[/C][C]7.7[/C][C]7.73157894736842[/C][C]-0.0315789473684207[/C][/ROW]
[ROW][C]45[/C][C]7.7[/C][C]7.73157894736842[/C][C]-0.0315789473684207[/C][/ROW]
[ROW][C]46[/C][C]7.7[/C][C]7.73157894736842[/C][C]-0.0315789473684207[/C][/ROW]
[ROW][C]47[/C][C]7.7[/C][C]7.73157894736842[/C][C]-0.0315789473684207[/C][/ROW]
[ROW][C]48[/C][C]7.8[/C][C]7.73157894736842[/C][C]0.0684210526315789[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]7.73157894736842[/C][C]0.268421052631579[/C][/ROW]
[ROW][C]50[/C][C]8.1[/C][C]8.06428571428571[/C][C]0.0357142857142847[/C][/ROW]
[ROW][C]51[/C][C]8.1[/C][C]7.73157894736842[/C][C]0.368421052631579[/C][/ROW]
[ROW][C]52[/C][C]8.2[/C][C]7.73157894736842[/C][C]0.468421052631578[/C][/ROW]
[ROW][C]53[/C][C]8.2[/C][C]8.27[/C][C]-0.0700000000000003[/C][/ROW]
[ROW][C]54[/C][C]8.2[/C][C]8.27[/C][C]-0.0700000000000003[/C][/ROW]
[ROW][C]55[/C][C]8.1[/C][C]8.27[/C][C]-0.17[/C][/ROW]
[ROW][C]56[/C][C]8.1[/C][C]8.06428571428571[/C][C]0.0357142857142847[/C][/ROW]
[ROW][C]57[/C][C]8.2[/C][C]8.06428571428571[/C][C]0.135714285714284[/C][/ROW]
[ROW][C]58[/C][C]8.3[/C][C]8.47272727272727[/C][C]-0.172727272727272[/C][/ROW]
[ROW][C]59[/C][C]8.3[/C][C]8.06428571428571[/C][C]0.235714285714286[/C][/ROW]
[ROW][C]60[/C][C]8.4[/C][C]8.06428571428571[/C][C]0.335714285714285[/C][/ROW]
[ROW][C]61[/C][C]8.5[/C][C]8.27[/C][C]0.23[/C][/ROW]
[ROW][C]62[/C][C]8.5[/C][C]8.27[/C][C]0.23[/C][/ROW]
[ROW][C]63[/C][C]8.4[/C][C]8.27[/C][C]0.130000000000001[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]8.06428571428571[/C][C]-0.064285714285715[/C][/ROW]
[ROW][C]65[/C][C]7.9[/C][C]8.06428571428571[/C][C]-0.164285714285715[/C][/ROW]
[ROW][C]66[/C][C]8.1[/C][C]8.06428571428571[/C][C]0.0357142857142847[/C][/ROW]
[ROW][C]67[/C][C]8.5[/C][C]8.50588235294118[/C][C]-0.00588235294117645[/C][/ROW]
[ROW][C]68[/C][C]8.8[/C][C]8.50588235294118[/C][C]0.294117647058824[/C][/ROW]
[ROW][C]69[/C][C]8.8[/C][C]8.78571428571429[/C][C]0.0142857142857142[/C][/ROW]
[ROW][C]70[/C][C]8.6[/C][C]8.50588235294118[/C][C]0.0941176470588232[/C][/ROW]
[ROW][C]71[/C][C]8.3[/C][C]8.50588235294118[/C][C]-0.205882352941176[/C][/ROW]
[ROW][C]72[/C][C]8.3[/C][C]8.78571428571429[/C][C]-0.485714285714286[/C][/ROW]
[ROW][C]73[/C][C]8.3[/C][C]8.50588235294118[/C][C]-0.205882352941176[/C][/ROW]
[ROW][C]74[/C][C]8.4[/C][C]8.50588235294118[/C][C]-0.105882352941176[/C][/ROW]
[ROW][C]75[/C][C]8.4[/C][C]8.50588235294118[/C][C]-0.105882352941176[/C][/ROW]
[ROW][C]76[/C][C]8.5[/C][C]8.50588235294118[/C][C]-0.00588235294117645[/C][/ROW]
[ROW][C]77[/C][C]8.6[/C][C]8.50588235294118[/C][C]0.0941176470588232[/C][/ROW]
[ROW][C]78[/C][C]8.6[/C][C]8.50588235294118[/C][C]0.0941176470588232[/C][/ROW]
[ROW][C]79[/C][C]8.6[/C][C]8.50588235294118[/C][C]0.0941176470588232[/C][/ROW]
[ROW][C]80[/C][C]8.6[/C][C]8.50588235294118[/C][C]0.0941176470588232[/C][/ROW]
[ROW][C]81[/C][C]8.6[/C][C]8.50588235294118[/C][C]0.0941176470588232[/C][/ROW]
[ROW][C]82[/C][C]8.5[/C][C]8.50588235294118[/C][C]-0.00588235294117645[/C][/ROW]
[ROW][C]83[/C][C]8.4[/C][C]8.50588235294118[/C][C]-0.105882352941176[/C][/ROW]
[ROW][C]84[/C][C]8.4[/C][C]8.50588235294118[/C][C]-0.105882352941176[/C][/ROW]
[ROW][C]85[/C][C]8.4[/C][C]8.47272727272727[/C][C]-0.0727272727272723[/C][/ROW]
[ROW][C]86[/C][C]8.5[/C][C]8.47272727272727[/C][C]0.0272727272727273[/C][/ROW]
[ROW][C]87[/C][C]8.5[/C][C]8.47272727272727[/C][C]0.0272727272727273[/C][/ROW]
[ROW][C]88[/C][C]8.6[/C][C]8.47272727272727[/C][C]0.127272727272727[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]8.47272727272727[/C][C]0.127272727272727[/C][/ROW]
[ROW][C]90[/C][C]8.4[/C][C]8.47272727272727[/C][C]-0.0727272727272723[/C][/ROW]
[ROW][C]91[/C][C]8.2[/C][C]8.47272727272727[/C][C]-0.272727272727273[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]8.06428571428571[/C][C]-0.064285714285715[/C][/ROW]
[ROW][C]93[/C][C]8[/C][C]8.06428571428571[/C][C]-0.064285714285715[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]8.06428571428571[/C][C]-0.064285714285715[/C][/ROW]
[ROW][C]95[/C][C]8[/C][C]8.06428571428571[/C][C]-0.064285714285715[/C][/ROW]
[ROW][C]96[/C][C]7.9[/C][C]8.06428571428571[/C][C]-0.164285714285715[/C][/ROW]
[ROW][C]97[/C][C]7.9[/C][C]8.06428571428571[/C][C]-0.164285714285715[/C][/ROW]
[ROW][C]98[/C][C]7.8[/C][C]7.13636363636364[/C][C]0.663636363636363[/C][/ROW]
[ROW][C]99[/C][C]7.8[/C][C]8.15[/C][C]-0.350000000000001[/C][/ROW]
[ROW][C]100[/C][C]8[/C][C]8.15[/C][C]-0.15[/C][/ROW]
[ROW][C]101[/C][C]7.8[/C][C]8.15[/C][C]-0.350000000000001[/C][/ROW]
[ROW][C]102[/C][C]7.4[/C][C]7.13636363636364[/C][C]0.263636363636364[/C][/ROW]
[ROW][C]103[/C][C]7.2[/C][C]7.13636363636364[/C][C]0.0636363636363635[/C][/ROW]
[ROW][C]104[/C][C]7[/C][C]7.13636363636364[/C][C]-0.136363636363637[/C][/ROW]
[ROW][C]105[/C][C]7[/C][C]7.13636363636364[/C][C]-0.136363636363637[/C][/ROW]
[ROW][C]106[/C][C]7.2[/C][C]7.1875[/C][C]0.0125000000000002[/C][/ROW]
[ROW][C]107[/C][C]7.2[/C][C]7.1875[/C][C]0.0125000000000002[/C][/ROW]
[ROW][C]108[/C][C]7.2[/C][C]7.1875[/C][C]0.0125000000000002[/C][/ROW]
[ROW][C]109[/C][C]7[/C][C]7.1875[/C][C]-0.1875[/C][/ROW]
[ROW][C]110[/C][C]6.9[/C][C]7.1875[/C][C]-0.2875[/C][/ROW]
[ROW][C]111[/C][C]6.8[/C][C]7.13636363636364[/C][C]-0.336363636363637[/C][/ROW]
[ROW][C]112[/C][C]6.8[/C][C]7.13636363636364[/C][C]-0.336363636363637[/C][/ROW]
[ROW][C]113[/C][C]6.8[/C][C]7.1875[/C][C]-0.3875[/C][/ROW]
[ROW][C]114[/C][C]6.9[/C][C]7.13636363636364[/C][C]-0.236363636363636[/C][/ROW]
[ROW][C]115[/C][C]7.2[/C][C]7.13636363636364[/C][C]0.0636363636363635[/C][/ROW]
[ROW][C]116[/C][C]7.2[/C][C]7.13636363636364[/C][C]0.0636363636363635[/C][/ROW]
[ROW][C]117[/C][C]7.2[/C][C]7.13636363636364[/C][C]0.0636363636363635[/C][/ROW]
[ROW][C]118[/C][C]7.1[/C][C]7.1875[/C][C]-0.0875000000000004[/C][/ROW]
[ROW][C]119[/C][C]7.2[/C][C]7.1875[/C][C]0.0125000000000002[/C][/ROW]
[ROW][C]120[/C][C]7.3[/C][C]7.1875[/C][C]0.1125[/C][/ROW]
[ROW][C]121[/C][C]7.5[/C][C]7.1875[/C][C]0.3125[/C][/ROW]
[ROW][C]122[/C][C]7.6[/C][C]7.1875[/C][C]0.4125[/C][/ROW]
[ROW][C]123[/C][C]7.7[/C][C]7.73157894736842[/C][C]-0.0315789473684207[/C][/ROW]
[ROW][C]124[/C][C]7.7[/C][C]7.73157894736842[/C][C]-0.0315789473684207[/C][/ROW]
[ROW][C]125[/C][C]7.7[/C][C]7.73157894736842[/C][C]-0.0315789473684207[/C][/ROW]
[ROW][C]126[/C][C]7.8[/C][C]8.15[/C][C]-0.350000000000001[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]8.15[/C][C]-0.15[/C][/ROW]
[ROW][C]128[/C][C]8.1[/C][C]8.15[/C][C]-0.0500000000000007[/C][/ROW]
[ROW][C]129[/C][C]8.1[/C][C]8.15[/C][C]-0.0500000000000007[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]8.15[/C][C]-0.15[/C][/ROW]
[ROW][C]131[/C][C]8.1[/C][C]8.15[/C][C]-0.0500000000000007[/C][/ROW]
[ROW][C]132[/C][C]8.2[/C][C]8.15[/C][C]0.0499999999999989[/C][/ROW]
[ROW][C]133[/C][C]8.3[/C][C]8.15[/C][C]0.15[/C][/ROW]
[ROW][C]134[/C][C]8.4[/C][C]8.15[/C][C]0.25[/C][/ROW]
[ROW][C]135[/C][C]8.4[/C][C]8.15[/C][C]0.25[/C][/ROW]
[ROW][C]136[/C][C]8.4[/C][C]8.15[/C][C]0.25[/C][/ROW]
[ROW][C]137[/C][C]8.5[/C][C]8.15[/C][C]0.35[/C][/ROW]
[ROW][C]138[/C][C]8.5[/C][C]8.15[/C][C]0.35[/C][/ROW]
[ROW][C]139[/C][C]8.6[/C][C]8.47272727272727[/C][C]0.127272727272727[/C][/ROW]
[ROW][C]140[/C][C]8.6[/C][C]8.47272727272727[/C][C]0.127272727272727[/C][/ROW]
[ROW][C]141[/C][C]8.5[/C][C]8.47272727272727[/C][C]0.0272727272727273[/C][/ROW]
[ROW][C]142[/C][C]8.5[/C][C]8.50588235294118[/C][C]-0.00588235294117645[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114761&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114761&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	9.1	7.73157894736842	1.36842105263158
2	9	8.78571428571429	0.214285714285714
3	9	8.78571428571429	0.214285714285714
4	8.9	8.78571428571429	0.114285714285714
5	8.8	8.78571428571429	0.0142857142857142
6	8.7	8.78571428571429	-0.0857142857142872
7	8.5	8.27	0.23
8	8.3	8.27	0.0300000000000011
9	8.1	8.27	-0.17
10	7.9	8.27	-0.369999999999999
11	7.8	7.73157894736842	0.0684210526315789
12	7.6	7.1875	0.4125
13	7.4	7.73157894736842	-0.331578947368421
14	7.2	6.70952380952381	0.490476190476191
15	7	6.70952380952381	0.29047619047619
16	7	6.70952380952381	0.29047619047619
17	6.8	6.70952380952381	0.0904761904761902
18	6.8	6.70952380952381	0.0904761904761902
19	6.7	7.73157894736842	-1.03157894736842
20	6.8	6.70952380952381	0.0904761904761902
21	6.7	6.70952380952381	-0.0095238095238095
22	6.7	6.70952380952381	-0.0095238095238095
23	6.7	6.70952380952381	-0.0095238095238095
24	6.5	6.70952380952381	-0.20952380952381
25	6.3	6.70952380952381	-0.40952380952381
26	6.3	6.70952380952381	-0.40952380952381
27	6.3	6.70952380952381	-0.40952380952381
28	6.5	6.70952380952381	-0.20952380952381
29	6.6	6.70952380952381	-0.10952380952381
30	6.5	6.70952380952381	-0.20952380952381
31	6.3	6.70952380952381	-0.40952380952381
32	6.3	7.1875	-0.8875
33	6.5	6.70952380952381	-0.20952380952381
34	7	6.70952380952381	0.29047619047619
35	7.1	6.70952380952381	0.39047619047619
36	7.3	6.70952380952381	0.59047619047619
37	7.3	7.1875	0.1125
38	7.4	7.1875	0.2125
39	7.4	7.1875	0.2125
40	7.3	7.73157894736842	-0.431578947368421
41	7.4	7.73157894736842	-0.331578947368421
42	7.5	7.73157894736842	-0.231578947368421
43	7.7	7.73157894736842	-0.0315789473684207
44	7.7	7.73157894736842	-0.0315789473684207
45	7.7	7.73157894736842	-0.0315789473684207
46	7.7	7.73157894736842	-0.0315789473684207
47	7.7	7.73157894736842	-0.0315789473684207
48	7.8	7.73157894736842	0.0684210526315789
49	8	7.73157894736842	0.268421052631579
50	8.1	8.06428571428571	0.0357142857142847
51	8.1	7.73157894736842	0.368421052631579
52	8.2	7.73157894736842	0.468421052631578
53	8.2	8.27	-0.0700000000000003
54	8.2	8.27	-0.0700000000000003
55	8.1	8.27	-0.17
56	8.1	8.06428571428571	0.0357142857142847
57	8.2	8.06428571428571	0.135714285714284
58	8.3	8.47272727272727	-0.172727272727272
59	8.3	8.06428571428571	0.235714285714286
60	8.4	8.06428571428571	0.335714285714285
61	8.5	8.27	0.23
62	8.5	8.27	0.23
63	8.4	8.27	0.130000000000001
64	8	8.06428571428571	-0.064285714285715
65	7.9	8.06428571428571	-0.164285714285715
66	8.1	8.06428571428571	0.0357142857142847
67	8.5	8.50588235294118	-0.00588235294117645
68	8.8	8.50588235294118	0.294117647058824
69	8.8	8.78571428571429	0.0142857142857142
70	8.6	8.50588235294118	0.0941176470588232
71	8.3	8.50588235294118	-0.205882352941176
72	8.3	8.78571428571429	-0.485714285714286
73	8.3	8.50588235294118	-0.205882352941176
74	8.4	8.50588235294118	-0.105882352941176
75	8.4	8.50588235294118	-0.105882352941176
76	8.5	8.50588235294118	-0.00588235294117645
77	8.6	8.50588235294118	0.0941176470588232
78	8.6	8.50588235294118	0.0941176470588232
79	8.6	8.50588235294118	0.0941176470588232
80	8.6	8.50588235294118	0.0941176470588232
81	8.6	8.50588235294118	0.0941176470588232
82	8.5	8.50588235294118	-0.00588235294117645
83	8.4	8.50588235294118	-0.105882352941176
84	8.4	8.50588235294118	-0.105882352941176
85	8.4	8.47272727272727	-0.0727272727272723
86	8.5	8.47272727272727	0.0272727272727273
87	8.5	8.47272727272727	0.0272727272727273
88	8.6	8.47272727272727	0.127272727272727
89	8.6	8.47272727272727	0.127272727272727
90	8.4	8.47272727272727	-0.0727272727272723
91	8.2	8.47272727272727	-0.272727272727273
92	8	8.06428571428571	-0.064285714285715
93	8	8.06428571428571	-0.064285714285715
94	8	8.06428571428571	-0.064285714285715
95	8	8.06428571428571	-0.064285714285715
96	7.9	8.06428571428571	-0.164285714285715
97	7.9	8.06428571428571	-0.164285714285715
98	7.8	7.13636363636364	0.663636363636363
99	7.8	8.15	-0.350000000000001
100	8	8.15	-0.15
101	7.8	8.15	-0.350000000000001
102	7.4	7.13636363636364	0.263636363636364
103	7.2	7.13636363636364	0.0636363636363635
104	7	7.13636363636364	-0.136363636363637
105	7	7.13636363636364	-0.136363636363637
106	7.2	7.1875	0.0125000000000002
107	7.2	7.1875	0.0125000000000002
108	7.2	7.1875	0.0125000000000002
109	7	7.1875	-0.1875
110	6.9	7.1875	-0.2875
111	6.8	7.13636363636364	-0.336363636363637
112	6.8	7.13636363636364	-0.336363636363637
113	6.8	7.1875	-0.3875
114	6.9	7.13636363636364	-0.236363636363636
115	7.2	7.13636363636364	0.0636363636363635
116	7.2	7.13636363636364	0.0636363636363635
117	7.2	7.13636363636364	0.0636363636363635
118	7.1	7.1875	-0.0875000000000004
119	7.2	7.1875	0.0125000000000002
120	7.3	7.1875	0.1125
121	7.5	7.1875	0.3125
122	7.6	7.1875	0.4125
123	7.7	7.73157894736842	-0.0315789473684207
124	7.7	7.73157894736842	-0.0315789473684207
125	7.7	7.73157894736842	-0.0315789473684207
126	7.8	8.15	-0.350000000000001
127	8	8.15	-0.15
128	8.1	8.15	-0.0500000000000007
129	8.1	8.15	-0.0500000000000007
130	8	8.15	-0.15
131	8.1	8.15	-0.0500000000000007
132	8.2	8.15	0.0499999999999989
133	8.3	8.15	0.15
134	8.4	8.15	0.25
135	8.4	8.15	0.25
136	8.4	8.15	0.25
137	8.5	8.15	0.35
138	8.5	8.15	0.35
139	8.6	8.47272727272727	0.127272727272727
140	8.6	8.47272727272727	0.127272727272727
141	8.5	8.47272727272727	0.0272727272727273
142	8.5	8.50588235294118	-0.00588235294117645

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 = 1 ; par2 = none ; par3 = 4 ; par4 = yes ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 4 ; par4 = yes ;

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