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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 computationSun, 19 Dec 2010 16:08:15 +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/19/t1292774819cakg31s15qkg6cp.htm/, Retrieved Sat, 04 May 2024 20:08:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112544, Retrieved Sat, 04 May 2024 20:08:51 +0000
QR Codes:

Original text written by user:elly.decuyper@student.lessius.eu
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD  [Recursive Partitioning (Regression Trees)] [RECURSIVE PARTITI...] [2010-12-12 19:03:25] [c289bfbb56808c5d93a0f55b5d39f5bd]
-   PD      [Recursive Partitioning (Regression Trees)] [RP - Depressie - ...] [2010-12-19 16:08:15] [3ee4962e6ce79244b15c133e74cea133] [Current]
-             [Recursive Partitioning (Regression Trees)] [RP - Cannotdo - N...] [2010-12-19 16:23:40] [c289bfbb56808c5d93a0f55b5d39f5bd]
-               [Recursive Partitioning (Regression Trees)] [RP - Cannotdo - 2...] [2010-12-19 16:25:39] [c289bfbb56808c5d93a0f55b5d39f5bd]
-                 [Recursive Partitioning (Regression Trees)] [RP - Cannotdo - 2...] [2010-12-19 16:26:51] [c289bfbb56808c5d93a0f55b5d39f5bd]
-                 [Recursive Partitioning (Regression Trees)] [RP - Voorspellen ...] [2010-12-19 16:33:31] [c289bfbb56808c5d93a0f55b5d39f5bd]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	1	4	4	2
0	1	2	2	2
0	1	5	5	4
1	1	4	5	3
0	2	1	1	2
0	1	2	4	1
0	4	5	6	4
0	1	1	5	3
0	1	3	4	1
0	2	5	5	4
1	1	2	7	4
0	1	2	2	4
1	2	2	7	3
0	1	2	5	4
0	1	1	5	1
1	1	4	7	4
1	1	3	3	1
0	1	6	6	4
1	1	1	2	4
0	2	3	6	3
1	1	2	1	2
0	2	5	5	6
0	1	5	4	5
0	2	3	4	4
1	1	3	7	6
0	1	5	7	1
1	1	5	5	2
0	2	4	6	4
1	1	2	5	4
0	1	1	1	1
1	2	4	6	2
0	1	6	4	1
0	1	2	2	2
1	1	3	2	2
1	1	2	6	2
1	2	4	6	6
1	1	2	6	2
0	1	1	1	1
1	1	5	6	4
1	1	5	6	3
0	1	1	1	3
1	1	1	1	1
1	1	2	7	4
0	1	4	2	3
0	1	5	3	4
0	1	3	5	3
0	1	3	3	2
1	1	1	4	1
0	1	2	2	5
1	1	3	3	4
1	2	2	7	1
0	2	5	7	2
1	1	4	5	4
0	1	4	1	3
0	1	2	2	2
0	2	3	5	3
1	1	6	2	3
0	1	2	4	2
1	2	3	7	2
1	1	2	2	4
0	1	5	5	4
0	1	5	6	2
0	1	5	3	2
1	1	6	7	5
0	2	4	4	4
1	1	2	3	5
0	1	5	5	5
1	2	2	3	2
1	1	1	2	3
0	1	6	6	4
0	1	6	6	2
1	1	3	5	2
1	1	4	2	2
0	3	5	3	5
0	2	2	4	2
0	2	4	6	3
1	1	3	5	2
1	1	2	2	2
1	1	2	5	2
1	1	3	2	2
0	1	3	1	2
1	1	7	2	1
0	1	2	4	3
0	1	2	5	3
1	1	2	5	3
0	1	5	3	3
0	1	1	2	1
0	3	5	7	4
0	1	2	1	1
0	1	1	5	1
0	1	2	5	1
0	1	2	2	3
0	1	0	6	2
0	1	5	2	3
0	1	3	5	5
0	1	2	3	3
1	1	4	3	2
1	1	2	5	2
0	1	2	5	3
1	2	4	5	4
0	1	1	6	4
1	1	5	5	3
0	1	4	5	2
1	2	6	6	3
0	1	2	2	3
1	2	5	5	4
1	2	1	5	2
0	3	7	1	5
0	2	5	5	2
0	2	3	6	2
0	1	4	6	4
1	1	4	3	5
0	1	2	3	0
1	1	1	3	1
0	1	6	5	6
0	1	4	5	1
0	1	2	2	2
0	2	7	3	1
0	1	4	3	4
0	1	4	6	2
0	1	4	5	4
0	1	2	2	1
0	1	5	4	4
1	1	3	2	3
0	1	2	2	1
0	1	3	5	2
0	1	4	5	5
1	2	5	4	3
1	2	6	5	2
0	1	2	1	2
1	1	2	5	4
1	1	2	5	4
0	1	2	5	4
1	4	2	6	4
1	1	5	5	4
0	2	2	5	4
0	1	3	6	2
1	1	6	5	4
0	1	4	5	2
1	1	5	7	2
0	1	1	1	1
1	1	2	3	3
0	1	2	5	2
0	1	2	5	1
1	1	6	6	3
1	1	2	4	3
0	1	2	2	2
0	2	1	4	5
1	1	5	5	2
0	1	3	5	5
0	3	6	5	4
0	1	1	5	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112544&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112544&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112544&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Goodness of Fit
Correlation0.3412
R-squared0.1164
RMSE0.5412

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.3412 \tabularnewline
R-squared & 0.1164 \tabularnewline
RMSE & 0.5412 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112544&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.3412[/C][/ROW]
[ROW][C]R-squared[/C][C]0.1164[/C][/ROW]
[ROW][C]RMSE[/C][C]0.5412[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112544&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112544&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
Correlation0.3412
R-squared0.1164
RMSE0.5412






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
111.11235955056180-0.112359550561798
211.11235955056180-0.112359550561798
311.46428571428571-0.464285714285714
411.11235955056180-0.112359550561798
521.112359550561800.887640449438202
611.11235955056180-0.112359550561798
741.542857142857142.45714285714286
811.11235955056180-0.112359550561798
911.11235955056180-0.112359550561798
1021.464285714285710.535714285714286
1111.54285714285714-0.542857142857143
1211.11235955056180-0.112359550561798
1321.542857142857140.457142857142857
1411.11235955056180-0.112359550561798
1511.11235955056180-0.112359550561798
1611.54285714285714-0.542857142857143
1711.11235955056180-0.112359550561798
1811.54285714285714-0.542857142857143
1911.11235955056180-0.112359550561798
2021.542857142857140.457142857142857
2111.11235955056180-0.112359550561798
2221.464285714285710.535714285714286
2311.46428571428571-0.464285714285714
2421.112359550561800.887640449438202
2511.54285714285714-0.542857142857143
2611.54285714285714-0.542857142857143
2711.46428571428571-0.464285714285714
2821.542857142857140.457142857142857
2911.11235955056180-0.112359550561798
3011.11235955056180-0.112359550561798
3121.542857142857140.457142857142857
3211.46428571428571-0.464285714285714
3311.11235955056180-0.112359550561798
3411.11235955056180-0.112359550561798
3511.54285714285714-0.542857142857143
3621.542857142857140.457142857142857
3711.54285714285714-0.542857142857143
3811.11235955056180-0.112359550561798
3911.54285714285714-0.542857142857143
4011.54285714285714-0.542857142857143
4111.11235955056180-0.112359550561798
4211.11235955056180-0.112359550561798
4311.54285714285714-0.542857142857143
4411.11235955056180-0.112359550561798
4511.46428571428571-0.464285714285714
4611.11235955056180-0.112359550561798
4711.11235955056180-0.112359550561798
4811.11235955056180-0.112359550561798
4911.11235955056180-0.112359550561798
5011.11235955056180-0.112359550561798
5121.542857142857140.457142857142857
5221.542857142857140.457142857142857
5311.11235955056180-0.112359550561798
5411.11235955056180-0.112359550561798
5511.11235955056180-0.112359550561798
5621.112359550561800.887640449438202
5711.46428571428571-0.464285714285714
5811.11235955056180-0.112359550561798
5921.542857142857140.457142857142857
6011.11235955056180-0.112359550561798
6111.46428571428571-0.464285714285714
6211.54285714285714-0.542857142857143
6311.46428571428571-0.464285714285714
6411.54285714285714-0.542857142857143
6521.112359550561800.887640449438202
6611.11235955056180-0.112359550561798
6711.46428571428571-0.464285714285714
6821.112359550561800.887640449438202
6911.11235955056180-0.112359550561798
7011.54285714285714-0.542857142857143
7111.54285714285714-0.542857142857143
7211.11235955056180-0.112359550561798
7311.11235955056180-0.112359550561798
7431.464285714285711.53571428571429
7521.112359550561800.887640449438202
7621.542857142857140.457142857142857
7711.11235955056180-0.112359550561798
7811.11235955056180-0.112359550561798
7911.11235955056180-0.112359550561798
8011.11235955056180-0.112359550561798
8111.11235955056180-0.112359550561798
8211.46428571428571-0.464285714285714
8311.11235955056180-0.112359550561798
8411.11235955056180-0.112359550561798
8511.11235955056180-0.112359550561798
8611.46428571428571-0.464285714285714
8711.11235955056180-0.112359550561798
8831.542857142857141.45714285714286
8911.11235955056180-0.112359550561798
9011.11235955056180-0.112359550561798
9111.11235955056180-0.112359550561798
9211.11235955056180-0.112359550561798
9311.54285714285714-0.542857142857143
9411.46428571428571-0.464285714285714
9511.11235955056180-0.112359550561798
9611.11235955056180-0.112359550561798
9711.11235955056180-0.112359550561798
9811.11235955056180-0.112359550561798
9911.11235955056180-0.112359550561798
10021.112359550561800.887640449438202
10111.54285714285714-0.542857142857143
10211.46428571428571-0.464285714285714
10311.11235955056180-0.112359550561798
10421.542857142857140.457142857142857
10511.11235955056180-0.112359550561798
10621.464285714285710.535714285714286
10721.112359550561800.887640449438202
10831.464285714285711.53571428571429
10921.464285714285710.535714285714286
11021.542857142857140.457142857142857
11111.54285714285714-0.542857142857143
11211.11235955056180-0.112359550561798
11311.11235955056180-0.112359550561798
11411.11235955056180-0.112359550561798
11511.46428571428571-0.464285714285714
11611.11235955056180-0.112359550561798
11711.11235955056180-0.112359550561798
11821.464285714285710.535714285714286
11911.11235955056180-0.112359550561798
12011.54285714285714-0.542857142857143
12111.11235955056180-0.112359550561798
12211.11235955056180-0.112359550561798
12311.46428571428571-0.464285714285714
12411.11235955056180-0.112359550561798
12511.11235955056180-0.112359550561798
12611.11235955056180-0.112359550561798
12711.11235955056180-0.112359550561798
12821.464285714285710.535714285714286
12921.464285714285710.535714285714286
13011.11235955056180-0.112359550561798
13111.11235955056180-0.112359550561798
13211.11235955056180-0.112359550561798
13311.11235955056180-0.112359550561798
13441.542857142857142.45714285714286
13511.46428571428571-0.464285714285714
13621.112359550561800.887640449438202
13711.54285714285714-0.542857142857143
13811.46428571428571-0.464285714285714
13911.11235955056180-0.112359550561798
14011.54285714285714-0.542857142857143
14111.11235955056180-0.112359550561798
14211.11235955056180-0.112359550561798
14311.11235955056180-0.112359550561798
14411.11235955056180-0.112359550561798
14511.54285714285714-0.542857142857143
14611.11235955056180-0.112359550561798
14711.11235955056180-0.112359550561798
14821.112359550561800.887640449438202
14911.46428571428571-0.464285714285714
15011.11235955056180-0.112359550561798
15131.464285714285711.53571428571429
15211.11235955056180-0.112359550561798

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
2 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
3 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
4 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
5 & 2 & 1.11235955056180 & 0.887640449438202 \tabularnewline
6 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
7 & 4 & 1.54285714285714 & 2.45714285714286 \tabularnewline
8 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
9 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
10 & 2 & 1.46428571428571 & 0.535714285714286 \tabularnewline
11 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
12 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
13 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
14 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
15 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
16 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
17 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
18 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
19 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
20 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
21 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
22 & 2 & 1.46428571428571 & 0.535714285714286 \tabularnewline
23 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
24 & 2 & 1.11235955056180 & 0.887640449438202 \tabularnewline
25 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
26 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
27 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
28 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
29 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
30 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
31 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
32 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
33 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
34 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
35 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
36 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
37 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
38 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
39 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
40 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
41 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
42 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
43 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
44 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
45 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
46 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
47 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
48 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
49 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
50 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
51 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
52 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
53 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
54 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
55 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
56 & 2 & 1.11235955056180 & 0.887640449438202 \tabularnewline
57 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
58 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
59 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
60 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
61 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
62 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
63 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
64 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
65 & 2 & 1.11235955056180 & 0.887640449438202 \tabularnewline
66 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
67 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
68 & 2 & 1.11235955056180 & 0.887640449438202 \tabularnewline
69 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
70 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
71 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
72 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
73 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
74 & 3 & 1.46428571428571 & 1.53571428571429 \tabularnewline
75 & 2 & 1.11235955056180 & 0.887640449438202 \tabularnewline
76 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
77 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
78 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
79 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
80 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
81 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
82 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
83 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
84 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
85 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
86 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
87 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
88 & 3 & 1.54285714285714 & 1.45714285714286 \tabularnewline
89 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
90 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
91 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
92 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
93 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
94 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
95 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
96 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
97 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
98 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
99 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
100 & 2 & 1.11235955056180 & 0.887640449438202 \tabularnewline
101 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
102 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
103 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
104 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
105 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
106 & 2 & 1.46428571428571 & 0.535714285714286 \tabularnewline
107 & 2 & 1.11235955056180 & 0.887640449438202 \tabularnewline
108 & 3 & 1.46428571428571 & 1.53571428571429 \tabularnewline
109 & 2 & 1.46428571428571 & 0.535714285714286 \tabularnewline
110 & 2 & 1.54285714285714 & 0.457142857142857 \tabularnewline
111 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
112 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
113 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
114 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
115 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
116 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
117 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
118 & 2 & 1.46428571428571 & 0.535714285714286 \tabularnewline
119 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
120 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
121 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
122 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
123 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
124 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
125 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
126 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
127 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
128 & 2 & 1.46428571428571 & 0.535714285714286 \tabularnewline
129 & 2 & 1.46428571428571 & 0.535714285714286 \tabularnewline
130 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
131 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
132 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
133 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
134 & 4 & 1.54285714285714 & 2.45714285714286 \tabularnewline
135 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
136 & 2 & 1.11235955056180 & 0.887640449438202 \tabularnewline
137 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
138 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
139 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
140 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
141 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
142 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
143 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
144 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
145 & 1 & 1.54285714285714 & -0.542857142857143 \tabularnewline
146 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
147 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
148 & 2 & 1.11235955056180 & 0.887640449438202 \tabularnewline
149 & 1 & 1.46428571428571 & -0.464285714285714 \tabularnewline
150 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
151 & 3 & 1.46428571428571 & 1.53571428571429 \tabularnewline
152 & 1 & 1.11235955056180 & -0.112359550561798 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112544&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]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]1.11235955056180[/C][C]0.887640449438202[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]1.54285714285714[/C][C]2.45714285714286[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.46428571428571[/C][C]0.535714285714286[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]1.46428571428571[/C][C]0.535714285714286[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.11235955056180[/C][C]0.887640449438202[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]56[/C][C]2[/C][C]1.11235955056180[/C][C]0.887640449438202[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]1.11235955056180[/C][C]0.887640449438202[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]1.11235955056180[/C][C]0.887640449438202[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]1.46428571428571[/C][C]1.53571428571429[/C][/ROW]
[ROW][C]75[/C][C]2[/C][C]1.11235955056180[/C][C]0.887640449438202[/C][/ROW]
[ROW][C]76[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]1.54285714285714[/C][C]1.45714285714286[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]1.11235955056180[/C][C]0.887640449438202[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]104[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]1.46428571428571[/C][C]0.535714285714286[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.11235955056180[/C][C]0.887640449438202[/C][/ROW]
[ROW][C]108[/C][C]3[/C][C]1.46428571428571[/C][C]1.53571428571429[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]1.46428571428571[/C][C]0.535714285714286[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.54285714285714[/C][C]0.457142857142857[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.46428571428571[/C][C]0.535714285714286[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]1.46428571428571[/C][C]0.535714285714286[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]1.46428571428571[/C][C]0.535714285714286[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]134[/C][C]4[/C][C]1.54285714285714[/C][C]2.45714285714286[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]1.11235955056180[/C][C]0.887640449438202[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.54285714285714[/C][C]-0.542857142857143[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]147[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]1.11235955056180[/C][C]0.887640449438202[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]1.46428571428571[/C][C]-0.464285714285714[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[ROW][C]151[/C][C]3[/C][C]1.46428571428571[/C][C]1.53571428571429[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]1.11235955056180[/C][C]-0.112359550561798[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112544&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112544&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
111.11235955056180-0.112359550561798
211.11235955056180-0.112359550561798
311.46428571428571-0.464285714285714
411.11235955056180-0.112359550561798
521.112359550561800.887640449438202
611.11235955056180-0.112359550561798
741.542857142857142.45714285714286
811.11235955056180-0.112359550561798
911.11235955056180-0.112359550561798
1021.464285714285710.535714285714286
1111.54285714285714-0.542857142857143
1211.11235955056180-0.112359550561798
1321.542857142857140.457142857142857
1411.11235955056180-0.112359550561798
1511.11235955056180-0.112359550561798
1611.54285714285714-0.542857142857143
1711.11235955056180-0.112359550561798
1811.54285714285714-0.542857142857143
1911.11235955056180-0.112359550561798
2021.542857142857140.457142857142857
2111.11235955056180-0.112359550561798
2221.464285714285710.535714285714286
2311.46428571428571-0.464285714285714
2421.112359550561800.887640449438202
2511.54285714285714-0.542857142857143
2611.54285714285714-0.542857142857143
2711.46428571428571-0.464285714285714
2821.542857142857140.457142857142857
2911.11235955056180-0.112359550561798
3011.11235955056180-0.112359550561798
3121.542857142857140.457142857142857
3211.46428571428571-0.464285714285714
3311.11235955056180-0.112359550561798
3411.11235955056180-0.112359550561798
3511.54285714285714-0.542857142857143
3621.542857142857140.457142857142857
3711.54285714285714-0.542857142857143
3811.11235955056180-0.112359550561798
3911.54285714285714-0.542857142857143
4011.54285714285714-0.542857142857143
4111.11235955056180-0.112359550561798
4211.11235955056180-0.112359550561798
4311.54285714285714-0.542857142857143
4411.11235955056180-0.112359550561798
4511.46428571428571-0.464285714285714
4611.11235955056180-0.112359550561798
4711.11235955056180-0.112359550561798
4811.11235955056180-0.112359550561798
4911.11235955056180-0.112359550561798
5011.11235955056180-0.112359550561798
5121.542857142857140.457142857142857
5221.542857142857140.457142857142857
5311.11235955056180-0.112359550561798
5411.11235955056180-0.112359550561798
5511.11235955056180-0.112359550561798
5621.112359550561800.887640449438202
5711.46428571428571-0.464285714285714
5811.11235955056180-0.112359550561798
5921.542857142857140.457142857142857
6011.11235955056180-0.112359550561798
6111.46428571428571-0.464285714285714
6211.54285714285714-0.542857142857143
6311.46428571428571-0.464285714285714
6411.54285714285714-0.542857142857143
6521.112359550561800.887640449438202
6611.11235955056180-0.112359550561798
6711.46428571428571-0.464285714285714
6821.112359550561800.887640449438202
6911.11235955056180-0.112359550561798
7011.54285714285714-0.542857142857143
7111.54285714285714-0.542857142857143
7211.11235955056180-0.112359550561798
7311.11235955056180-0.112359550561798
7431.464285714285711.53571428571429
7521.112359550561800.887640449438202
7621.542857142857140.457142857142857
7711.11235955056180-0.112359550561798
7811.11235955056180-0.112359550561798
7911.11235955056180-0.112359550561798
8011.11235955056180-0.112359550561798
8111.11235955056180-0.112359550561798
8211.46428571428571-0.464285714285714
8311.11235955056180-0.112359550561798
8411.11235955056180-0.112359550561798
8511.11235955056180-0.112359550561798
8611.46428571428571-0.464285714285714
8711.11235955056180-0.112359550561798
8831.542857142857141.45714285714286
8911.11235955056180-0.112359550561798
9011.11235955056180-0.112359550561798
9111.11235955056180-0.112359550561798
9211.11235955056180-0.112359550561798
9311.54285714285714-0.542857142857143
9411.46428571428571-0.464285714285714
9511.11235955056180-0.112359550561798
9611.11235955056180-0.112359550561798
9711.11235955056180-0.112359550561798
9811.11235955056180-0.112359550561798
9911.11235955056180-0.112359550561798
10021.112359550561800.887640449438202
10111.54285714285714-0.542857142857143
10211.46428571428571-0.464285714285714
10311.11235955056180-0.112359550561798
10421.542857142857140.457142857142857
10511.11235955056180-0.112359550561798
10621.464285714285710.535714285714286
10721.112359550561800.887640449438202
10831.464285714285711.53571428571429
10921.464285714285710.535714285714286
11021.542857142857140.457142857142857
11111.54285714285714-0.542857142857143
11211.11235955056180-0.112359550561798
11311.11235955056180-0.112359550561798
11411.11235955056180-0.112359550561798
11511.46428571428571-0.464285714285714
11611.11235955056180-0.112359550561798
11711.11235955056180-0.112359550561798
11821.464285714285710.535714285714286
11911.11235955056180-0.112359550561798
12011.54285714285714-0.542857142857143
12111.11235955056180-0.112359550561798
12211.11235955056180-0.112359550561798
12311.46428571428571-0.464285714285714
12411.11235955056180-0.112359550561798
12511.11235955056180-0.112359550561798
12611.11235955056180-0.112359550561798
12711.11235955056180-0.112359550561798
12821.464285714285710.535714285714286
12921.464285714285710.535714285714286
13011.11235955056180-0.112359550561798
13111.11235955056180-0.112359550561798
13211.11235955056180-0.112359550561798
13311.11235955056180-0.112359550561798
13441.542857142857142.45714285714286
13511.46428571428571-0.464285714285714
13621.112359550561800.887640449438202
13711.54285714285714-0.542857142857143
13811.46428571428571-0.464285714285714
13911.11235955056180-0.112359550561798
14011.54285714285714-0.542857142857143
14111.11235955056180-0.112359550561798
14211.11235955056180-0.112359550561798
14311.11235955056180-0.112359550561798
14411.11235955056180-0.112359550561798
14511.54285714285714-0.542857142857143
14611.11235955056180-0.112359550561798
14711.11235955056180-0.112359550561798
14821.112359550561800.887640449438202
14911.46428571428571-0.464285714285714
15011.11235955056180-0.112359550561798
15131.464285714285711.53571428571429
15211.11235955056180-0.112359550561798


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
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')
}