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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationTue, 21 Dec 2010 14:20:30 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t1292941694nb3u5y7gc347vaq.htm/, Retrieved Fri, 17 May 2024 22:05:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113613, Retrieved Fri, 17 May 2024 22:05:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [p_Stress_RP1] [2010-12-11 13:00:40] [19f9551d4d95750ef21e9f3cf8fe2131]
-   PD    [Recursive Partitioning (Regression Trees)] [PAPER BAEYENS (Re...] [2010-12-21 13:24:14] [e4076051fbfb461c886b1e223cd7862f]
-    D        [Recursive Partitioning (Regression Trees)] [PAPER BAEYENS (Re...] [2010-12-21 14:20:30] [2953e4eb3235e2fd3d6373a16d27c72f] [Current]
-               [Recursive Partitioning (Regression Trees)] [PAPER BAEYENS (Re...] [2010-12-21 14:53:27] [e4076051fbfb461c886b1e223cd7862f]
-               [Recursive Partitioning (Regression Trees)] [PAPER BAEYENS (Re...] [2010-12-21 14:56:32] [e4076051fbfb461c886b1e223cd7862f]
-               [Recursive Partitioning (Regression Trees)] [PAPER BAEYENS (Re...] [2010-12-21 14:58:24] [e4076051fbfb461c886b1e223cd7862f]
-               [Recursive Partitioning (Regression Trees)] [PAPER BAEYENS (Re...] [2010-12-21 15:00:45] [e4076051fbfb461c886b1e223cd7862f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5	13	14
3	12	18
0	15	11
7	12	12
4	10	16
1	12	18
6	15	14
3	9	14
12	12	15
0	11	15
5	11	17
6	11	19
6	15	10
6	7	16
2	11	18
1	11	14
5	10	14
7	14	17
3	10	14
3	6	16
3	11	18
7	15	11
8	11	14
6	12	12
3	14	17
5	15	9
5	9	16
10	13	14
2	13	15
6	16	11
4	13	16
6	12	13
8	14	17
4	11	15
5	9	14
10	16	16
6	12	9
7	10	15
4	13	17
10	16	13
4	14	15
3	15	16
3	5	16
3	8	12
3	11	12
7	16	11
15	17	15
0	9	15
0	9	17
4	13	13
5	10	16
5	6	14
2	12	11
3	8	12
0	14	12
9	12	15
2	11	16
7	16	15
7	8	12
0	15	12
0	7	8
10	16	13
2	14	11
1	16	14
8	9	15
6	14	10
11	11	11
3	13	12
8	15	15
6	5	15
9	15	14
9	13	16
8	11	15
8	11	15
7	12	13
6	12	12
5	12	17
4	12	13
6	14	15
3	6	13
2	7	15
12	14	16
8	14	15
5	10	16
9	13	15
6	12	14
5	9	15
2	12	14
4	16	13
7	10	7
5	14	17
6	10	13
7	16	15
8	15	14
6	12	13
0	10	16
1	8	12
5	8	14
5	11	17
5	13	15
7	16	17
7	16	12
1	14	16
3	11	11
4	4	15
8	14	9
6	9	16
6	14	15
2	8	10
2	8	10
3	11	15
3	12	11
0	11	13
2	14	14
8	15	18
8	16	16
0	16	14
5	11	14
9	14	14
6	14	14
6	12	12
3	14	14
9	8	15
7	13	15
8	16	15
0	12	13
7	16	17
0	12	17
5	11	19
0	4	15
14	16	13
5	15	9
2	10	15
8	13	15
4	15	15
2	12	16
6	14	11
3	7	14
5	19	11
9	12	15
3	12	13
3	13	15
0	15	16
10	8	14
4	12	15
2	10	16
3	8	16
10	10	11
7	15	12
0	16	9
6	13	16
8	16	13
0	9	16
4	14	12
10	14	9
5	12	13

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113613&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113613&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113613&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Goodness of Fit
Correlation0.3172
R-squared0.1006
RMSE2.7761

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.3172 \tabularnewline
R-squared & 0.1006 \tabularnewline
RMSE & 2.7761 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113613&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.3172[/C][/ROW]
[ROW][C]R-squared[/C][C]0.1006[/C][/ROW]
[ROW][C]RMSE[/C][C]2.7761[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113613&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113613&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Goodness of Fit
Correlation0.3172
R-squared0.1006
RMSE2.7761






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
11311.42592592592591.57407407407407
21211.42592592592590.574074074074074
31511.42592592592593.57407407407407
41213.4375-1.4375
51011.4259259259259-1.42592592592593
61211.42592592592590.574074074074074
71511.42592592592593.57407407407407
8911.4259259259259-2.42592592592593
91213.4375-1.4375
101111.4259259259259-0.425925925925926
111111.4259259259259-0.425925925925926
121111.4259259259259-0.425925925925926
131511.42592592592593.57407407407407
14711.4259259259259-4.42592592592593
151111.4259259259259-0.425925925925926
161111.4259259259259-0.425925925925926
171011.4259259259259-1.42592592592593
181413.43750.5625
191011.4259259259259-1.42592592592593
20611.4259259259259-5.42592592592593
211111.4259259259259-0.425925925925926
221513.43751.5625
231113.4375-2.4375
241211.42592592592590.574074074074074
251411.42592592592592.57407407407407
261511.42592592592593.57407407407407
27911.4259259259259-2.42592592592593
281313.4375-0.4375
291311.42592592592591.57407407407407
301611.42592592592594.57407407407407
311311.42592592592591.57407407407407
321211.42592592592590.574074074074074
331413.43750.5625
341111.4259259259259-0.425925925925926
35911.4259259259259-2.42592592592593
361613.43752.5625
371211.42592592592590.574074074074074
381013.4375-3.4375
391311.42592592592591.57407407407407
401613.43752.5625
411411.42592592592592.57407407407407
421511.42592592592593.57407407407407
43511.4259259259259-6.42592592592593
44811.4259259259259-3.42592592592593
451111.4259259259259-0.425925925925926
461613.43752.5625
471713.43753.5625
48911.4259259259259-2.42592592592593
49911.4259259259259-2.42592592592593
501311.42592592592591.57407407407407
511011.4259259259259-1.42592592592593
52611.4259259259259-5.42592592592593
531211.42592592592590.574074074074074
54811.4259259259259-3.42592592592593
551411.42592592592592.57407407407407
561213.4375-1.4375
571111.4259259259259-0.425925925925926
581613.43752.5625
59813.4375-5.4375
601511.42592592592593.57407407407407
61711.4259259259259-4.42592592592593
621613.43752.5625
631411.42592592592592.57407407407407
641611.42592592592594.57407407407407
65913.4375-4.4375
661411.42592592592592.57407407407407
671113.4375-2.4375
681311.42592592592591.57407407407407
691513.43751.5625
70511.4259259259259-6.42592592592593
711513.43751.5625
721313.4375-0.4375
731113.4375-2.4375
741113.4375-2.4375
751213.4375-1.4375
761211.42592592592590.574074074074074
771211.42592592592590.574074074074074
781211.42592592592590.574074074074074
791411.42592592592592.57407407407407
80611.4259259259259-5.42592592592593
81711.4259259259259-4.42592592592593
821413.43750.5625
831413.43750.5625
841011.4259259259259-1.42592592592593
851313.4375-0.4375
861211.42592592592590.574074074074074
87911.4259259259259-2.42592592592593
881211.42592592592590.574074074074074
891611.42592592592594.57407407407407
901013.4375-3.4375
911411.42592592592592.57407407407407
921011.4259259259259-1.42592592592593
931613.43752.5625
941513.43751.5625
951211.42592592592590.574074074074074
961011.4259259259259-1.42592592592593
97811.4259259259259-3.42592592592593
98811.4259259259259-3.42592592592593
991111.4259259259259-0.425925925925926
1001311.42592592592591.57407407407407
1011613.43752.5625
1021613.43752.5625
1031411.42592592592592.57407407407407
1041111.4259259259259-0.425925925925926
105411.4259259259259-7.42592592592593
1061413.43750.5625
107911.4259259259259-2.42592592592593
1081411.42592592592592.57407407407407
109811.4259259259259-3.42592592592593
110811.4259259259259-3.42592592592593
1111111.4259259259259-0.425925925925926
1121211.42592592592590.574074074074074
1131111.4259259259259-0.425925925925926
1141411.42592592592592.57407407407407
1151513.43751.5625
1161613.43752.5625
1171611.42592592592594.57407407407407
1181111.4259259259259-0.425925925925926
1191413.43750.5625
1201411.42592592592592.57407407407407
1211211.42592592592590.574074074074074
1221411.42592592592592.57407407407407
123813.4375-5.4375
1241313.4375-0.4375
1251613.43752.5625
1261211.42592592592590.574074074074074
1271613.43752.5625
1281211.42592592592590.574074074074074
1291111.4259259259259-0.425925925925926
130411.4259259259259-7.42592592592593
1311613.43752.5625
1321511.42592592592593.57407407407407
1331011.4259259259259-1.42592592592593
1341313.4375-0.4375
1351511.42592592592593.57407407407407
1361211.42592592592590.574074074074074
1371411.42592592592592.57407407407407
138711.4259259259259-4.42592592592593
1391911.42592592592597.57407407407407
1401213.4375-1.4375
1411211.42592592592590.574074074074074
1421311.42592592592591.57407407407407
1431511.42592592592593.57407407407407
144813.4375-5.4375
1451211.42592592592590.574074074074074
1461011.4259259259259-1.42592592592593
147811.4259259259259-3.42592592592593
1481013.4375-3.4375
1491513.43751.5625
1501611.42592592592594.57407407407407
1511311.42592592592591.57407407407407
1521613.43752.5625
153911.4259259259259-2.42592592592593
1541411.42592592592592.57407407407407
1551413.43750.5625
1561211.42592592592590.574074074074074

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 13 & 11.4259259259259 & 1.57407407407407 \tabularnewline
2 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
3 & 15 & 11.4259259259259 & 3.57407407407407 \tabularnewline
4 & 12 & 13.4375 & -1.4375 \tabularnewline
5 & 10 & 11.4259259259259 & -1.42592592592593 \tabularnewline
6 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
7 & 15 & 11.4259259259259 & 3.57407407407407 \tabularnewline
8 & 9 & 11.4259259259259 & -2.42592592592593 \tabularnewline
9 & 12 & 13.4375 & -1.4375 \tabularnewline
10 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
11 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
12 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
13 & 15 & 11.4259259259259 & 3.57407407407407 \tabularnewline
14 & 7 & 11.4259259259259 & -4.42592592592593 \tabularnewline
15 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
16 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
17 & 10 & 11.4259259259259 & -1.42592592592593 \tabularnewline
18 & 14 & 13.4375 & 0.5625 \tabularnewline
19 & 10 & 11.4259259259259 & -1.42592592592593 \tabularnewline
20 & 6 & 11.4259259259259 & -5.42592592592593 \tabularnewline
21 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
22 & 15 & 13.4375 & 1.5625 \tabularnewline
23 & 11 & 13.4375 & -2.4375 \tabularnewline
24 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
25 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
26 & 15 & 11.4259259259259 & 3.57407407407407 \tabularnewline
27 & 9 & 11.4259259259259 & -2.42592592592593 \tabularnewline
28 & 13 & 13.4375 & -0.4375 \tabularnewline
29 & 13 & 11.4259259259259 & 1.57407407407407 \tabularnewline
30 & 16 & 11.4259259259259 & 4.57407407407407 \tabularnewline
31 & 13 & 11.4259259259259 & 1.57407407407407 \tabularnewline
32 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
33 & 14 & 13.4375 & 0.5625 \tabularnewline
34 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
35 & 9 & 11.4259259259259 & -2.42592592592593 \tabularnewline
36 & 16 & 13.4375 & 2.5625 \tabularnewline
37 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
38 & 10 & 13.4375 & -3.4375 \tabularnewline
39 & 13 & 11.4259259259259 & 1.57407407407407 \tabularnewline
40 & 16 & 13.4375 & 2.5625 \tabularnewline
41 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
42 & 15 & 11.4259259259259 & 3.57407407407407 \tabularnewline
43 & 5 & 11.4259259259259 & -6.42592592592593 \tabularnewline
44 & 8 & 11.4259259259259 & -3.42592592592593 \tabularnewline
45 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
46 & 16 & 13.4375 & 2.5625 \tabularnewline
47 & 17 & 13.4375 & 3.5625 \tabularnewline
48 & 9 & 11.4259259259259 & -2.42592592592593 \tabularnewline
49 & 9 & 11.4259259259259 & -2.42592592592593 \tabularnewline
50 & 13 & 11.4259259259259 & 1.57407407407407 \tabularnewline
51 & 10 & 11.4259259259259 & -1.42592592592593 \tabularnewline
52 & 6 & 11.4259259259259 & -5.42592592592593 \tabularnewline
53 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
54 & 8 & 11.4259259259259 & -3.42592592592593 \tabularnewline
55 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
56 & 12 & 13.4375 & -1.4375 \tabularnewline
57 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
58 & 16 & 13.4375 & 2.5625 \tabularnewline
59 & 8 & 13.4375 & -5.4375 \tabularnewline
60 & 15 & 11.4259259259259 & 3.57407407407407 \tabularnewline
61 & 7 & 11.4259259259259 & -4.42592592592593 \tabularnewline
62 & 16 & 13.4375 & 2.5625 \tabularnewline
63 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
64 & 16 & 11.4259259259259 & 4.57407407407407 \tabularnewline
65 & 9 & 13.4375 & -4.4375 \tabularnewline
66 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
67 & 11 & 13.4375 & -2.4375 \tabularnewline
68 & 13 & 11.4259259259259 & 1.57407407407407 \tabularnewline
69 & 15 & 13.4375 & 1.5625 \tabularnewline
70 & 5 & 11.4259259259259 & -6.42592592592593 \tabularnewline
71 & 15 & 13.4375 & 1.5625 \tabularnewline
72 & 13 & 13.4375 & -0.4375 \tabularnewline
73 & 11 & 13.4375 & -2.4375 \tabularnewline
74 & 11 & 13.4375 & -2.4375 \tabularnewline
75 & 12 & 13.4375 & -1.4375 \tabularnewline
76 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
77 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
78 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
79 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
80 & 6 & 11.4259259259259 & -5.42592592592593 \tabularnewline
81 & 7 & 11.4259259259259 & -4.42592592592593 \tabularnewline
82 & 14 & 13.4375 & 0.5625 \tabularnewline
83 & 14 & 13.4375 & 0.5625 \tabularnewline
84 & 10 & 11.4259259259259 & -1.42592592592593 \tabularnewline
85 & 13 & 13.4375 & -0.4375 \tabularnewline
86 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
87 & 9 & 11.4259259259259 & -2.42592592592593 \tabularnewline
88 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
89 & 16 & 11.4259259259259 & 4.57407407407407 \tabularnewline
90 & 10 & 13.4375 & -3.4375 \tabularnewline
91 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
92 & 10 & 11.4259259259259 & -1.42592592592593 \tabularnewline
93 & 16 & 13.4375 & 2.5625 \tabularnewline
94 & 15 & 13.4375 & 1.5625 \tabularnewline
95 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
96 & 10 & 11.4259259259259 & -1.42592592592593 \tabularnewline
97 & 8 & 11.4259259259259 & -3.42592592592593 \tabularnewline
98 & 8 & 11.4259259259259 & -3.42592592592593 \tabularnewline
99 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
100 & 13 & 11.4259259259259 & 1.57407407407407 \tabularnewline
101 & 16 & 13.4375 & 2.5625 \tabularnewline
102 & 16 & 13.4375 & 2.5625 \tabularnewline
103 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
104 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
105 & 4 & 11.4259259259259 & -7.42592592592593 \tabularnewline
106 & 14 & 13.4375 & 0.5625 \tabularnewline
107 & 9 & 11.4259259259259 & -2.42592592592593 \tabularnewline
108 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
109 & 8 & 11.4259259259259 & -3.42592592592593 \tabularnewline
110 & 8 & 11.4259259259259 & -3.42592592592593 \tabularnewline
111 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
112 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
113 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
114 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
115 & 15 & 13.4375 & 1.5625 \tabularnewline
116 & 16 & 13.4375 & 2.5625 \tabularnewline
117 & 16 & 11.4259259259259 & 4.57407407407407 \tabularnewline
118 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
119 & 14 & 13.4375 & 0.5625 \tabularnewline
120 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
121 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
122 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
123 & 8 & 13.4375 & -5.4375 \tabularnewline
124 & 13 & 13.4375 & -0.4375 \tabularnewline
125 & 16 & 13.4375 & 2.5625 \tabularnewline
126 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
127 & 16 & 13.4375 & 2.5625 \tabularnewline
128 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
129 & 11 & 11.4259259259259 & -0.425925925925926 \tabularnewline
130 & 4 & 11.4259259259259 & -7.42592592592593 \tabularnewline
131 & 16 & 13.4375 & 2.5625 \tabularnewline
132 & 15 & 11.4259259259259 & 3.57407407407407 \tabularnewline
133 & 10 & 11.4259259259259 & -1.42592592592593 \tabularnewline
134 & 13 & 13.4375 & -0.4375 \tabularnewline
135 & 15 & 11.4259259259259 & 3.57407407407407 \tabularnewline
136 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
137 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
138 & 7 & 11.4259259259259 & -4.42592592592593 \tabularnewline
139 & 19 & 11.4259259259259 & 7.57407407407407 \tabularnewline
140 & 12 & 13.4375 & -1.4375 \tabularnewline
141 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
142 & 13 & 11.4259259259259 & 1.57407407407407 \tabularnewline
143 & 15 & 11.4259259259259 & 3.57407407407407 \tabularnewline
144 & 8 & 13.4375 & -5.4375 \tabularnewline
145 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
146 & 10 & 11.4259259259259 & -1.42592592592593 \tabularnewline
147 & 8 & 11.4259259259259 & -3.42592592592593 \tabularnewline
148 & 10 & 13.4375 & -3.4375 \tabularnewline
149 & 15 & 13.4375 & 1.5625 \tabularnewline
150 & 16 & 11.4259259259259 & 4.57407407407407 \tabularnewline
151 & 13 & 11.4259259259259 & 1.57407407407407 \tabularnewline
152 & 16 & 13.4375 & 2.5625 \tabularnewline
153 & 9 & 11.4259259259259 & -2.42592592592593 \tabularnewline
154 & 14 & 11.4259259259259 & 2.57407407407407 \tabularnewline
155 & 14 & 13.4375 & 0.5625 \tabularnewline
156 & 12 & 11.4259259259259 & 0.574074074074074 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113613&T=2

[TABLE]
[ROW][C]Actuals, Predictions, and Residuals[/C][/ROW]
[ROW][C]#[/C][C]Actuals[/C][C]Forecasts[/C][C]Residuals[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]11.4259259259259[/C][C]1.57407407407407[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]11.4259259259259[/C][C]3.57407407407407[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.4375[/C][C]-1.4375[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]11.4259259259259[/C][C]-1.42592592592593[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]11.4259259259259[/C][C]3.57407407407407[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]11.4259259259259[/C][C]-2.42592592592593[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]13.4375[/C][C]-1.4375[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]11.4259259259259[/C][C]3.57407407407407[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.4259259259259[/C][C]-4.42592592592593[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.4259259259259[/C][C]-1.42592592592593[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]13.4375[/C][C]0.5625[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]11.4259259259259[/C][C]-1.42592592592593[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]11.4259259259259[/C][C]-5.42592592592593[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]13.4375[/C][C]1.5625[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]13.4375[/C][C]-2.4375[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]11.4259259259259[/C][C]3.57407407407407[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]11.4259259259259[/C][C]-2.42592592592593[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]13.4375[/C][C]-0.4375[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]11.4259259259259[/C][C]1.57407407407407[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]11.4259259259259[/C][C]4.57407407407407[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]11.4259259259259[/C][C]1.57407407407407[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]13.4375[/C][C]0.5625[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]11.4259259259259[/C][C]-2.42592592592593[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]13.4375[/C][C]-3.4375[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]11.4259259259259[/C][C]1.57407407407407[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]11.4259259259259[/C][C]3.57407407407407[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]11.4259259259259[/C][C]-6.42592592592593[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]11.4259259259259[/C][C]-3.42592592592593[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]13.4375[/C][C]3.5625[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]11.4259259259259[/C][C]-2.42592592592593[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.4259259259259[/C][C]-2.42592592592593[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]11.4259259259259[/C][C]1.57407407407407[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.4259259259259[/C][C]-1.42592592592593[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]11.4259259259259[/C][C]-5.42592592592593[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]11.4259259259259[/C][C]-3.42592592592593[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]13.4375[/C][C]-1.4375[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]13.4375[/C][C]-5.4375[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]11.4259259259259[/C][C]3.57407407407407[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]11.4259259259259[/C][C]-4.42592592592593[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]11.4259259259259[/C][C]4.57407407407407[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]13.4375[/C][C]-4.4375[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.4375[/C][C]-2.4375[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]11.4259259259259[/C][C]1.57407407407407[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.4375[/C][C]1.5625[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]11.4259259259259[/C][C]-6.42592592592593[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]13.4375[/C][C]1.5625[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]13.4375[/C][C]-0.4375[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]13.4375[/C][C]-2.4375[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]13.4375[/C][C]-2.4375[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]13.4375[/C][C]-1.4375[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]11.4259259259259[/C][C]-5.42592592592593[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]11.4259259259259[/C][C]-4.42592592592593[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]13.4375[/C][C]0.5625[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]13.4375[/C][C]0.5625[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.4259259259259[/C][C]-1.42592592592593[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]13.4375[/C][C]-0.4375[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]11.4259259259259[/C][C]-2.42592592592593[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]11.4259259259259[/C][C]4.57407407407407[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]13.4375[/C][C]-3.4375[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]11.4259259259259[/C][C]-1.42592592592593[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.4375[/C][C]1.5625[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.4259259259259[/C][C]-1.42592592592593[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]11.4259259259259[/C][C]-3.42592592592593[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]11.4259259259259[/C][C]-3.42592592592593[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]11.4259259259259[/C][C]1.57407407407407[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]11.4259259259259[/C][C]-7.42592592592593[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]13.4375[/C][C]0.5625[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]11.4259259259259[/C][C]-2.42592592592593[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]11.4259259259259[/C][C]-3.42592592592593[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]11.4259259259259[/C][C]-3.42592592592593[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]13.4375[/C][C]1.5625[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]11.4259259259259[/C][C]4.57407407407407[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.4375[/C][C]0.5625[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]13.4375[/C][C]-5.4375[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]13.4375[/C][C]-0.4375[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.4259259259259[/C][C]-0.425925925925926[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]11.4259259259259[/C][C]-7.42592592592593[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]11.4259259259259[/C][C]3.57407407407407[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.4259259259259[/C][C]-1.42592592592593[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.4375[/C][C]-0.4375[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]11.4259259259259[/C][C]3.57407407407407[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]11.4259259259259[/C][C]-4.42592592592593[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]11.4259259259259[/C][C]7.57407407407407[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.4375[/C][C]-1.4375[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]11.4259259259259[/C][C]1.57407407407407[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]11.4259259259259[/C][C]3.57407407407407[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]13.4375[/C][C]-5.4375[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]11.4259259259259[/C][C]-1.42592592592593[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.4259259259259[/C][C]-3.42592592592593[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]13.4375[/C][C]-3.4375[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.4375[/C][C]1.5625[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]11.4259259259259[/C][C]4.57407407407407[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]11.4259259259259[/C][C]1.57407407407407[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.4375[/C][C]2.5625[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.4259259259259[/C][C]-2.42592592592593[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]11.4259259259259[/C][C]2.57407407407407[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.4375[/C][C]0.5625[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]11.4259259259259[/C][C]0.574074074074074[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113613&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113613&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
11311.42592592592591.57407407407407
21211.42592592592590.574074074074074
31511.42592592592593.57407407407407
41213.4375-1.4375
51011.4259259259259-1.42592592592593
61211.42592592592590.574074074074074
71511.42592592592593.57407407407407
8911.4259259259259-2.42592592592593
91213.4375-1.4375
101111.4259259259259-0.425925925925926
111111.4259259259259-0.425925925925926
121111.4259259259259-0.425925925925926
131511.42592592592593.57407407407407
14711.4259259259259-4.42592592592593
151111.4259259259259-0.425925925925926
161111.4259259259259-0.425925925925926
171011.4259259259259-1.42592592592593
181413.43750.5625
191011.4259259259259-1.42592592592593
20611.4259259259259-5.42592592592593
211111.4259259259259-0.425925925925926
221513.43751.5625
231113.4375-2.4375
241211.42592592592590.574074074074074
251411.42592592592592.57407407407407
261511.42592592592593.57407407407407
27911.4259259259259-2.42592592592593
281313.4375-0.4375
291311.42592592592591.57407407407407
301611.42592592592594.57407407407407
311311.42592592592591.57407407407407
321211.42592592592590.574074074074074
331413.43750.5625
341111.4259259259259-0.425925925925926
35911.4259259259259-2.42592592592593
361613.43752.5625
371211.42592592592590.574074074074074
381013.4375-3.4375
391311.42592592592591.57407407407407
401613.43752.5625
411411.42592592592592.57407407407407
421511.42592592592593.57407407407407
43511.4259259259259-6.42592592592593
44811.4259259259259-3.42592592592593
451111.4259259259259-0.425925925925926
461613.43752.5625
471713.43753.5625
48911.4259259259259-2.42592592592593
49911.4259259259259-2.42592592592593
501311.42592592592591.57407407407407
511011.4259259259259-1.42592592592593
52611.4259259259259-5.42592592592593
531211.42592592592590.574074074074074
54811.4259259259259-3.42592592592593
551411.42592592592592.57407407407407
561213.4375-1.4375
571111.4259259259259-0.425925925925926
581613.43752.5625
59813.4375-5.4375
601511.42592592592593.57407407407407
61711.4259259259259-4.42592592592593
621613.43752.5625
631411.42592592592592.57407407407407
641611.42592592592594.57407407407407
65913.4375-4.4375
661411.42592592592592.57407407407407
671113.4375-2.4375
681311.42592592592591.57407407407407
691513.43751.5625
70511.4259259259259-6.42592592592593
711513.43751.5625
721313.4375-0.4375
731113.4375-2.4375
741113.4375-2.4375
751213.4375-1.4375
761211.42592592592590.574074074074074
771211.42592592592590.574074074074074
781211.42592592592590.574074074074074
791411.42592592592592.57407407407407
80611.4259259259259-5.42592592592593
81711.4259259259259-4.42592592592593
821413.43750.5625
831413.43750.5625
841011.4259259259259-1.42592592592593
851313.4375-0.4375
861211.42592592592590.574074074074074
87911.4259259259259-2.42592592592593
881211.42592592592590.574074074074074
891611.42592592592594.57407407407407
901013.4375-3.4375
911411.42592592592592.57407407407407
921011.4259259259259-1.42592592592593
931613.43752.5625
941513.43751.5625
951211.42592592592590.574074074074074
961011.4259259259259-1.42592592592593
97811.4259259259259-3.42592592592593
98811.4259259259259-3.42592592592593
991111.4259259259259-0.425925925925926
1001311.42592592592591.57407407407407
1011613.43752.5625
1021613.43752.5625
1031411.42592592592592.57407407407407
1041111.4259259259259-0.425925925925926
105411.4259259259259-7.42592592592593
1061413.43750.5625
107911.4259259259259-2.42592592592593
1081411.42592592592592.57407407407407
109811.4259259259259-3.42592592592593
110811.4259259259259-3.42592592592593
1111111.4259259259259-0.425925925925926
1121211.42592592592590.574074074074074
1131111.4259259259259-0.425925925925926
1141411.42592592592592.57407407407407
1151513.43751.5625
1161613.43752.5625
1171611.42592592592594.57407407407407
1181111.4259259259259-0.425925925925926
1191413.43750.5625
1201411.42592592592592.57407407407407
1211211.42592592592590.574074074074074
1221411.42592592592592.57407407407407
123813.4375-5.4375
1241313.4375-0.4375
1251613.43752.5625
1261211.42592592592590.574074074074074
1271613.43752.5625
1281211.42592592592590.574074074074074
1291111.4259259259259-0.425925925925926
130411.4259259259259-7.42592592592593
1311613.43752.5625
1321511.42592592592593.57407407407407
1331011.4259259259259-1.42592592592593
1341313.4375-0.4375
1351511.42592592592593.57407407407407
1361211.42592592592590.574074074074074
1371411.42592592592592.57407407407407
138711.4259259259259-4.42592592592593
1391911.42592592592597.57407407407407
1401213.4375-1.4375
1411211.42592592592590.574074074074074
1421311.42592592592591.57407407407407
1431511.42592592592593.57407407407407
144813.4375-5.4375
1451211.42592592592590.574074074074074
1461011.4259259259259-1.42592592592593
147811.4259259259259-3.42592592592593
1481013.4375-3.4375
1491513.43751.5625
1501611.42592592592594.57407407407407
1511311.42592592592591.57407407407407
1521613.43752.5625
153911.4259259259259-2.42592592592593
1541411.42592592592592.57407407407407
1551413.43750.5625
1561211.42592592592590.574074074074074


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;
R code (references can be found in the software module):
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}