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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 computationFri, 07 Dec 2012 08:36:53 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/07/t1354887497gq88h6kzwt6hbss.htm/, Retrieved Fri, 29 Mar 2024 02:28:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197363, Retrieved Fri, 29 Mar 2024 02:28:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD    [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2012-12-07 13:36:53] [7ac586d7aaad1f98cbd1d1bd98b37cf0] [Current]
-           [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2012-12-07 14:10:26] [3e2c7966ca4198d187b4c59e4eb5d004]
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Dataseries X:
2.7	8.4	4.3	1.5	2.2	2.1
2.5	7.5	3.1	1.7	2.3	2.2
2.2	4.0	5.7	1.6	2.1	2.2
2.9	8.5	6.7	1.7	2.8	2.7
3.1	7.6	9.5	1.8	3.1	3.1
3.0	5.5	9.0	1.7	2.9	3.2
2.8	3.3	6.9	2.2	2.6	3.1
2.5	1.4	7.5	2.7	2.7	3.1
1.9	-4.4	7.0	3.0	2.3	2.8
1.9	-6.5	9.3	2.8	2.3	3.0
1.8	-8.5	7.2	2.7	2.1	2.8
2.0	-6.7	6.6	2.7	2.2	2.7
2.6	-3.3	10.4	2.5	2.9	3.2
2.5	-5.1	8.7	2.0	2.6	3.1
2.5	-3.5	7.9	1.8	2.7	3.0
1.6	-3.6	4.1	1.4	1.8	2.0
1.4	-6.3	2.2	1.5	1.3	1.7
0.8	-8.0	-0.5	1.6	0.9	1.2
1.1	-5.3	1.7	1.3	1.3	1.4
1.3	-4.0	0.4	1.1	1.3	1.3
1.2	-4.0	2.6	0.8	1.3	1.3
1.3	0.1	0.7	1.1	1.3	1.1
1.1	-0.9	0.7	1.3	1.1	0.9
1.3	1.1	0.5	1.5	1.4	1.2
1.2	3.1	-2.3	1.8	1.2	0.9
1.6	5.7	0.3	2.7	1.7	1.3
1.7	6.2	-0.2	3.0	1.8	1.4
1.5	-2.2	0.6	3.2	1.5	1.5
0.9	-4.2	-0.6	3.2	1.0	1.1
1.5	-1.6	2.7	3.3	1.6	1.6
1.4	-1.9	2.3	3.2	1.5	1.5
1.6	0.2	4.3	2.9	1.8	1.6
1.7	-1.2	5.4	2.7	1.8	1.7
1.4	-2.4	2.6	2.6	1.6	1.6
1.8	0.8	2.9	2.3	1.9	1.7
1.7	-0.1	2.9	2.2	1.7	1.6
1.4	-1.5	2.9	2.1	1.6	1.6
1.2	-4.4	1.4	2.4	1.3	1.3
1.0	-4.2	1.1	2.5	1.1	1.1
1.7	3.5	1.9	2.4	1.9	1.6
2.4	10.0	2.8	2.3	2.6	1.9
2.0	8.6	1.4	2.1	2.3	1.6
2.1	9.5	0.7	2.3	2.4	1.7
2.0	9.9	-0.8	2.2	2.2	1.6
1.8	10.4	-3.1	2.1	2.0	1.4
2.7	16.0	0.1	2.0	2.9	2.1
2.3	12.7	1.0	2.1	2.6	1.9
1.9	10.2	1.9	2.1	2.3	1.7
2.0	8.9	-0.5	2.5	2.3	1.8
2.3	12.6	1.5	2.2	2.6	2.0
2.8	13.6	3.9	2.3	3.1	2.5
2.4	14.8	1.9	2.3	2.8	2.1
2.3	9.5	2.6	2.2	2.5	2.1
2.7	13.7	1.7	2.2	2.9	2.3
2.7	17.0	1.4	1.6	3.1	2.4
2.9	14.7	2.8	1.8	3.1	2.4
3.0	17.4	0.5	1.7	3.2	2.3
2.2	9.0	1.0	1.9	2.5	1.7
2.3	9.1	1.5	1.8	2.6	2.0
2.8	12.2	1.8	1.9	2.9	2.3
2.8	15.9	2.7	1.5	2.6	2.0
2.8	12.9	3.0	1.0	2.4	2.0
2.2	10.9	-0.3	0.8	1.7	1.3
2.6	10.6	1.1	1.1	2.0	1.7
2.8	13.2	1.7	1.5	2.2	1.9
2.5	9.6	1.6	1.7	1.9	1.7
2.4	6.4	3.0	2.3	1.6	1.6
2.3	5.8	3.3	2.4	1.6	1.7
1.9	-1.0	6.7	3.0	1.2	1.8
1.7	-0.2	5.6	3.0	1.2	1.9
2.0	2.7	6.0	3.2	1.5	1.9
2.1	3.6	4.8	3.2	1.6	1.9
1.7	-0.9	5.9	3.2	1.7	2.0
1.8	0.3	4.3	3.5	1.8	2.1
1.8	-1.1	3.7	4.0	1.8	1.9
1.8	-2.5	5.6	4.3	1.8	1.9
1.3	-3.4	1.7	4.1	1.3	1.3
1.3	-3.5	3.2	4.0	1.3	1.3
1.3	-3.9	3.6	4.1	1.4	1.4
1.2	-4.6	1.7	4.2	1.1	1.2
1.4	-0.1	0.5	4.5	1.5	1.3
2.2	4.3	2.1	5.6	2.2	1.8
2.9	10.2	1.5	6.5	2.9	2.2
3.1	8.7	2.7	7.6	3.1	2.6
3.5	13.3	1.4	8.5	3.5	2.8
3.6	15.0	1.2	8.7	3.6	3.1
4.4	20.7	2.3	8.3	4.4	3.9
4.1	20.7	1.6	8.3	4.2	3.7
5.1	26.4	4.7	8.5	5.2	4.6
5.8	31.2	3.5	8.7	5.8	5.1
5.9	31.4	4.4	8.7	5.9	5.2
5.4	26.6	3.9	8.5	5.4	4.9
5.5	26.6	3.5	7.9	5.5	5.1
4.8	19.2	3.0	7.0	4.7	4.8
3.2	6.5	1.6	5.8	3.1	3.9
2.7	3.1	2.2	4.5	2.6	3.5
2.1	-0.2	4.1	3.7	2.3	3.3
1.9	-4.0	4.3	3.1	1.9	2.8
0.6	-12.6	3.5	2.7	0.6	1.6
0.7	-13.0	1.8	2.3	0.6	1.5
-0.2	-17.6	0.6	1.8	-0.4	0.7
-1.0	-21.7	-0.4	1.5	-1.1	-0.1
-1.7	-23.2	-2.5	1.2	-1.7	-0.7
-0.7	-16.8	-1.6	1.0	-0.8	-0.2
-1.0	-19.8	-1.9	0.9	-1.2	-0.6
-0.9	-17.2	-1.6	0.6	-1.0	-0.6
0.0	-10.4	-0.7	0.6	-0.1	-0.3
0.3	-6.8	-1.1	0.7	0.3	-0.3
0.8	-2.9	0.3	0.5	0.6	-0.1
0.8	-1.9	1.3	0.5	0.7	0.1
1.9	7.0	3.3	0.5	1.7	0.9
2.1	9.8	2.4	0.5	1.8	1.1
2.5	12.5	2.0	0.8	2.3	1.6
2.7	13.7	3.9	0.8	2.5	2.0
2.4	13.7	4.2	1.1	2.6	2.2
2.4	9.7	4.9	1.2	2.3	2.1
2.9	14.0	5.8	1.5	2.9	2.6
3.1	15.3	4.8	1.7	3.0	2.5
3.0	13.4	4.4	1.8	2.9	2.5
3.4	17.1	5.3	1.8	3.1	2.6
3.7	15.7	2.1	2.1	3.2	2.7
3.5	18.3	2.0	2.2	3.4	2.8
3.5	18.1	-0.9	2.5	3.5	2.9
3.3	16.3	0.1	2.7	3.4	2.9
3.1	15.8	-0.5	3.0	3.3	2.9
3.4	17.3	-0.1	3.4	3.7	3.3
4.0	18.0	0.7	3.4	3.8	3.3
3.4	17.6	-0.4	3.5	3.6	3.1
3.4	18.4	-1.5	3.5	3.6	3.0
3.4	17.4	-0.3	3.4	3.6	3.1
3.7	17.9	1.0	3.6	3.8	3.4
3.2	13.5	0.4	3.8	3.5	3.2
3.3	13.7	0.3	3.5	3.6	3.4
3.3	12.6	1.8	3.5	3.7	3.4
3.1	10.4	3.0	3.5	3.4	3.1
2.9	8.8	2.2	3.2	3.2	3.0
2.6	5.4	3.4	2.9	2.8	2.7
2.2	2.1	3.4	2.5	2.3	2.2
2.0	2.8	3.1	2.3	2.3	2.2
2.6	5.6	4.5	2.7	2.9	2.6
2.6	4.8	4.6	3.0	2.8	2.4
2.6	4.5	5.7	3.3	2.8	2.5
2.2	1.5	4.3	3.2	2.3	2.2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197363&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197363&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197363&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Goodness of Fit
Correlation0.9694
R-squared0.9396
RMSE0.3005

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9694 \tabularnewline
R-squared & 0.9396 \tabularnewline
RMSE & 0.3005 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197363&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9694[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9396[/C][/ROW]
[ROW][C]RMSE[/C][C]0.3005[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197363&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197363&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
Correlation0.9694
R-squared0.9396
RMSE0.3005







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
12.72.173076923076920.526923076923077
22.52.173076923076920.326923076923077
32.22.173076923076920.0269230769230773
42.92.509523809523810.39047619047619
53.13.083333333333330.0166666666666666
632.80.2
72.82.509523809523810.29047619047619
82.52.50952380952381-0.00952380952380949
91.91.744444444444440.155555555555555
101.91.744444444444440.155555555555555
111.81.744444444444440.0555555555555556
1221.744444444444440.255555555555556
132.62.8-0.2
142.52.50952380952381-0.00952380952380949
152.52.50952380952381-0.00952380952380949
161.61.74444444444444-0.144444444444444
171.41.40
180.80.877777777777778-0.0777777777777777
191.11.4-0.3
201.31.4-0.0999999999999999
211.21.4-0.2
221.31.4-0.0999999999999999
231.10.8777777777777780.222222222222222
241.31.4-0.0999999999999999
251.21.4-0.2
261.62.17307692307692-0.573076923076923
271.72.17307692307692-0.473076923076923
281.51.40.1
290.90.8777777777777780.0222222222222223
301.51.74444444444444-0.244444444444444
311.41.40
321.61.74444444444444-0.144444444444444
331.71.74444444444444-0.0444444444444445
341.41.74444444444444-0.344444444444445
351.81.744444444444440.0555555555555556
361.71.74444444444444-0.0444444444444445
371.41.74444444444444-0.344444444444445
381.21.4-0.2
3910.8777777777777780.122222222222222
401.72.17307692307692-0.473076923076923
412.42.50952380952381-0.10952380952381
4222.17307692307692-0.173076923076923
432.12.50952380952381-0.409523809523809
4422.17307692307692-0.173076923076923
451.82.17307692307692-0.373076923076923
462.72.8-0.0999999999999996
472.32.50952380952381-0.20952380952381
481.92.17307692307692-0.273076923076923
4922.17307692307692-0.173076923076923
502.32.50952380952381-0.20952380952381
512.83.08333333333333-0.283333333333334
522.42.50952380952381-0.10952380952381
532.32.50952380952381-0.20952380952381
542.72.8-0.0999999999999996
552.73.08333333333333-0.383333333333333
562.93.08333333333333-0.183333333333334
5733.08333333333333-0.0833333333333335
582.22.50952380952381-0.309523809523809
592.32.50952380952381-0.20952380952381
602.82.80
612.82.509523809523810.29047619047619
622.82.509523809523810.29047619047619
632.22.173076923076920.0269230769230773
642.62.173076923076920.426923076923077
652.82.173076923076920.626923076923077
662.52.173076923076920.326923076923077
672.42.173076923076920.226923076923077
682.32.173076923076920.126923076923077
691.91.40.5
701.71.40.3
7121.40.6
722.12.17307692307692-0.0730769230769228
731.71.74444444444444-0.0444444444444445
741.81.744444444444440.0555555555555556
751.81.744444444444440.0555555555555556
761.81.744444444444440.0555555555555556
771.31.4-0.0999999999999999
781.31.4-0.0999999999999999
791.31.4-0.0999999999999999
801.20.8777777777777780.322222222222222
811.41.40
822.22.173076923076920.0269230769230773
832.92.80.1
843.13.083333333333330.0166666666666666
853.53.440.0600000000000001
863.63.440.16
874.45.125-0.725
884.15.125-1.025
895.15.125-0.0250000000000004
905.85.1250.675
915.95.1250.775
925.45.1250.275
935.55.1250.375
944.85.125-0.325
953.23.083333333333330.116666666666667
962.72.509523809523810.190476190476191
972.11.744444444444440.355555555555556
981.91.744444444444440.155555555555555
990.60.877777777777778-0.277777777777778
1000.70.877777777777778-0.177777777777778
101-0.2-0.650.45
102-1-0.65-0.35
103-1.7-0.65-1.05
104-0.7-0.65-0.0499999999999999
105-1-0.65-0.35
106-0.9-0.65-0.25
1070-0.650.65
1080.3-0.650.95
1090.80.877777777777778-0.0777777777777777
1100.80.877777777777778-0.0777777777777777
1111.92.17307692307692-0.273076923076923
1122.12.17307692307692-0.0730769230769228
1132.52.173076923076920.326923076923077
1142.72.509523809523810.190476190476191
1152.42.50952380952381-0.10952380952381
1162.42.173076923076920.226923076923077
1172.92.80.1
1183.13.083333333333330.0166666666666666
11932.80.2
1203.43.083333333333330.316666666666666
1213.73.083333333333330.616666666666667
1223.53.440.0600000000000001
1233.53.440.0600000000000001
1243.33.44-0.14
1253.13.083333333333330.0166666666666666
1263.43.44-0.04
12743.440.56
1283.43.44-0.04
1293.43.44-0.04
1303.43.44-0.04
1313.73.440.26
1323.23.44-0.24
1333.33.44-0.14
1343.33.44-0.14
1353.13.44-0.34
1362.93.08333333333333-0.183333333333334
1372.62.509523809523810.0904761904761906
1382.22.173076923076920.0269230769230773
13922.17307692307692-0.173076923076923
1402.62.8-0.2
1412.62.509523809523810.0904761904761906
1422.62.509523809523810.0904761904761906
1432.22.173076923076920.0269230769230773

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 2.7 & 2.17307692307692 & 0.526923076923077 \tabularnewline
2 & 2.5 & 2.17307692307692 & 0.326923076923077 \tabularnewline
3 & 2.2 & 2.17307692307692 & 0.0269230769230773 \tabularnewline
4 & 2.9 & 2.50952380952381 & 0.39047619047619 \tabularnewline
5 & 3.1 & 3.08333333333333 & 0.0166666666666666 \tabularnewline
6 & 3 & 2.8 & 0.2 \tabularnewline
7 & 2.8 & 2.50952380952381 & 0.29047619047619 \tabularnewline
8 & 2.5 & 2.50952380952381 & -0.00952380952380949 \tabularnewline
9 & 1.9 & 1.74444444444444 & 0.155555555555555 \tabularnewline
10 & 1.9 & 1.74444444444444 & 0.155555555555555 \tabularnewline
11 & 1.8 & 1.74444444444444 & 0.0555555555555556 \tabularnewline
12 & 2 & 1.74444444444444 & 0.255555555555556 \tabularnewline
13 & 2.6 & 2.8 & -0.2 \tabularnewline
14 & 2.5 & 2.50952380952381 & -0.00952380952380949 \tabularnewline
15 & 2.5 & 2.50952380952381 & -0.00952380952380949 \tabularnewline
16 & 1.6 & 1.74444444444444 & -0.144444444444444 \tabularnewline
17 & 1.4 & 1.4 & 0 \tabularnewline
18 & 0.8 & 0.877777777777778 & -0.0777777777777777 \tabularnewline
19 & 1.1 & 1.4 & -0.3 \tabularnewline
20 & 1.3 & 1.4 & -0.0999999999999999 \tabularnewline
21 & 1.2 & 1.4 & -0.2 \tabularnewline
22 & 1.3 & 1.4 & -0.0999999999999999 \tabularnewline
23 & 1.1 & 0.877777777777778 & 0.222222222222222 \tabularnewline
24 & 1.3 & 1.4 & -0.0999999999999999 \tabularnewline
25 & 1.2 & 1.4 & -0.2 \tabularnewline
26 & 1.6 & 2.17307692307692 & -0.573076923076923 \tabularnewline
27 & 1.7 & 2.17307692307692 & -0.473076923076923 \tabularnewline
28 & 1.5 & 1.4 & 0.1 \tabularnewline
29 & 0.9 & 0.877777777777778 & 0.0222222222222223 \tabularnewline
30 & 1.5 & 1.74444444444444 & -0.244444444444444 \tabularnewline
31 & 1.4 & 1.4 & 0 \tabularnewline
32 & 1.6 & 1.74444444444444 & -0.144444444444444 \tabularnewline
33 & 1.7 & 1.74444444444444 & -0.0444444444444445 \tabularnewline
34 & 1.4 & 1.74444444444444 & -0.344444444444445 \tabularnewline
35 & 1.8 & 1.74444444444444 & 0.0555555555555556 \tabularnewline
36 & 1.7 & 1.74444444444444 & -0.0444444444444445 \tabularnewline
37 & 1.4 & 1.74444444444444 & -0.344444444444445 \tabularnewline
38 & 1.2 & 1.4 & -0.2 \tabularnewline
39 & 1 & 0.877777777777778 & 0.122222222222222 \tabularnewline
40 & 1.7 & 2.17307692307692 & -0.473076923076923 \tabularnewline
41 & 2.4 & 2.50952380952381 & -0.10952380952381 \tabularnewline
42 & 2 & 2.17307692307692 & -0.173076923076923 \tabularnewline
43 & 2.1 & 2.50952380952381 & -0.409523809523809 \tabularnewline
44 & 2 & 2.17307692307692 & -0.173076923076923 \tabularnewline
45 & 1.8 & 2.17307692307692 & -0.373076923076923 \tabularnewline
46 & 2.7 & 2.8 & -0.0999999999999996 \tabularnewline
47 & 2.3 & 2.50952380952381 & -0.20952380952381 \tabularnewline
48 & 1.9 & 2.17307692307692 & -0.273076923076923 \tabularnewline
49 & 2 & 2.17307692307692 & -0.173076923076923 \tabularnewline
50 & 2.3 & 2.50952380952381 & -0.20952380952381 \tabularnewline
51 & 2.8 & 3.08333333333333 & -0.283333333333334 \tabularnewline
52 & 2.4 & 2.50952380952381 & -0.10952380952381 \tabularnewline
53 & 2.3 & 2.50952380952381 & -0.20952380952381 \tabularnewline
54 & 2.7 & 2.8 & -0.0999999999999996 \tabularnewline
55 & 2.7 & 3.08333333333333 & -0.383333333333333 \tabularnewline
56 & 2.9 & 3.08333333333333 & -0.183333333333334 \tabularnewline
57 & 3 & 3.08333333333333 & -0.0833333333333335 \tabularnewline
58 & 2.2 & 2.50952380952381 & -0.309523809523809 \tabularnewline
59 & 2.3 & 2.50952380952381 & -0.20952380952381 \tabularnewline
60 & 2.8 & 2.8 & 0 \tabularnewline
61 & 2.8 & 2.50952380952381 & 0.29047619047619 \tabularnewline
62 & 2.8 & 2.50952380952381 & 0.29047619047619 \tabularnewline
63 & 2.2 & 2.17307692307692 & 0.0269230769230773 \tabularnewline
64 & 2.6 & 2.17307692307692 & 0.426923076923077 \tabularnewline
65 & 2.8 & 2.17307692307692 & 0.626923076923077 \tabularnewline
66 & 2.5 & 2.17307692307692 & 0.326923076923077 \tabularnewline
67 & 2.4 & 2.17307692307692 & 0.226923076923077 \tabularnewline
68 & 2.3 & 2.17307692307692 & 0.126923076923077 \tabularnewline
69 & 1.9 & 1.4 & 0.5 \tabularnewline
70 & 1.7 & 1.4 & 0.3 \tabularnewline
71 & 2 & 1.4 & 0.6 \tabularnewline
72 & 2.1 & 2.17307692307692 & -0.0730769230769228 \tabularnewline
73 & 1.7 & 1.74444444444444 & -0.0444444444444445 \tabularnewline
74 & 1.8 & 1.74444444444444 & 0.0555555555555556 \tabularnewline
75 & 1.8 & 1.74444444444444 & 0.0555555555555556 \tabularnewline
76 & 1.8 & 1.74444444444444 & 0.0555555555555556 \tabularnewline
77 & 1.3 & 1.4 & -0.0999999999999999 \tabularnewline
78 & 1.3 & 1.4 & -0.0999999999999999 \tabularnewline
79 & 1.3 & 1.4 & -0.0999999999999999 \tabularnewline
80 & 1.2 & 0.877777777777778 & 0.322222222222222 \tabularnewline
81 & 1.4 & 1.4 & 0 \tabularnewline
82 & 2.2 & 2.17307692307692 & 0.0269230769230773 \tabularnewline
83 & 2.9 & 2.8 & 0.1 \tabularnewline
84 & 3.1 & 3.08333333333333 & 0.0166666666666666 \tabularnewline
85 & 3.5 & 3.44 & 0.0600000000000001 \tabularnewline
86 & 3.6 & 3.44 & 0.16 \tabularnewline
87 & 4.4 & 5.125 & -0.725 \tabularnewline
88 & 4.1 & 5.125 & -1.025 \tabularnewline
89 & 5.1 & 5.125 & -0.0250000000000004 \tabularnewline
90 & 5.8 & 5.125 & 0.675 \tabularnewline
91 & 5.9 & 5.125 & 0.775 \tabularnewline
92 & 5.4 & 5.125 & 0.275 \tabularnewline
93 & 5.5 & 5.125 & 0.375 \tabularnewline
94 & 4.8 & 5.125 & -0.325 \tabularnewline
95 & 3.2 & 3.08333333333333 & 0.116666666666667 \tabularnewline
96 & 2.7 & 2.50952380952381 & 0.190476190476191 \tabularnewline
97 & 2.1 & 1.74444444444444 & 0.355555555555556 \tabularnewline
98 & 1.9 & 1.74444444444444 & 0.155555555555555 \tabularnewline
99 & 0.6 & 0.877777777777778 & -0.277777777777778 \tabularnewline
100 & 0.7 & 0.877777777777778 & -0.177777777777778 \tabularnewline
101 & -0.2 & -0.65 & 0.45 \tabularnewline
102 & -1 & -0.65 & -0.35 \tabularnewline
103 & -1.7 & -0.65 & -1.05 \tabularnewline
104 & -0.7 & -0.65 & -0.0499999999999999 \tabularnewline
105 & -1 & -0.65 & -0.35 \tabularnewline
106 & -0.9 & -0.65 & -0.25 \tabularnewline
107 & 0 & -0.65 & 0.65 \tabularnewline
108 & 0.3 & -0.65 & 0.95 \tabularnewline
109 & 0.8 & 0.877777777777778 & -0.0777777777777777 \tabularnewline
110 & 0.8 & 0.877777777777778 & -0.0777777777777777 \tabularnewline
111 & 1.9 & 2.17307692307692 & -0.273076923076923 \tabularnewline
112 & 2.1 & 2.17307692307692 & -0.0730769230769228 \tabularnewline
113 & 2.5 & 2.17307692307692 & 0.326923076923077 \tabularnewline
114 & 2.7 & 2.50952380952381 & 0.190476190476191 \tabularnewline
115 & 2.4 & 2.50952380952381 & -0.10952380952381 \tabularnewline
116 & 2.4 & 2.17307692307692 & 0.226923076923077 \tabularnewline
117 & 2.9 & 2.8 & 0.1 \tabularnewline
118 & 3.1 & 3.08333333333333 & 0.0166666666666666 \tabularnewline
119 & 3 & 2.8 & 0.2 \tabularnewline
120 & 3.4 & 3.08333333333333 & 0.316666666666666 \tabularnewline
121 & 3.7 & 3.08333333333333 & 0.616666666666667 \tabularnewline
122 & 3.5 & 3.44 & 0.0600000000000001 \tabularnewline
123 & 3.5 & 3.44 & 0.0600000000000001 \tabularnewline
124 & 3.3 & 3.44 & -0.14 \tabularnewline
125 & 3.1 & 3.08333333333333 & 0.0166666666666666 \tabularnewline
126 & 3.4 & 3.44 & -0.04 \tabularnewline
127 & 4 & 3.44 & 0.56 \tabularnewline
128 & 3.4 & 3.44 & -0.04 \tabularnewline
129 & 3.4 & 3.44 & -0.04 \tabularnewline
130 & 3.4 & 3.44 & -0.04 \tabularnewline
131 & 3.7 & 3.44 & 0.26 \tabularnewline
132 & 3.2 & 3.44 & -0.24 \tabularnewline
133 & 3.3 & 3.44 & -0.14 \tabularnewline
134 & 3.3 & 3.44 & -0.14 \tabularnewline
135 & 3.1 & 3.44 & -0.34 \tabularnewline
136 & 2.9 & 3.08333333333333 & -0.183333333333334 \tabularnewline
137 & 2.6 & 2.50952380952381 & 0.0904761904761906 \tabularnewline
138 & 2.2 & 2.17307692307692 & 0.0269230769230773 \tabularnewline
139 & 2 & 2.17307692307692 & -0.173076923076923 \tabularnewline
140 & 2.6 & 2.8 & -0.2 \tabularnewline
141 & 2.6 & 2.50952380952381 & 0.0904761904761906 \tabularnewline
142 & 2.6 & 2.50952380952381 & 0.0904761904761906 \tabularnewline
143 & 2.2 & 2.17307692307692 & 0.0269230769230773 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197363&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]2.7[/C][C]2.17307692307692[/C][C]0.526923076923077[/C][/ROW]
[ROW][C]2[/C][C]2.5[/C][C]2.17307692307692[/C][C]0.326923076923077[/C][/ROW]
[ROW][C]3[/C][C]2.2[/C][C]2.17307692307692[/C][C]0.0269230769230773[/C][/ROW]
[ROW][C]4[/C][C]2.9[/C][C]2.50952380952381[/C][C]0.39047619047619[/C][/ROW]
[ROW][C]5[/C][C]3.1[/C][C]3.08333333333333[/C][C]0.0166666666666666[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]2.8[/C][C]0.2[/C][/ROW]
[ROW][C]7[/C][C]2.8[/C][C]2.50952380952381[/C][C]0.29047619047619[/C][/ROW]
[ROW][C]8[/C][C]2.5[/C][C]2.50952380952381[/C][C]-0.00952380952380949[/C][/ROW]
[ROW][C]9[/C][C]1.9[/C][C]1.74444444444444[/C][C]0.155555555555555[/C][/ROW]
[ROW][C]10[/C][C]1.9[/C][C]1.74444444444444[/C][C]0.155555555555555[/C][/ROW]
[ROW][C]11[/C][C]1.8[/C][C]1.74444444444444[/C][C]0.0555555555555556[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.74444444444444[/C][C]0.255555555555556[/C][/ROW]
[ROW][C]13[/C][C]2.6[/C][C]2.8[/C][C]-0.2[/C][/ROW]
[ROW][C]14[/C][C]2.5[/C][C]2.50952380952381[/C][C]-0.00952380952380949[/C][/ROW]
[ROW][C]15[/C][C]2.5[/C][C]2.50952380952381[/C][C]-0.00952380952380949[/C][/ROW]
[ROW][C]16[/C][C]1.6[/C][C]1.74444444444444[/C][C]-0.144444444444444[/C][/ROW]
[ROW][C]17[/C][C]1.4[/C][C]1.4[/C][C]0[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]0.877777777777778[/C][C]-0.0777777777777777[/C][/ROW]
[ROW][C]19[/C][C]1.1[/C][C]1.4[/C][C]-0.3[/C][/ROW]
[ROW][C]20[/C][C]1.3[/C][C]1.4[/C][C]-0.0999999999999999[/C][/ROW]
[ROW][C]21[/C][C]1.2[/C][C]1.4[/C][C]-0.2[/C][/ROW]
[ROW][C]22[/C][C]1.3[/C][C]1.4[/C][C]-0.0999999999999999[/C][/ROW]
[ROW][C]23[/C][C]1.1[/C][C]0.877777777777778[/C][C]0.222222222222222[/C][/ROW]
[ROW][C]24[/C][C]1.3[/C][C]1.4[/C][C]-0.0999999999999999[/C][/ROW]
[ROW][C]25[/C][C]1.2[/C][C]1.4[/C][C]-0.2[/C][/ROW]
[ROW][C]26[/C][C]1.6[/C][C]2.17307692307692[/C][C]-0.573076923076923[/C][/ROW]
[ROW][C]27[/C][C]1.7[/C][C]2.17307692307692[/C][C]-0.473076923076923[/C][/ROW]
[ROW][C]28[/C][C]1.5[/C][C]1.4[/C][C]0.1[/C][/ROW]
[ROW][C]29[/C][C]0.9[/C][C]0.877777777777778[/C][C]0.0222222222222223[/C][/ROW]
[ROW][C]30[/C][C]1.5[/C][C]1.74444444444444[/C][C]-0.244444444444444[/C][/ROW]
[ROW][C]31[/C][C]1.4[/C][C]1.4[/C][C]0[/C][/ROW]
[ROW][C]32[/C][C]1.6[/C][C]1.74444444444444[/C][C]-0.144444444444444[/C][/ROW]
[ROW][C]33[/C][C]1.7[/C][C]1.74444444444444[/C][C]-0.0444444444444445[/C][/ROW]
[ROW][C]34[/C][C]1.4[/C][C]1.74444444444444[/C][C]-0.344444444444445[/C][/ROW]
[ROW][C]35[/C][C]1.8[/C][C]1.74444444444444[/C][C]0.0555555555555556[/C][/ROW]
[ROW][C]36[/C][C]1.7[/C][C]1.74444444444444[/C][C]-0.0444444444444445[/C][/ROW]
[ROW][C]37[/C][C]1.4[/C][C]1.74444444444444[/C][C]-0.344444444444445[/C][/ROW]
[ROW][C]38[/C][C]1.2[/C][C]1.4[/C][C]-0.2[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]0.877777777777778[/C][C]0.122222222222222[/C][/ROW]
[ROW][C]40[/C][C]1.7[/C][C]2.17307692307692[/C][C]-0.473076923076923[/C][/ROW]
[ROW][C]41[/C][C]2.4[/C][C]2.50952380952381[/C][C]-0.10952380952381[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]2.17307692307692[/C][C]-0.173076923076923[/C][/ROW]
[ROW][C]43[/C][C]2.1[/C][C]2.50952380952381[/C][C]-0.409523809523809[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]2.17307692307692[/C][C]-0.173076923076923[/C][/ROW]
[ROW][C]45[/C][C]1.8[/C][C]2.17307692307692[/C][C]-0.373076923076923[/C][/ROW]
[ROW][C]46[/C][C]2.7[/C][C]2.8[/C][C]-0.0999999999999996[/C][/ROW]
[ROW][C]47[/C][C]2.3[/C][C]2.50952380952381[/C][C]-0.20952380952381[/C][/ROW]
[ROW][C]48[/C][C]1.9[/C][C]2.17307692307692[/C][C]-0.273076923076923[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]2.17307692307692[/C][C]-0.173076923076923[/C][/ROW]
[ROW][C]50[/C][C]2.3[/C][C]2.50952380952381[/C][C]-0.20952380952381[/C][/ROW]
[ROW][C]51[/C][C]2.8[/C][C]3.08333333333333[/C][C]-0.283333333333334[/C][/ROW]
[ROW][C]52[/C][C]2.4[/C][C]2.50952380952381[/C][C]-0.10952380952381[/C][/ROW]
[ROW][C]53[/C][C]2.3[/C][C]2.50952380952381[/C][C]-0.20952380952381[/C][/ROW]
[ROW][C]54[/C][C]2.7[/C][C]2.8[/C][C]-0.0999999999999996[/C][/ROW]
[ROW][C]55[/C][C]2.7[/C][C]3.08333333333333[/C][C]-0.383333333333333[/C][/ROW]
[ROW][C]56[/C][C]2.9[/C][C]3.08333333333333[/C][C]-0.183333333333334[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]3.08333333333333[/C][C]-0.0833333333333335[/C][/ROW]
[ROW][C]58[/C][C]2.2[/C][C]2.50952380952381[/C][C]-0.309523809523809[/C][/ROW]
[ROW][C]59[/C][C]2.3[/C][C]2.50952380952381[/C][C]-0.20952380952381[/C][/ROW]
[ROW][C]60[/C][C]2.8[/C][C]2.8[/C][C]0[/C][/ROW]
[ROW][C]61[/C][C]2.8[/C][C]2.50952380952381[/C][C]0.29047619047619[/C][/ROW]
[ROW][C]62[/C][C]2.8[/C][C]2.50952380952381[/C][C]0.29047619047619[/C][/ROW]
[ROW][C]63[/C][C]2.2[/C][C]2.17307692307692[/C][C]0.0269230769230773[/C][/ROW]
[ROW][C]64[/C][C]2.6[/C][C]2.17307692307692[/C][C]0.426923076923077[/C][/ROW]
[ROW][C]65[/C][C]2.8[/C][C]2.17307692307692[/C][C]0.626923076923077[/C][/ROW]
[ROW][C]66[/C][C]2.5[/C][C]2.17307692307692[/C][C]0.326923076923077[/C][/ROW]
[ROW][C]67[/C][C]2.4[/C][C]2.17307692307692[/C][C]0.226923076923077[/C][/ROW]
[ROW][C]68[/C][C]2.3[/C][C]2.17307692307692[/C][C]0.126923076923077[/C][/ROW]
[ROW][C]69[/C][C]1.9[/C][C]1.4[/C][C]0.5[/C][/ROW]
[ROW][C]70[/C][C]1.7[/C][C]1.4[/C][C]0.3[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]1.4[/C][C]0.6[/C][/ROW]
[ROW][C]72[/C][C]2.1[/C][C]2.17307692307692[/C][C]-0.0730769230769228[/C][/ROW]
[ROW][C]73[/C][C]1.7[/C][C]1.74444444444444[/C][C]-0.0444444444444445[/C][/ROW]
[ROW][C]74[/C][C]1.8[/C][C]1.74444444444444[/C][C]0.0555555555555556[/C][/ROW]
[ROW][C]75[/C][C]1.8[/C][C]1.74444444444444[/C][C]0.0555555555555556[/C][/ROW]
[ROW][C]76[/C][C]1.8[/C][C]1.74444444444444[/C][C]0.0555555555555556[/C][/ROW]
[ROW][C]77[/C][C]1.3[/C][C]1.4[/C][C]-0.0999999999999999[/C][/ROW]
[ROW][C]78[/C][C]1.3[/C][C]1.4[/C][C]-0.0999999999999999[/C][/ROW]
[ROW][C]79[/C][C]1.3[/C][C]1.4[/C][C]-0.0999999999999999[/C][/ROW]
[ROW][C]80[/C][C]1.2[/C][C]0.877777777777778[/C][C]0.322222222222222[/C][/ROW]
[ROW][C]81[/C][C]1.4[/C][C]1.4[/C][C]0[/C][/ROW]
[ROW][C]82[/C][C]2.2[/C][C]2.17307692307692[/C][C]0.0269230769230773[/C][/ROW]
[ROW][C]83[/C][C]2.9[/C][C]2.8[/C][C]0.1[/C][/ROW]
[ROW][C]84[/C][C]3.1[/C][C]3.08333333333333[/C][C]0.0166666666666666[/C][/ROW]
[ROW][C]85[/C][C]3.5[/C][C]3.44[/C][C]0.0600000000000001[/C][/ROW]
[ROW][C]86[/C][C]3.6[/C][C]3.44[/C][C]0.16[/C][/ROW]
[ROW][C]87[/C][C]4.4[/C][C]5.125[/C][C]-0.725[/C][/ROW]
[ROW][C]88[/C][C]4.1[/C][C]5.125[/C][C]-1.025[/C][/ROW]
[ROW][C]89[/C][C]5.1[/C][C]5.125[/C][C]-0.0250000000000004[/C][/ROW]
[ROW][C]90[/C][C]5.8[/C][C]5.125[/C][C]0.675[/C][/ROW]
[ROW][C]91[/C][C]5.9[/C][C]5.125[/C][C]0.775[/C][/ROW]
[ROW][C]92[/C][C]5.4[/C][C]5.125[/C][C]0.275[/C][/ROW]
[ROW][C]93[/C][C]5.5[/C][C]5.125[/C][C]0.375[/C][/ROW]
[ROW][C]94[/C][C]4.8[/C][C]5.125[/C][C]-0.325[/C][/ROW]
[ROW][C]95[/C][C]3.2[/C][C]3.08333333333333[/C][C]0.116666666666667[/C][/ROW]
[ROW][C]96[/C][C]2.7[/C][C]2.50952380952381[/C][C]0.190476190476191[/C][/ROW]
[ROW][C]97[/C][C]2.1[/C][C]1.74444444444444[/C][C]0.355555555555556[/C][/ROW]
[ROW][C]98[/C][C]1.9[/C][C]1.74444444444444[/C][C]0.155555555555555[/C][/ROW]
[ROW][C]99[/C][C]0.6[/C][C]0.877777777777778[/C][C]-0.277777777777778[/C][/ROW]
[ROW][C]100[/C][C]0.7[/C][C]0.877777777777778[/C][C]-0.177777777777778[/C][/ROW]
[ROW][C]101[/C][C]-0.2[/C][C]-0.65[/C][C]0.45[/C][/ROW]
[ROW][C]102[/C][C]-1[/C][C]-0.65[/C][C]-0.35[/C][/ROW]
[ROW][C]103[/C][C]-1.7[/C][C]-0.65[/C][C]-1.05[/C][/ROW]
[ROW][C]104[/C][C]-0.7[/C][C]-0.65[/C][C]-0.0499999999999999[/C][/ROW]
[ROW][C]105[/C][C]-1[/C][C]-0.65[/C][C]-0.35[/C][/ROW]
[ROW][C]106[/C][C]-0.9[/C][C]-0.65[/C][C]-0.25[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]-0.65[/C][C]0.65[/C][/ROW]
[ROW][C]108[/C][C]0.3[/C][C]-0.65[/C][C]0.95[/C][/ROW]
[ROW][C]109[/C][C]0.8[/C][C]0.877777777777778[/C][C]-0.0777777777777777[/C][/ROW]
[ROW][C]110[/C][C]0.8[/C][C]0.877777777777778[/C][C]-0.0777777777777777[/C][/ROW]
[ROW][C]111[/C][C]1.9[/C][C]2.17307692307692[/C][C]-0.273076923076923[/C][/ROW]
[ROW][C]112[/C][C]2.1[/C][C]2.17307692307692[/C][C]-0.0730769230769228[/C][/ROW]
[ROW][C]113[/C][C]2.5[/C][C]2.17307692307692[/C][C]0.326923076923077[/C][/ROW]
[ROW][C]114[/C][C]2.7[/C][C]2.50952380952381[/C][C]0.190476190476191[/C][/ROW]
[ROW][C]115[/C][C]2.4[/C][C]2.50952380952381[/C][C]-0.10952380952381[/C][/ROW]
[ROW][C]116[/C][C]2.4[/C][C]2.17307692307692[/C][C]0.226923076923077[/C][/ROW]
[ROW][C]117[/C][C]2.9[/C][C]2.8[/C][C]0.1[/C][/ROW]
[ROW][C]118[/C][C]3.1[/C][C]3.08333333333333[/C][C]0.0166666666666666[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]2.8[/C][C]0.2[/C][/ROW]
[ROW][C]120[/C][C]3.4[/C][C]3.08333333333333[/C][C]0.316666666666666[/C][/ROW]
[ROW][C]121[/C][C]3.7[/C][C]3.08333333333333[/C][C]0.616666666666667[/C][/ROW]
[ROW][C]122[/C][C]3.5[/C][C]3.44[/C][C]0.0600000000000001[/C][/ROW]
[ROW][C]123[/C][C]3.5[/C][C]3.44[/C][C]0.0600000000000001[/C][/ROW]
[ROW][C]124[/C][C]3.3[/C][C]3.44[/C][C]-0.14[/C][/ROW]
[ROW][C]125[/C][C]3.1[/C][C]3.08333333333333[/C][C]0.0166666666666666[/C][/ROW]
[ROW][C]126[/C][C]3.4[/C][C]3.44[/C][C]-0.04[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]3.44[/C][C]0.56[/C][/ROW]
[ROW][C]128[/C][C]3.4[/C][C]3.44[/C][C]-0.04[/C][/ROW]
[ROW][C]129[/C][C]3.4[/C][C]3.44[/C][C]-0.04[/C][/ROW]
[ROW][C]130[/C][C]3.4[/C][C]3.44[/C][C]-0.04[/C][/ROW]
[ROW][C]131[/C][C]3.7[/C][C]3.44[/C][C]0.26[/C][/ROW]
[ROW][C]132[/C][C]3.2[/C][C]3.44[/C][C]-0.24[/C][/ROW]
[ROW][C]133[/C][C]3.3[/C][C]3.44[/C][C]-0.14[/C][/ROW]
[ROW][C]134[/C][C]3.3[/C][C]3.44[/C][C]-0.14[/C][/ROW]
[ROW][C]135[/C][C]3.1[/C][C]3.44[/C][C]-0.34[/C][/ROW]
[ROW][C]136[/C][C]2.9[/C][C]3.08333333333333[/C][C]-0.183333333333334[/C][/ROW]
[ROW][C]137[/C][C]2.6[/C][C]2.50952380952381[/C][C]0.0904761904761906[/C][/ROW]
[ROW][C]138[/C][C]2.2[/C][C]2.17307692307692[/C][C]0.0269230769230773[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]2.17307692307692[/C][C]-0.173076923076923[/C][/ROW]
[ROW][C]140[/C][C]2.6[/C][C]2.8[/C][C]-0.2[/C][/ROW]
[ROW][C]141[/C][C]2.6[/C][C]2.50952380952381[/C][C]0.0904761904761906[/C][/ROW]
[ROW][C]142[/C][C]2.6[/C][C]2.50952380952381[/C][C]0.0904761904761906[/C][/ROW]
[ROW][C]143[/C][C]2.2[/C][C]2.17307692307692[/C][C]0.0269230769230773[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197363&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197363&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
#ActualsForecastsResiduals
12.72.173076923076920.526923076923077
22.52.173076923076920.326923076923077
32.22.173076923076920.0269230769230773
42.92.509523809523810.39047619047619
53.13.083333333333330.0166666666666666
632.80.2
72.82.509523809523810.29047619047619
82.52.50952380952381-0.00952380952380949
91.91.744444444444440.155555555555555
101.91.744444444444440.155555555555555
111.81.744444444444440.0555555555555556
1221.744444444444440.255555555555556
132.62.8-0.2
142.52.50952380952381-0.00952380952380949
152.52.50952380952381-0.00952380952380949
161.61.74444444444444-0.144444444444444
171.41.40
180.80.877777777777778-0.0777777777777777
191.11.4-0.3
201.31.4-0.0999999999999999
211.21.4-0.2
221.31.4-0.0999999999999999
231.10.8777777777777780.222222222222222
241.31.4-0.0999999999999999
251.21.4-0.2
261.62.17307692307692-0.573076923076923
271.72.17307692307692-0.473076923076923
281.51.40.1
290.90.8777777777777780.0222222222222223
301.51.74444444444444-0.244444444444444
311.41.40
321.61.74444444444444-0.144444444444444
331.71.74444444444444-0.0444444444444445
341.41.74444444444444-0.344444444444445
351.81.744444444444440.0555555555555556
361.71.74444444444444-0.0444444444444445
371.41.74444444444444-0.344444444444445
381.21.4-0.2
3910.8777777777777780.122222222222222
401.72.17307692307692-0.473076923076923
412.42.50952380952381-0.10952380952381
4222.17307692307692-0.173076923076923
432.12.50952380952381-0.409523809523809
4422.17307692307692-0.173076923076923
451.82.17307692307692-0.373076923076923
462.72.8-0.0999999999999996
472.32.50952380952381-0.20952380952381
481.92.17307692307692-0.273076923076923
4922.17307692307692-0.173076923076923
502.32.50952380952381-0.20952380952381
512.83.08333333333333-0.283333333333334
522.42.50952380952381-0.10952380952381
532.32.50952380952381-0.20952380952381
542.72.8-0.0999999999999996
552.73.08333333333333-0.383333333333333
562.93.08333333333333-0.183333333333334
5733.08333333333333-0.0833333333333335
582.22.50952380952381-0.309523809523809
592.32.50952380952381-0.20952380952381
602.82.80
612.82.509523809523810.29047619047619
622.82.509523809523810.29047619047619
632.22.173076923076920.0269230769230773
642.62.173076923076920.426923076923077
652.82.173076923076920.626923076923077
662.52.173076923076920.326923076923077
672.42.173076923076920.226923076923077
682.32.173076923076920.126923076923077
691.91.40.5
701.71.40.3
7121.40.6
722.12.17307692307692-0.0730769230769228
731.71.74444444444444-0.0444444444444445
741.81.744444444444440.0555555555555556
751.81.744444444444440.0555555555555556
761.81.744444444444440.0555555555555556
771.31.4-0.0999999999999999
781.31.4-0.0999999999999999
791.31.4-0.0999999999999999
801.20.8777777777777780.322222222222222
811.41.40
822.22.173076923076920.0269230769230773
832.92.80.1
843.13.083333333333330.0166666666666666
853.53.440.0600000000000001
863.63.440.16
874.45.125-0.725
884.15.125-1.025
895.15.125-0.0250000000000004
905.85.1250.675
915.95.1250.775
925.45.1250.275
935.55.1250.375
944.85.125-0.325
953.23.083333333333330.116666666666667
962.72.509523809523810.190476190476191
972.11.744444444444440.355555555555556
981.91.744444444444440.155555555555555
990.60.877777777777778-0.277777777777778
1000.70.877777777777778-0.177777777777778
101-0.2-0.650.45
102-1-0.65-0.35
103-1.7-0.65-1.05
104-0.7-0.65-0.0499999999999999
105-1-0.65-0.35
106-0.9-0.65-0.25
1070-0.650.65
1080.3-0.650.95
1090.80.877777777777778-0.0777777777777777
1100.80.877777777777778-0.0777777777777777
1111.92.17307692307692-0.273076923076923
1122.12.17307692307692-0.0730769230769228
1132.52.173076923076920.326923076923077
1142.72.509523809523810.190476190476191
1152.42.50952380952381-0.10952380952381
1162.42.173076923076920.226923076923077
1172.92.80.1
1183.13.083333333333330.0166666666666666
11932.80.2
1203.43.083333333333330.316666666666666
1213.73.083333333333330.616666666666667
1223.53.440.0600000000000001
1233.53.440.0600000000000001
1243.33.44-0.14
1253.13.083333333333330.0166666666666666
1263.43.44-0.04
12743.440.56
1283.43.44-0.04
1293.43.44-0.04
1303.43.44-0.04
1313.73.440.26
1323.23.44-0.24
1333.33.44-0.14
1343.33.44-0.14
1353.13.44-0.34
1362.93.08333333333333-0.183333333333334
1372.62.509523809523810.0904761904761906
1382.22.173076923076920.0269230769230773
13922.17307692307692-0.173076923076923
1402.62.8-0.2
1412.62.509523809523810.0904761904761906
1422.62.509523809523810.0904761904761906
1432.22.173076923076920.0269230769230773



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = none ; par3 = 0 ; 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')
}