FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationWed, 22 Dec 2010 20:22:32 +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/22/t12930492672jc7rn0y22686vd.htm/, Retrieved Mon, 06 May 2024 04:10:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114559, Retrieved Mon, 06 May 2024 04:10:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD  [Bivariate Explorative Data Analysis] [Ws4 part 1.1 s090...] [2009-10-27 21:56:53] [e0fc65a5811681d807296d590d5b45de]
-  M D    [Bivariate Explorative Data Analysis] [Paper; bivariate ...] [2009-12-19 19:10:37] [e0fc65a5811681d807296d590d5b45de]
- RMPD      [Cross Correlation Function] [cross correlation...] [2010-12-08 19:50:23] [74be16979710d4c4e7c6647856088456]
- RMPD        [Recursive Partitioning (Regression Trees)] [] [2010-12-09 09:47:05] [b98453cac15ba1066b407e146608df68]
-    D            [Recursive Partitioning (Regression Trees)] [] [2010-12-22 20:22:32] [6b31f806e9ccc1f74a26091056f791cb] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.01244	0.00149	0.01848	0.00338	0.00099	-0.01826	-0.01025	0.04860	0.04399	-0.03429	0.00779
0.01150	0.01244	0.00149	0.01848	0.00338	0.00099	-0.03086	-0.01025	0.04860	0.04399	-0.03429
-0.00793	0.01150	0.01244	0.00149	0.01848	0.00338	0.04033	-0.03086	-0.01025	0.04860	0.04399
-0.01514	-0.00793	0.01150	0.01244	0.00149	0.01848	-0.02352	0.04033	-0.03086	-0.01025	0.04860
0.01778	-0.01514	-0.00793	0.01150	0.01244	0.00149	0.00573	-0.02352	0.04033	-0.03086	-0.01025
0.00634	0.01778	-0.01514	-0.00793	0.01150	0.01244	0.01805	0.00573	-0.02352	0.04033	-0.03086
0.00770	0.00634	0.01778	-0.01514	-0.00793	0.01150	-0.01887	0.01805	0.00573	-0.02352	0.04033
0.00692	0.00770	0.00634	0.01778	-0.01514	-0.00793	0.04363	-0.01887	0.01805	0.00573	-0.02352
0.00029	0.00692	0.00770	0.00634	0.01778	-0.01514	0.02875	0.04363	-0.01887	0.01805	0.00573
0.02487	0.00029	0.00692	0.00770	0.00634	0.01778	-0.00393	0.02875	0.04363	-0.01887	0.01805
0.01708	0.02487	0.00029	0.00692	0.00770	0.00634	0.05280	-0.00393	0.02875	0.04363	-0.01887
0.02540	0.01708	0.02487	0.00029	0.00692	0.00770	-0.00351	0.05280	-0.00393	0.02875	0.04363
0.02935	0.02540	0.01708	0.02487	0.00029	0.00692	0.05407	-0.00351	0.05280	-0.00393	0.02875
0.02615	0.02935	0.02540	0.01708	0.02487	0.00029	-0.01299	0.05407	-0.00351	0.05280	-0.00393
0.00424	0.02615	0.02935	0.02540	0.01708	0.02487	0.00747	-0.01299	0.05407	-0.00351	0.05280
-0.00032	0.00424	0.02615	0.02935	0.02540	0.01708	-0.03288	0.00747	-0.01299	0.05407	-0.00351
-0.02353	-0.00032	0.00424	0.02615	0.02935	0.02540	-0.05013	-0.03288	0.00747	-0.01299	0.05407
0.01387	-0.02353	-0.00032	0.00424	0.02615	0.02935	0.03715	-0.05013	-0.03288	0.00747	-0.01299
0.01286	0.01387	-0.02353	-0.00032	0.00424	0.02615	0.00205	0.03715	-0.05013	-0.03288	0.00747
-0.00609	0.01286	0.01387	-0.02353	-0.00032	0.00424	0.02912	0.00205	0.03715	-0.05013	-0.03288
0.00635	-0.00609	0.01286	0.01387	-0.02353	-0.00032	-0.00832	0.02912	0.00205	0.03715	-0.05013
0.02049	0.00635	-0.00609	0.01286	0.01387	-0.02353	0.02908	-0.00832	0.02912	0.00205	0.03715
0.00332	0.02049	0.00635	-0.00609	0.01286	0.01387	-0.00942	0.02908	-0.00832	0.02912	0.00205
0.00409	0.00332	0.02049	0.00635	-0.00609	0.01286	0.04381	-0.00942	0.02908	-0.00832	0.02912
0.02753	0.00409	0.00332	0.02049	0.00635	-0.00609	0.00603	0.04381	-0.00942	0.02908	-0.00832
0.01205	0.02753	0.00409	0.00332	0.02049	0.00635	0.02253	0.00603	0.04381	-0.00942	0.02908
0.01773	0.01205	0.02753	0.00409	0.00332	0.02049	0.05789	0.02253	0.00603	0.04381	-0.00942
-0.00897	0.01773	0.01205	0.02753	0.00409	0.00332	-0.03783	0.05789	0.02253	0.00603	0.04381
-0.01226	-0.00897	0.01773	0.01205	0.02753	0.00409	-0.03176	-0.03783	0.05789	0.02253	0.00603
0.00644	-0.01226	-0.00897	0.01773	0.01205	0.02753	-0.00572	-0.03176	-0.03783	0.05789	0.02253
-0.00059	0.00644	-0.01226	-0.00897	0.01773	0.01205	0.01040	-0.00572	-0.03176	-0.03783	0.05789
0.01707	-0.00059	0.00644	-0.01226	-0.00897	0.01773	0.03662	0.01040	-0.00572	-0.03176	-0.03783
-0.00104	0.01707	-0.00059	0.00644	-0.01226	-0.00897	0.03771	0.03662	0.01040	-0.00572	-0.03176
0.01272	-0.00104	0.01707	-0.00059	0.00644	-0.01226	0.05981	0.03771	0.03662	0.01040	-0.00572
0.01859	0.01272	-0.00104	0.01707	-0.00059	0.00644	-0.03204	0.05981	0.03771	0.03662	0.01040
0.03238	0.01859	0.01272	-0.00104	0.01707	-0.00059	0.02837	-0.03204	0.05981	0.03771	0.03662
0.03132	0.03238	0.01859	0.01272	-0.00104	0.01707	0.05003	0.02837	-0.03204	0.05981	0.03771
0.01412	0.03132	0.03238	0.01859	0.01272	-0.00104	0.04980	0.05003	0.02837	-0.03204	0.05981
0.00588	0.01412	0.03132	0.03238	0.01859	0.01272	-0.02299	0.04980	0.05003	0.02837	-0.03204
0.05686	0.00588	0.01412	0.03132	0.03238	0.01859	0.04030	-0.02299	0.04980	0.05003	0.02837
0.05681	0.05686	0.00588	0.01412	0.03132	0.03238	0.03176	0.04030	-0.02299	0.04980	0.05003
-0.04078	0.05681	0.05686	0.00588	0.01412	0.03132	-0.00135	0.03176	0.04030	-0.02299	0.04980
0.02507	-0.04078	0.05681	0.05686	0.00588	0.01412	-0.02473	-0.00135	0.03176	0.04030	-0.02299
0.00600	0.02507	-0.04078	0.05681	0.05686	0.00588	-0.00171	-0.02473	-0.00135	0.03176	0.04030
0.00249	0.00600	0.02507	-0.04078	0.05681	0.05686	-0.01575	-0.00171	-0.02473	-0.00135	0.03176
0.01885	0.00249	0.00600	0.02507	-0.04078	0.05681	-0.02624	-0.01575	-0.00171	-0.02473	-0.00135
0.00125	0.01885	0.00249	0.00600	0.02507	-0.04078	0.06724	-0.02624	-0.01575	-0.00171	-0.02473
0.00695	0.00125	0.01885	0.00249	0.00600	0.02507	-0.01362	0.06724	-0.02624	-0.01575	-0.00171
-0.01563	0.00695	0.00125	0.01885	0.00249	0.00600	-0.00422	-0.01362	0.06724	-0.02624	-0.01575
0.00814	-0.01563	0.00695	0.00125	0.01885	0.00249	0.00754	-0.00422	-0.01362	0.06724	-0.02624
0.02368	0.00814	-0.01563	0.00695	0.00125	0.01885	0.00087	0.00754	-0.00422	-0.01362	0.06724
0.04099	0.02368	0.00814	-0.01563	0.00695	0.00125	0.02715	0.00087	0.00754	-0.00422	-0.01362
0.00731	0.04099	0.02368	0.00814	-0.01563	0.00695	0.02976	0.02715	0.00087	0.00754	-0.00422
-0.01730	0.00731	0.04099	0.02368	0.00814	-0.01563	0.07946	0.02976	0.02715	0.00087	0.00754
-0.00183	-0.01730	0.00731	0.04099	0.02368	0.00814	0.01909	0.07946	0.02976	0.02715	0.00087
-0.03830	-0.00183	-0.01730	0.00731	0.04099	0.02368	-0.02483	0.01909	0.07946	0.02976	0.02715
-0.01249	-0.03830	-0.00183	-0.01730	0.00731	0.04099	-0.01870	-0.02483	0.01909	0.07946	0.02976
0.01229	-0.01249	-0.03830	-0.00183	-0.01730	0.00731	0.09682	-0.01870	-0.02483	0.01909	0.07946
-0.01747	0.01229	-0.01249	-0.03830	-0.00183	-0.01730	0.03823	0.09682	-0.01870	-0.02483	0.01909
-0.02645	-0.01747	0.01229	-0.01249	-0.03830	-0.00183	0.09571	0.03823	0.09682	-0.01870	-0.02483
0.04038	-0.02645	-0.01747	0.01229	-0.01249	-0.03830	-0.04663	0.09571	0.03823	0.09682	-0.01870
0.02925	0.04038	-0.02645	-0.01747	0.01229	-0.01249	-0.01359	-0.04663	0.09571	0.03823	0.09682
0.02270	0.02925	0.04038	-0.02645	-0.01747	0.01229	0.05114	-0.01359	-0.04663	0.09571	0.03823
-0.00460	0.02270	0.02925	0.04038	-0.02645	-0.01747	-0.04275	0.05114	-0.01359	-0.04663	0.09571
-0.01894	-0.00460	0.02270	0.02925	0.04038	-0.02645	0.05739	-0.04275	0.05114	-0.01359	-0.04663
-0.00966	-0.01894	-0.00460	0.02270	0.02925	0.04038	0.01186	0.05739	-0.04275	0.05114	-0.01359
0.00392	-0.00966	-0.01894	-0.00460	0.02270	0.02925	0.01066	0.01186	0.05739	-0.04275	0.05114
-0.03105	0.00392	-0.00966	-0.01894	-0.00460	0.02270	-0.07387	0.01066	0.01186	0.05739	-0.04275
-0.02790	-0.03105	0.00392	-0.00966	-0.01894	-0.00460	-0.04131	-0.07387	0.01066	0.01186	0.05739
-0.09625	-0.02790	-0.03105	0.00392	-0.00966	-0.01894	-0.17889	-0.04131	-0.07387	0.01066	0.01186
-0.05388	-0.09625	-0.02790	-0.03105	0.00392	-0.00966	-0.12781	-0.17889	-0.04131	-0.07387	0.01066
-0.05034	-0.05388	-0.09625	-0.02790	-0.03105	0.00392	-0.26933	-0.12781	-0.17889	-0.04131	-0.07387
-0.02846	-0.05034	-0.05388	-0.09625	-0.02790	-0.03105	-0.05095	-0.26933	-0.12781	-0.17889	-0.04131
-0.01454	-0.02846	-0.05034	-0.05388	-0.09625	-0.02790	-0.01074	-0.05095	-0.26933	-0.12781	-0.17889
0.01284	-0.01454	-0.02846	-0.05034	-0.05388	-0.09625	0.08172	-0.01074	-0.05095	-0.26933	-0.12781
0.03762	0.01284	-0.01454	-0.02846	-0.05034	-0.05388	0.11870	0.08172	-0.01074	-0.05095	-0.26933
0.01973	0.03762	0.01284	-0.01454	-0.02846	-0.05034	0.08475	0.11870	0.08172	-0.01074	-0.05095
0.03178	0.01973	0.03762	0.01284	-0.01454	-0.02846	0.04663	0.08475	0.11870	0.08172	-0.01074
0.01329	0.03178	0.01973	0.03762	0.01284	-0.01454	-0.04415	0.04663	0.08475	0.11870	0.08172
0.05094	0.01329	0.03178	0.01973	0.03762	0.01284	0.00970	-0.04415	0.04663	0.08475	0.11870
-0.00804	0.05094	0.01329	0.03178	0.01973	0.03762	-0.03341	0.00970	-0.04415	0.04663	0.08475
0.01116	-0.00804	0.05094	0.01329	0.03178	0.01973	0.04031	-0.03341	0.00970	-0.04415	0.04663
0.01128	0.01116	-0.00804	0.05094	0.01329	0.03178	0.01938	0.04031	-0.03341	0.00970	-0.04415
0.02227	0.01128	0.01116	-0.00804	0.05094	0.01329	0.05928	0.01938	0.04031	-0.03341	0.00970
0.01494	0.02227	0.01128	0.01116	-0.00804	0.05094	0.02343	0.05928	0.01938	0.04031	-0.03341
-0.02514	0.01494	0.02227	0.01128	0.01116	-0.00804	-0.04536	0.02343	0.05928	0.01938	0.04031
0.02975	-0.02514	0.01494	0.02227	0.01128	0.01116	0.03355	-0.04536	0.02343	0.05928	0.01938
0.05216	0.02975	-0.02514	0.01494	0.02227	0.01128	0.05659	0.03355	-0.04536	0.02343	0.05928
-0.04459	0.05216	0.02975	-0.02514	0.01494	0.02227	-0.06579	0.05659	0.03355	-0.04536	0.02343
-0.02212	-0.04459	0.05216	0.02975	-0.02514	0.01494	-0.04267	-0.06579	0.05659	0.03355	-0.04536
0.03171	-0.02212	-0.04459	0.05216	0.02975	-0.02514	-0.02422	-0.04267	-0.06579	0.05659	0.03355
0.02985	0.03171	-0.02212	-0.04459	0.05216	0.02975	0.07584	-0.02422	-0.04267	-0.06579	0.05659
0.01545	0.02985	0.03171	-0.02212	-0.04459	0.05216	-0.00903	0.07584	-0.02422	-0.04267	-0.06579
0.01140	0.01545	0.02985	0.03171	-0.02212	-0.04459	0.06617	-0.00903	0.07584	-0.02422	-0.04267
0.00238	0.01140	0.01545	0.02985	0.03171	-0.02212	0.04485	0.06617	-0.00903	0.07584	-0.02422

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114559&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114559&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114559&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Goodness of Fit
Correlation0.594
R-squared0.3528
RMSE0.02

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.594 \tabularnewline
R-squared & 0.3528 \tabularnewline
RMSE & 0.02 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114559&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.594[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3528[/C][/ROW]
[ROW][C]RMSE[/C][C]0.02[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114559&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114559&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
Correlation0.594
R-squared0.3528
RMSE0.02






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
10.012440.009713977272727270.00272602272727273
20.01150.009713977272727270.00178602272727273
3-0.007930.00971397727272727-0.0176439772727273
4-0.015140.00971397727272727-0.0248539772727273
50.017780.009713977272727270.00806602272727273
60.006340.00971397727272727-0.00337397727272727
70.00770.00971397727272727-0.00201397727272727
80.006920.00971397727272727-0.00279397727272727
90.000290.00971397727272727-0.00942397727272727
100.024870.009713977272727270.0151560227272727
110.017080.009713977272727270.00736602272727273
120.02540.009713977272727270.0156860227272727
130.029350.009713977272727270.0196360227272727
140.026150.009713977272727270.0164360227272727
150.004240.00971397727272727-0.00547397727272727
16-0.000320.00971397727272727-0.0100339772727273
17-0.02353-0.04687142857142860.0233414285714286
180.013870.009713977272727270.00415602272727273
190.012860.009713977272727270.00314602272727273
20-0.006090.00971397727272727-0.0158039772727273
210.006350.00971397727272727-0.00336397727272727
220.020490.009713977272727270.0107760227272727
230.003320.00971397727272727-0.00639397727272727
240.004090.00971397727272727-0.00562397727272727
250.027530.009713977272727270.0178160227272727
260.012050.009713977272727270.00233602272727273
270.017730.009713977272727270.00801602272727273
28-0.008970.00971397727272727-0.0186839772727273
29-0.012260.00971397727272727-0.0219739772727273
300.006440.00971397727272727-0.00327397727272727
31-0.000590.00971397727272727-0.0103039772727273
320.017070.009713977272727270.00735602272727272
33-0.001040.00971397727272727-0.0107539772727273
340.012720.009713977272727270.00300602272727273
350.018590.009713977272727270.00887602272727273
360.032380.009713977272727270.0226660227272727
370.031320.009713977272727270.0216060227272727
380.014120.009713977272727270.00440602272727273
390.005880.00971397727272727-0.00383397727272727
400.056860.009713977272727270.0471460227272727
410.056810.009713977272727270.0470960227272727
42-0.040780.00971397727272727-0.0504939772727273
430.025070.009713977272727270.0153560227272727
440.0060.00971397727272727-0.00371397727272727
450.002490.00971397727272727-0.00722397727272727
460.018850.009713977272727270.00913602272727273
470.001250.00971397727272727-0.00846397727272727
480.006950.00971397727272727-0.00276397727272727
49-0.015630.00971397727272727-0.0253439772727273
500.008140.00971397727272727-0.00157397727272727
510.023680.009713977272727270.0139660227272727
520.040990.009713977272727270.0312760227272727
530.007310.00971397727272727-0.00240397727272727
54-0.01730.00971397727272727-0.0270139772727273
55-0.001830.00971397727272727-0.0115439772727273
56-0.03830.00971397727272727-0.0480139772727273
57-0.012490.00971397727272727-0.0222039772727273
580.012290.009713977272727270.00257602272727273
59-0.017470.00971397727272727-0.0271839772727273
60-0.026450.00971397727272727-0.0361639772727273
610.040380.009713977272727270.0306660227272727
620.029250.009713977272727270.0195360227272727
630.02270.009713977272727270.0129860227272727
64-0.00460.00971397727272727-0.0143139772727273
65-0.018940.00971397727272727-0.0286539772727273
66-0.009660.00971397727272727-0.0193739772727273
670.003920.00971397727272727-0.00579397727272727
68-0.03105-0.04687142857142860.0158214285714286
69-0.02790.00971397727272727-0.0376139772727273
70-0.09625-0.0468714285714286-0.0493785714285714
71-0.05388-0.0468714285714286-0.00700857142857143
72-0.05034-0.0468714285714286-0.00346857142857143
73-0.02846-0.04687142857142860.0184114285714286
74-0.014540.00971397727272727-0.0242539772727273
750.012840.009713977272727270.00312602272727273
760.037620.009713977272727270.0279060227272727
770.019730.009713977272727270.0100160227272727
780.031780.009713977272727270.0220660227272727
790.013290.009713977272727270.00357602272727273
800.050940.009713977272727270.0412260227272727
81-0.008040.00971397727272727-0.0177539772727273
820.011160.009713977272727270.00144602272727273
830.011280.009713977272727270.00156602272727273
840.022270.009713977272727270.0125560227272727
850.014940.009713977272727270.00522602272727273
86-0.025140.00971397727272727-0.0348539772727273
870.029750.009713977272727270.0200360227272727
880.052160.009713977272727270.0424460227272727
89-0.04459-0.04687142857142860.00228142857142857
90-0.022120.00971397727272727-0.0318339772727273
910.031710.009713977272727270.0219960227272727
920.029850.009713977272727270.0201360227272727
930.015450.009713977272727270.00573602272727273
940.01140.009713977272727270.00168602272727273
950.002380.00971397727272727-0.00733397727272727

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 0.01244 & 0.00971397727272727 & 0.00272602272727273 \tabularnewline
2 & 0.0115 & 0.00971397727272727 & 0.00178602272727273 \tabularnewline
3 & -0.00793 & 0.00971397727272727 & -0.0176439772727273 \tabularnewline
4 & -0.01514 & 0.00971397727272727 & -0.0248539772727273 \tabularnewline
5 & 0.01778 & 0.00971397727272727 & 0.00806602272727273 \tabularnewline
6 & 0.00634 & 0.00971397727272727 & -0.00337397727272727 \tabularnewline
7 & 0.0077 & 0.00971397727272727 & -0.00201397727272727 \tabularnewline
8 & 0.00692 & 0.00971397727272727 & -0.00279397727272727 \tabularnewline
9 & 0.00029 & 0.00971397727272727 & -0.00942397727272727 \tabularnewline
10 & 0.02487 & 0.00971397727272727 & 0.0151560227272727 \tabularnewline
11 & 0.01708 & 0.00971397727272727 & 0.00736602272727273 \tabularnewline
12 & 0.0254 & 0.00971397727272727 & 0.0156860227272727 \tabularnewline
13 & 0.02935 & 0.00971397727272727 & 0.0196360227272727 \tabularnewline
14 & 0.02615 & 0.00971397727272727 & 0.0164360227272727 \tabularnewline
15 & 0.00424 & 0.00971397727272727 & -0.00547397727272727 \tabularnewline
16 & -0.00032 & 0.00971397727272727 & -0.0100339772727273 \tabularnewline
17 & -0.02353 & -0.0468714285714286 & 0.0233414285714286 \tabularnewline
18 & 0.01387 & 0.00971397727272727 & 0.00415602272727273 \tabularnewline
19 & 0.01286 & 0.00971397727272727 & 0.00314602272727273 \tabularnewline
20 & -0.00609 & 0.00971397727272727 & -0.0158039772727273 \tabularnewline
21 & 0.00635 & 0.00971397727272727 & -0.00336397727272727 \tabularnewline
22 & 0.02049 & 0.00971397727272727 & 0.0107760227272727 \tabularnewline
23 & 0.00332 & 0.00971397727272727 & -0.00639397727272727 \tabularnewline
24 & 0.00409 & 0.00971397727272727 & -0.00562397727272727 \tabularnewline
25 & 0.02753 & 0.00971397727272727 & 0.0178160227272727 \tabularnewline
26 & 0.01205 & 0.00971397727272727 & 0.00233602272727273 \tabularnewline
27 & 0.01773 & 0.00971397727272727 & 0.00801602272727273 \tabularnewline
28 & -0.00897 & 0.00971397727272727 & -0.0186839772727273 \tabularnewline
29 & -0.01226 & 0.00971397727272727 & -0.0219739772727273 \tabularnewline
30 & 0.00644 & 0.00971397727272727 & -0.00327397727272727 \tabularnewline
31 & -0.00059 & 0.00971397727272727 & -0.0103039772727273 \tabularnewline
32 & 0.01707 & 0.00971397727272727 & 0.00735602272727272 \tabularnewline
33 & -0.00104 & 0.00971397727272727 & -0.0107539772727273 \tabularnewline
34 & 0.01272 & 0.00971397727272727 & 0.00300602272727273 \tabularnewline
35 & 0.01859 & 0.00971397727272727 & 0.00887602272727273 \tabularnewline
36 & 0.03238 & 0.00971397727272727 & 0.0226660227272727 \tabularnewline
37 & 0.03132 & 0.00971397727272727 & 0.0216060227272727 \tabularnewline
38 & 0.01412 & 0.00971397727272727 & 0.00440602272727273 \tabularnewline
39 & 0.00588 & 0.00971397727272727 & -0.00383397727272727 \tabularnewline
40 & 0.05686 & 0.00971397727272727 & 0.0471460227272727 \tabularnewline
41 & 0.05681 & 0.00971397727272727 & 0.0470960227272727 \tabularnewline
42 & -0.04078 & 0.00971397727272727 & -0.0504939772727273 \tabularnewline
43 & 0.02507 & 0.00971397727272727 & 0.0153560227272727 \tabularnewline
44 & 0.006 & 0.00971397727272727 & -0.00371397727272727 \tabularnewline
45 & 0.00249 & 0.00971397727272727 & -0.00722397727272727 \tabularnewline
46 & 0.01885 & 0.00971397727272727 & 0.00913602272727273 \tabularnewline
47 & 0.00125 & 0.00971397727272727 & -0.00846397727272727 \tabularnewline
48 & 0.00695 & 0.00971397727272727 & -0.00276397727272727 \tabularnewline
49 & -0.01563 & 0.00971397727272727 & -0.0253439772727273 \tabularnewline
50 & 0.00814 & 0.00971397727272727 & -0.00157397727272727 \tabularnewline
51 & 0.02368 & 0.00971397727272727 & 0.0139660227272727 \tabularnewline
52 & 0.04099 & 0.00971397727272727 & 0.0312760227272727 \tabularnewline
53 & 0.00731 & 0.00971397727272727 & -0.00240397727272727 \tabularnewline
54 & -0.0173 & 0.00971397727272727 & -0.0270139772727273 \tabularnewline
55 & -0.00183 & 0.00971397727272727 & -0.0115439772727273 \tabularnewline
56 & -0.0383 & 0.00971397727272727 & -0.0480139772727273 \tabularnewline
57 & -0.01249 & 0.00971397727272727 & -0.0222039772727273 \tabularnewline
58 & 0.01229 & 0.00971397727272727 & 0.00257602272727273 \tabularnewline
59 & -0.01747 & 0.00971397727272727 & -0.0271839772727273 \tabularnewline
60 & -0.02645 & 0.00971397727272727 & -0.0361639772727273 \tabularnewline
61 & 0.04038 & 0.00971397727272727 & 0.0306660227272727 \tabularnewline
62 & 0.02925 & 0.00971397727272727 & 0.0195360227272727 \tabularnewline
63 & 0.0227 & 0.00971397727272727 & 0.0129860227272727 \tabularnewline
64 & -0.0046 & 0.00971397727272727 & -0.0143139772727273 \tabularnewline
65 & -0.01894 & 0.00971397727272727 & -0.0286539772727273 \tabularnewline
66 & -0.00966 & 0.00971397727272727 & -0.0193739772727273 \tabularnewline
67 & 0.00392 & 0.00971397727272727 & -0.00579397727272727 \tabularnewline
68 & -0.03105 & -0.0468714285714286 & 0.0158214285714286 \tabularnewline
69 & -0.0279 & 0.00971397727272727 & -0.0376139772727273 \tabularnewline
70 & -0.09625 & -0.0468714285714286 & -0.0493785714285714 \tabularnewline
71 & -0.05388 & -0.0468714285714286 & -0.00700857142857143 \tabularnewline
72 & -0.05034 & -0.0468714285714286 & -0.00346857142857143 \tabularnewline
73 & -0.02846 & -0.0468714285714286 & 0.0184114285714286 \tabularnewline
74 & -0.01454 & 0.00971397727272727 & -0.0242539772727273 \tabularnewline
75 & 0.01284 & 0.00971397727272727 & 0.00312602272727273 \tabularnewline
76 & 0.03762 & 0.00971397727272727 & 0.0279060227272727 \tabularnewline
77 & 0.01973 & 0.00971397727272727 & 0.0100160227272727 \tabularnewline
78 & 0.03178 & 0.00971397727272727 & 0.0220660227272727 \tabularnewline
79 & 0.01329 & 0.00971397727272727 & 0.00357602272727273 \tabularnewline
80 & 0.05094 & 0.00971397727272727 & 0.0412260227272727 \tabularnewline
81 & -0.00804 & 0.00971397727272727 & -0.0177539772727273 \tabularnewline
82 & 0.01116 & 0.00971397727272727 & 0.00144602272727273 \tabularnewline
83 & 0.01128 & 0.00971397727272727 & 0.00156602272727273 \tabularnewline
84 & 0.02227 & 0.00971397727272727 & 0.0125560227272727 \tabularnewline
85 & 0.01494 & 0.00971397727272727 & 0.00522602272727273 \tabularnewline
86 & -0.02514 & 0.00971397727272727 & -0.0348539772727273 \tabularnewline
87 & 0.02975 & 0.00971397727272727 & 0.0200360227272727 \tabularnewline
88 & 0.05216 & 0.00971397727272727 & 0.0424460227272727 \tabularnewline
89 & -0.04459 & -0.0468714285714286 & 0.00228142857142857 \tabularnewline
90 & -0.02212 & 0.00971397727272727 & -0.0318339772727273 \tabularnewline
91 & 0.03171 & 0.00971397727272727 & 0.0219960227272727 \tabularnewline
92 & 0.02985 & 0.00971397727272727 & 0.0201360227272727 \tabularnewline
93 & 0.01545 & 0.00971397727272727 & 0.00573602272727273 \tabularnewline
94 & 0.0114 & 0.00971397727272727 & 0.00168602272727273 \tabularnewline
95 & 0.00238 & 0.00971397727272727 & -0.00733397727272727 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114559&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]0.01244[/C][C]0.00971397727272727[/C][C]0.00272602272727273[/C][/ROW]
[ROW][C]2[/C][C]0.0115[/C][C]0.00971397727272727[/C][C]0.00178602272727273[/C][/ROW]
[ROW][C]3[/C][C]-0.00793[/C][C]0.00971397727272727[/C][C]-0.0176439772727273[/C][/ROW]
[ROW][C]4[/C][C]-0.01514[/C][C]0.00971397727272727[/C][C]-0.0248539772727273[/C][/ROW]
[ROW][C]5[/C][C]0.01778[/C][C]0.00971397727272727[/C][C]0.00806602272727273[/C][/ROW]
[ROW][C]6[/C][C]0.00634[/C][C]0.00971397727272727[/C][C]-0.00337397727272727[/C][/ROW]
[ROW][C]7[/C][C]0.0077[/C][C]0.00971397727272727[/C][C]-0.00201397727272727[/C][/ROW]
[ROW][C]8[/C][C]0.00692[/C][C]0.00971397727272727[/C][C]-0.00279397727272727[/C][/ROW]
[ROW][C]9[/C][C]0.00029[/C][C]0.00971397727272727[/C][C]-0.00942397727272727[/C][/ROW]
[ROW][C]10[/C][C]0.02487[/C][C]0.00971397727272727[/C][C]0.0151560227272727[/C][/ROW]
[ROW][C]11[/C][C]0.01708[/C][C]0.00971397727272727[/C][C]0.00736602272727273[/C][/ROW]
[ROW][C]12[/C][C]0.0254[/C][C]0.00971397727272727[/C][C]0.0156860227272727[/C][/ROW]
[ROW][C]13[/C][C]0.02935[/C][C]0.00971397727272727[/C][C]0.0196360227272727[/C][/ROW]
[ROW][C]14[/C][C]0.02615[/C][C]0.00971397727272727[/C][C]0.0164360227272727[/C][/ROW]
[ROW][C]15[/C][C]0.00424[/C][C]0.00971397727272727[/C][C]-0.00547397727272727[/C][/ROW]
[ROW][C]16[/C][C]-0.00032[/C][C]0.00971397727272727[/C][C]-0.0100339772727273[/C][/ROW]
[ROW][C]17[/C][C]-0.02353[/C][C]-0.0468714285714286[/C][C]0.0233414285714286[/C][/ROW]
[ROW][C]18[/C][C]0.01387[/C][C]0.00971397727272727[/C][C]0.00415602272727273[/C][/ROW]
[ROW][C]19[/C][C]0.01286[/C][C]0.00971397727272727[/C][C]0.00314602272727273[/C][/ROW]
[ROW][C]20[/C][C]-0.00609[/C][C]0.00971397727272727[/C][C]-0.0158039772727273[/C][/ROW]
[ROW][C]21[/C][C]0.00635[/C][C]0.00971397727272727[/C][C]-0.00336397727272727[/C][/ROW]
[ROW][C]22[/C][C]0.02049[/C][C]0.00971397727272727[/C][C]0.0107760227272727[/C][/ROW]
[ROW][C]23[/C][C]0.00332[/C][C]0.00971397727272727[/C][C]-0.00639397727272727[/C][/ROW]
[ROW][C]24[/C][C]0.00409[/C][C]0.00971397727272727[/C][C]-0.00562397727272727[/C][/ROW]
[ROW][C]25[/C][C]0.02753[/C][C]0.00971397727272727[/C][C]0.0178160227272727[/C][/ROW]
[ROW][C]26[/C][C]0.01205[/C][C]0.00971397727272727[/C][C]0.00233602272727273[/C][/ROW]
[ROW][C]27[/C][C]0.01773[/C][C]0.00971397727272727[/C][C]0.00801602272727273[/C][/ROW]
[ROW][C]28[/C][C]-0.00897[/C][C]0.00971397727272727[/C][C]-0.0186839772727273[/C][/ROW]
[ROW][C]29[/C][C]-0.01226[/C][C]0.00971397727272727[/C][C]-0.0219739772727273[/C][/ROW]
[ROW][C]30[/C][C]0.00644[/C][C]0.00971397727272727[/C][C]-0.00327397727272727[/C][/ROW]
[ROW][C]31[/C][C]-0.00059[/C][C]0.00971397727272727[/C][C]-0.0103039772727273[/C][/ROW]
[ROW][C]32[/C][C]0.01707[/C][C]0.00971397727272727[/C][C]0.00735602272727272[/C][/ROW]
[ROW][C]33[/C][C]-0.00104[/C][C]0.00971397727272727[/C][C]-0.0107539772727273[/C][/ROW]
[ROW][C]34[/C][C]0.01272[/C][C]0.00971397727272727[/C][C]0.00300602272727273[/C][/ROW]
[ROW][C]35[/C][C]0.01859[/C][C]0.00971397727272727[/C][C]0.00887602272727273[/C][/ROW]
[ROW][C]36[/C][C]0.03238[/C][C]0.00971397727272727[/C][C]0.0226660227272727[/C][/ROW]
[ROW][C]37[/C][C]0.03132[/C][C]0.00971397727272727[/C][C]0.0216060227272727[/C][/ROW]
[ROW][C]38[/C][C]0.01412[/C][C]0.00971397727272727[/C][C]0.00440602272727273[/C][/ROW]
[ROW][C]39[/C][C]0.00588[/C][C]0.00971397727272727[/C][C]-0.00383397727272727[/C][/ROW]
[ROW][C]40[/C][C]0.05686[/C][C]0.00971397727272727[/C][C]0.0471460227272727[/C][/ROW]
[ROW][C]41[/C][C]0.05681[/C][C]0.00971397727272727[/C][C]0.0470960227272727[/C][/ROW]
[ROW][C]42[/C][C]-0.04078[/C][C]0.00971397727272727[/C][C]-0.0504939772727273[/C][/ROW]
[ROW][C]43[/C][C]0.02507[/C][C]0.00971397727272727[/C][C]0.0153560227272727[/C][/ROW]
[ROW][C]44[/C][C]0.006[/C][C]0.00971397727272727[/C][C]-0.00371397727272727[/C][/ROW]
[ROW][C]45[/C][C]0.00249[/C][C]0.00971397727272727[/C][C]-0.00722397727272727[/C][/ROW]
[ROW][C]46[/C][C]0.01885[/C][C]0.00971397727272727[/C][C]0.00913602272727273[/C][/ROW]
[ROW][C]47[/C][C]0.00125[/C][C]0.00971397727272727[/C][C]-0.00846397727272727[/C][/ROW]
[ROW][C]48[/C][C]0.00695[/C][C]0.00971397727272727[/C][C]-0.00276397727272727[/C][/ROW]
[ROW][C]49[/C][C]-0.01563[/C][C]0.00971397727272727[/C][C]-0.0253439772727273[/C][/ROW]
[ROW][C]50[/C][C]0.00814[/C][C]0.00971397727272727[/C][C]-0.00157397727272727[/C][/ROW]
[ROW][C]51[/C][C]0.02368[/C][C]0.00971397727272727[/C][C]0.0139660227272727[/C][/ROW]
[ROW][C]52[/C][C]0.04099[/C][C]0.00971397727272727[/C][C]0.0312760227272727[/C][/ROW]
[ROW][C]53[/C][C]0.00731[/C][C]0.00971397727272727[/C][C]-0.00240397727272727[/C][/ROW]
[ROW][C]54[/C][C]-0.0173[/C][C]0.00971397727272727[/C][C]-0.0270139772727273[/C][/ROW]
[ROW][C]55[/C][C]-0.00183[/C][C]0.00971397727272727[/C][C]-0.0115439772727273[/C][/ROW]
[ROW][C]56[/C][C]-0.0383[/C][C]0.00971397727272727[/C][C]-0.0480139772727273[/C][/ROW]
[ROW][C]57[/C][C]-0.01249[/C][C]0.00971397727272727[/C][C]-0.0222039772727273[/C][/ROW]
[ROW][C]58[/C][C]0.01229[/C][C]0.00971397727272727[/C][C]0.00257602272727273[/C][/ROW]
[ROW][C]59[/C][C]-0.01747[/C][C]0.00971397727272727[/C][C]-0.0271839772727273[/C][/ROW]
[ROW][C]60[/C][C]-0.02645[/C][C]0.00971397727272727[/C][C]-0.0361639772727273[/C][/ROW]
[ROW][C]61[/C][C]0.04038[/C][C]0.00971397727272727[/C][C]0.0306660227272727[/C][/ROW]
[ROW][C]62[/C][C]0.02925[/C][C]0.00971397727272727[/C][C]0.0195360227272727[/C][/ROW]
[ROW][C]63[/C][C]0.0227[/C][C]0.00971397727272727[/C][C]0.0129860227272727[/C][/ROW]
[ROW][C]64[/C][C]-0.0046[/C][C]0.00971397727272727[/C][C]-0.0143139772727273[/C][/ROW]
[ROW][C]65[/C][C]-0.01894[/C][C]0.00971397727272727[/C][C]-0.0286539772727273[/C][/ROW]
[ROW][C]66[/C][C]-0.00966[/C][C]0.00971397727272727[/C][C]-0.0193739772727273[/C][/ROW]
[ROW][C]67[/C][C]0.00392[/C][C]0.00971397727272727[/C][C]-0.00579397727272727[/C][/ROW]
[ROW][C]68[/C][C]-0.03105[/C][C]-0.0468714285714286[/C][C]0.0158214285714286[/C][/ROW]
[ROW][C]69[/C][C]-0.0279[/C][C]0.00971397727272727[/C][C]-0.0376139772727273[/C][/ROW]
[ROW][C]70[/C][C]-0.09625[/C][C]-0.0468714285714286[/C][C]-0.0493785714285714[/C][/ROW]
[ROW][C]71[/C][C]-0.05388[/C][C]-0.0468714285714286[/C][C]-0.00700857142857143[/C][/ROW]
[ROW][C]72[/C][C]-0.05034[/C][C]-0.0468714285714286[/C][C]-0.00346857142857143[/C][/ROW]
[ROW][C]73[/C][C]-0.02846[/C][C]-0.0468714285714286[/C][C]0.0184114285714286[/C][/ROW]
[ROW][C]74[/C][C]-0.01454[/C][C]0.00971397727272727[/C][C]-0.0242539772727273[/C][/ROW]
[ROW][C]75[/C][C]0.01284[/C][C]0.00971397727272727[/C][C]0.00312602272727273[/C][/ROW]
[ROW][C]76[/C][C]0.03762[/C][C]0.00971397727272727[/C][C]0.0279060227272727[/C][/ROW]
[ROW][C]77[/C][C]0.01973[/C][C]0.00971397727272727[/C][C]0.0100160227272727[/C][/ROW]
[ROW][C]78[/C][C]0.03178[/C][C]0.00971397727272727[/C][C]0.0220660227272727[/C][/ROW]
[ROW][C]79[/C][C]0.01329[/C][C]0.00971397727272727[/C][C]0.00357602272727273[/C][/ROW]
[ROW][C]80[/C][C]0.05094[/C][C]0.00971397727272727[/C][C]0.0412260227272727[/C][/ROW]
[ROW][C]81[/C][C]-0.00804[/C][C]0.00971397727272727[/C][C]-0.0177539772727273[/C][/ROW]
[ROW][C]82[/C][C]0.01116[/C][C]0.00971397727272727[/C][C]0.00144602272727273[/C][/ROW]
[ROW][C]83[/C][C]0.01128[/C][C]0.00971397727272727[/C][C]0.00156602272727273[/C][/ROW]
[ROW][C]84[/C][C]0.02227[/C][C]0.00971397727272727[/C][C]0.0125560227272727[/C][/ROW]
[ROW][C]85[/C][C]0.01494[/C][C]0.00971397727272727[/C][C]0.00522602272727273[/C][/ROW]
[ROW][C]86[/C][C]-0.02514[/C][C]0.00971397727272727[/C][C]-0.0348539772727273[/C][/ROW]
[ROW][C]87[/C][C]0.02975[/C][C]0.00971397727272727[/C][C]0.0200360227272727[/C][/ROW]
[ROW][C]88[/C][C]0.05216[/C][C]0.00971397727272727[/C][C]0.0424460227272727[/C][/ROW]
[ROW][C]89[/C][C]-0.04459[/C][C]-0.0468714285714286[/C][C]0.00228142857142857[/C][/ROW]
[ROW][C]90[/C][C]-0.02212[/C][C]0.00971397727272727[/C][C]-0.0318339772727273[/C][/ROW]
[ROW][C]91[/C][C]0.03171[/C][C]0.00971397727272727[/C][C]0.0219960227272727[/C][/ROW]
[ROW][C]92[/C][C]0.02985[/C][C]0.00971397727272727[/C][C]0.0201360227272727[/C][/ROW]
[ROW][C]93[/C][C]0.01545[/C][C]0.00971397727272727[/C][C]0.00573602272727273[/C][/ROW]
[ROW][C]94[/C][C]0.0114[/C][C]0.00971397727272727[/C][C]0.00168602272727273[/C][/ROW]
[ROW][C]95[/C][C]0.00238[/C][C]0.00971397727272727[/C][C]-0.00733397727272727[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114559&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114559&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
10.012440.009713977272727270.00272602272727273
20.01150.009713977272727270.00178602272727273
3-0.007930.00971397727272727-0.0176439772727273
4-0.015140.00971397727272727-0.0248539772727273
50.017780.009713977272727270.00806602272727273
60.006340.00971397727272727-0.00337397727272727
70.00770.00971397727272727-0.00201397727272727
80.006920.00971397727272727-0.00279397727272727
90.000290.00971397727272727-0.00942397727272727
100.024870.009713977272727270.0151560227272727
110.017080.009713977272727270.00736602272727273
120.02540.009713977272727270.0156860227272727
130.029350.009713977272727270.0196360227272727
140.026150.009713977272727270.0164360227272727
150.004240.00971397727272727-0.00547397727272727
16-0.000320.00971397727272727-0.0100339772727273
17-0.02353-0.04687142857142860.0233414285714286
180.013870.009713977272727270.00415602272727273
190.012860.009713977272727270.00314602272727273
20-0.006090.00971397727272727-0.0158039772727273
210.006350.00971397727272727-0.00336397727272727
220.020490.009713977272727270.0107760227272727
230.003320.00971397727272727-0.00639397727272727
240.004090.00971397727272727-0.00562397727272727
250.027530.009713977272727270.0178160227272727
260.012050.009713977272727270.00233602272727273
270.017730.009713977272727270.00801602272727273
28-0.008970.00971397727272727-0.0186839772727273
29-0.012260.00971397727272727-0.0219739772727273
300.006440.00971397727272727-0.00327397727272727
31-0.000590.00971397727272727-0.0103039772727273
320.017070.009713977272727270.00735602272727272
33-0.001040.00971397727272727-0.0107539772727273
340.012720.009713977272727270.00300602272727273
350.018590.009713977272727270.00887602272727273
360.032380.009713977272727270.0226660227272727
370.031320.009713977272727270.0216060227272727
380.014120.009713977272727270.00440602272727273
390.005880.00971397727272727-0.00383397727272727
400.056860.009713977272727270.0471460227272727
410.056810.009713977272727270.0470960227272727
42-0.040780.00971397727272727-0.0504939772727273
430.025070.009713977272727270.0153560227272727
440.0060.00971397727272727-0.00371397727272727
450.002490.00971397727272727-0.00722397727272727
460.018850.009713977272727270.00913602272727273
470.001250.00971397727272727-0.00846397727272727
480.006950.00971397727272727-0.00276397727272727
49-0.015630.00971397727272727-0.0253439772727273
500.008140.00971397727272727-0.00157397727272727
510.023680.009713977272727270.0139660227272727
520.040990.009713977272727270.0312760227272727
530.007310.00971397727272727-0.00240397727272727
54-0.01730.00971397727272727-0.0270139772727273
55-0.001830.00971397727272727-0.0115439772727273
56-0.03830.00971397727272727-0.0480139772727273
57-0.012490.00971397727272727-0.0222039772727273
580.012290.009713977272727270.00257602272727273
59-0.017470.00971397727272727-0.0271839772727273
60-0.026450.00971397727272727-0.0361639772727273
610.040380.009713977272727270.0306660227272727
620.029250.009713977272727270.0195360227272727
630.02270.009713977272727270.0129860227272727
64-0.00460.00971397727272727-0.0143139772727273
65-0.018940.00971397727272727-0.0286539772727273
66-0.009660.00971397727272727-0.0193739772727273
670.003920.00971397727272727-0.00579397727272727
68-0.03105-0.04687142857142860.0158214285714286
69-0.02790.00971397727272727-0.0376139772727273
70-0.09625-0.0468714285714286-0.0493785714285714
71-0.05388-0.0468714285714286-0.00700857142857143
72-0.05034-0.0468714285714286-0.00346857142857143
73-0.02846-0.04687142857142860.0184114285714286
74-0.014540.00971397727272727-0.0242539772727273
750.012840.009713977272727270.00312602272727273
760.037620.009713977272727270.0279060227272727
770.019730.009713977272727270.0100160227272727
780.031780.009713977272727270.0220660227272727
790.013290.009713977272727270.00357602272727273
800.050940.009713977272727270.0412260227272727
81-0.008040.00971397727272727-0.0177539772727273
820.011160.009713977272727270.00144602272727273
830.011280.009713977272727270.00156602272727273
840.022270.009713977272727270.0125560227272727
850.014940.009713977272727270.00522602272727273
86-0.025140.00971397727272727-0.0348539772727273
870.029750.009713977272727270.0200360227272727
880.052160.009713977272727270.0424460227272727
89-0.04459-0.04687142857142860.00228142857142857
90-0.022120.00971397727272727-0.0318339772727273
910.031710.009713977272727270.0219960227272727
920.029850.009713977272727270.0201360227272727
930.015450.009713977272727270.00573602272727273
940.01140.009713977272727270.00168602272727273
950.002380.00971397727272727-0.00733397727272727


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 1 ; 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')
}