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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 computationWed, 22 Dec 2010 13:40:54 +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/t1293025136o1pefg5t5asvf6a.htm/, Retrieved Mon, 06 May 2024 06:13:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114202, Retrieved Mon, 06 May 2024 06:13:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact109

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD    [Recursive Partitioning (Regression Trees)] [] [2010-12-22 13:40:54] [23ca1b0f6f6de1e008a90be3f55e3db8] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1,4562	8,1000	7,9000	8,7000	104,5000	2443,2700	16,2000	16,3000	3,0000	-12,0000	65,0000
1,4268	8,3000	8,1000	8,9000	89,1000	2293,4100	12,5000	13,6000	6,0000	-11,0000	55,0000
1,4088	8,1000	8,3000	8,9000	82,6000	2070,8300	14,8000	14,3000	7,0000	-11,0000	57,0000
1,4016	7,4000	8,1000	8,1000	102,7000	2029,6000	15,4000	15,5000	-4,0000	-17,0000	57,0000
1,3650	7,3000	7,4000	8,0000	91,8000	2052,0200	13,6000	13,9000	-5,0000	-18,0000	57,0000
1,3190	7,7000	7,3000	8,3000	94,1000	1864,4400	14,2000	14,3000	-7,0000	-19,0000	65,0000
1,3050	8,0000	7,7000	8,5000	103,1000	1670,0700	15,0000	15,8000	-10,0000	-22,0000	69,0000
1,2785	8,0000	8,0000	8,7000	93,2000	1810,9900	14,1000	14,5000	-21,0000	-24,0000	70,0000
1,3239	7,7000	8,0000	8,6000	91,0000	1905,4100	13,7000	15,1000	-22,0000	-24,0000	71,0000
1,3449	6,9000	7,7000	8,3000	94,3000	1862,8300	14,4000	15,8000	-16,0000	-20,0000	71,0000
1,2732	6,6000	6,9000	7,9000	99,4000	2014,4500	15,6000	17,2000	-25,0000	-25,0000	73,0000
1,3322	6,9000	6,6000	7,9000	115,7000	2197,8200	19,7000	20,4000	-22,0000	-22,0000	68,0000
1,4369	7,5000	6,9000	8,1000	116,8000	2962,3400	20,4000	21,3000	-22,0000	-17,0000	65,0000
1,4975	7,9000	7,5000	8,3000	99,8000	3047,0300	16,1000	18,2000	-19,0000	-9,0000	57,0000
1,5770	7,7000	7,9000	8,1000	96,0000	3032,6000	20,1000	20,2000	-21,0000	-11,0000	41,0000
1,5553	6,5000	7,7000	7,4000	115,9000	3504,3700	20,6000	21,1000	-31,0000	-13,0000	21,0000
1,5557	6,1000	6,5000	7,3000	109,1000	3801,0600	19,3000	19,7000	-28,0000	-11,0000	21,0000
1,5750	6,4000	6,1000	7,7000	117,3000	3857,6200	20,5000	21,5000	-23,0000	-9,0000	17,0000
1,5527	6,8000	6,4000	8,0000	109,8000	3674,4000	19,2000	20,2000	-17,0000	-7,0000	9,0000
1,4748	7,1000	6,8000	8,0000	112,8000	3720,9800	19,0000	19,0000	-12,0000	-3,0000	11,0000
1,4718	7,3000	7,1000	7,7000	110,7000	3844,4900	18,7000	20,2000	-14,0000	-3,0000	6,0000
1,4570	7,2000	7,3000	6,9000	100,0000	4116,6800	16,5000	18,0000	-18,0000	-6,0000	-2,0000
1,4684	7,0000	7,2000	6,6000	113,3000	4105,1800	19,0000	19,5000	-16,0000	-4,0000	0,0000
1,4227	7,0000	7,0000	6,9000	122,4000	4435,2300	20,5000	20,3000	-22,0000	-8,0000	5,0000
1,3896	7,0000	7,0000	7,5000	112,5000	4296,4900	18,4000	18,0000	-9,0000	-1,0000	3,0000
1,3622	7,3000	7,0000	7,9000	104,2000	4202,5200	16,2000	16,4000	-10,0000	-2,0000	7,0000
1,3716	7,5000	7,3000	7,7000	92,5000	4562,8400	18,1000	17,8000	-10,0000	-2,0000	4,0000
1,3419	7,2000	7,5000	6,5000	117,2000	4621,4000	19,3000	18,5000	0,0000	-1,0000	8,0000
1,3511	7,7000	7,2000	6,1000	109,3000	4696,9600	18,3000	18,2000	3,0000	1,0000	9,0000
1,3516	8,0000	7,7000	6,4000	106,1000	4591,2700	17,2000	16,7000	2,0000	2,0000	14,0000
1,3242	7,9000	8,0000	6,8000	118,8000	4356,9800	19,6000	19,1000	4,0000	2,0000	12,0000
1,3074	8,0000	7,9000	7,1000	105,3000	4502,6400	17,2000	16,8000	-3,0000	-1,0000	12,0000
1,2999	8,0000	8,0000	7,3000	106,0000	4443,9100	17,4000	17,5000	0,0000	1,0000	7,0000
1,3213	7,9000	8,0000	7,2000	102,0000	4290,8900	16,0000	16,2000	-1,0000	-1,0000	15,0000
1,2881	7,9000	7,9000	7,0000	112,9000	4199,7500	18,5000	17,9000	-7,0000	-8,0000	14,0000
1,2611	8,0000	7,9000	7,0000	116,5000	4138,5200	18,4000	17,7000	2,0000	1,0000	19,0000
1,2727	8,1000	8,0000	7,0000	114,8000	3970,1000	18,2000	17,2000	3,0000	2,0000	39,0000
1,2811	8,1000	8,1000	7,3000	100,5000	3862,2700	14,9000	15,7000	-3,0000	-2,0000	12,0000
1,2684	8,2000	8,1000	7,5000	85,4000	3701,6100	16,3000	15,2000	-5,0000	-2,0000	11,0000
1,2650	8,0000	8,2000	7,2000	114,6000	3570,1200	18,3000	17,7000	0,0000	-2,0000	17,0000
1,2770	8,3000	8,0000	7,7000	109,9000	3801,0600	18,0000	17,4000	-3,0000	-2,0000	16,0000
1,2271	8,5000	8,3000	8,0000	100,7000	3895,5100	15,9000	15,9000	-7,0000	-6,0000	25,0000
1,2020	8,6000	8,5000	7,9000	115,5000	3917,9600	19,6000	19,7000	-7,0000	-4,0000	24,0000
1,1938	8,7000	8,6000	8,0000	100,7000	3813,0600	16,6000	16,7000	-7,0000	-5,0000	28,0000
1,2103	8,7000	8,7000	8,0000	99,0000	3667,0300	16,2000	16,9000	-4,0000	-2,0000	25,0000
1,1856	8,5000	8,7000	7,9000	102,3000	3494,1700	16,6000	18,0000	-3,0000	-1,0000	31,0000
1,1786	8,4000	8,5000	7,9000	108,8000	3363,9900	17,5000	17,6000	-6,0000	-5,0000	24,0000
1,2015	8,5000	8,4000	8,0000	105,9000	3295,3200	16,2000	15,2000	-10,0000	-9,0000	24,0000
1,2256	8,7000	8,5000	8,1000	113,2000	3277,0100	17,5000	16,5000	-10,0000	-8,0000	33,0000
1,2292	8,7000	8,7000	8,1000	95,7000	3257,1600	13,8000	14,7000	-23,0000	-14,0000	37,0000
1,2037	8,6000	8,7000	8,2000	80,9000	3161,6900	14,9000	14,1000	-13,0000	-10,0000	35,0000
1,2165	7,9000	8,6000	8,0000	113,9000	3097,3100	17,2000	16,9000	-18,0000	-11,0000	37,0000
1,2694	8,1000	7,9000	8,3000	98,1000	3061,2600	15,6000	15,2000	-16,0000	-11,0000	38,0000
1,2938	8,2000	8,1000	8,5000	102,8000	3119,3100	16,2000	15,4000	-15,0000	-11,0000	42,0000
1,3201	8,5000	8,2000	8,6000	104,7000	3106,2200	17,4000	16,8000	-5,0000	-5,0000	43,0000
1,3014	8,6000	8,5000	8,7000	95,9000	3080,5800	15,1000	14,8000	2,0000	-2,0000	44,0000
1,3119	8,5000	8,6000	8,7000	94,6000	2981,8500	14,5000	14,1000	-2,0000	-3,0000	32,0000
1,3408	8,3000	8,5000	8,5000	101,6000	2921,4400	15,1000	15,0000	-4,0000	-6,0000	32,0000
1,2991	8,2000	8,3000	8,4000	103,9000	2849,2700	15,5000	14,8000	-4,0000	-6,0000	37,0000
1,2490	8,7000	8,2000	8,5000	110,3000	2756,7600	15,9000	15,0000	-6,0000	-7,0000	38,0000
1,2218	9,3000	8,7000	8,7000	114,1000	2645,6400	15,9000	15,1000	-7,0000	-6,0000	39,0000
1,2176	9,3000	9,3000	8,7000	96,8000	2497,8400	12,3000	12,8000	0,0000	-2,0000	38,0000
1,2266	8,8000	9,3000	8,6000	87,4000	2448,0500	14,4000	13,0000	1,0000	-2,0000	39,0000
1,2138	7,4000	8,8000	7,9000	111,4000	2454,6200	16,0000	15,7000	-3,0000	-4,0000	30,0000
1,2007	7,2000	7,4000	8,1000	97,4000	2407,6000	13,9000	12,8000	6,0000	0,0000	28,0000
1,1985	7,5000	7,2000	8,2000	102,9000	2472,8100	14,7000	13,9000	-2,0000	-6,0000	31,0000
1,2262	8,3000	7,5000	8,5000	112,7000	2408,6400	16,2000	15,4000	2,0000	-4,0000	28,0000
1,2646	8,8000	8,3000	8,6000	97,0000	2440,2500	13,8000	13,2000	5,0000	-3,0000	38,0000
1,2613	8,9000	8,8000	8,5000	95,1000	2350,4400	13,2000	12,7000	7,0000	-1,0000	37,0000
1,2286	8,6000	8,9000	8,3000	96,9000	2196,7200	13,5000	13,5000	4,0000	-3,0000	34,0000
1,1702	8,4000	8,6000	8,2000	98,6000	2174,5600	13,5000	12,8000	0,0000	-6,0000	32,0000
1,1692	8,4000	8,4000	8,7000	111,7000	2120,8800	15,0000	13,9000	0,0000	-6,0000	33,0000
1,1222	8,4000	8,4000	9,3000	109,8000	2093,4800	14,5000	13,3000	-13,0000	-15,0000	39,0000
1,1139	8,4000	8,4000	9,3000	89,9000	2061,4100	10,5000	10,7000	-2,0000	-5,0000	42,0000
1,1372	8,3000	8,4000	8,8000	87,4000	1969,6000	13,7000	12,3000	-10,0000	-11,0000	57,0000
1,1663	7,6000	8,3000	7,4000	104,5000	1959,6700	13,9000	12,9000	-12,0000	-13,0000	36,0000
1,1582	7,6000	7,6000	7,2000	98,1000	1910,4300	13,4000	12,5000	-9,0000	-10,0000	42,0000
1,0848	7,9000	7,6000	7,5000	102,7000	1833,4200	14,0000	13,0000	-4,0000	-9,0000	49,0000
1,0807	8,0000	7,9000	8,3000	105,4000	1635,2500	14,3000	13,9000	-11,0000	-11,0000	44,0000
1,0773	8,2000	8,0000	8,8000	97,0000	1765,9000	13,3000	13,1000	-28,0000	-18,0000	44,0000
1,0622	8,3000	8,2000	8,9000	97,4000	1946,8100	13,2000	13,1000	-19,0000	-13,0000	43,0000
1,0183	8,2000	8,3000	8,6000	92,0000	1995,3700	12,6000	13,0000	-16,0000	-9,0000	50,0000
1,0014	8,1000	8,2000	8,4000	101,7000	2042,0000	13,7000	12,8000	-8,0000	-8,0000	45,0000
0,9811	8,0000	8,1000	8,4000	112,6000	1940,4900	15,6000	14,2000	-1,0000	-4,0000	40,0000
0,9808	7,8000	8,0000	8,4000	106,9000	2065,8100	14,4000	13,0000	-2,0000	-3,0000	38,0000
0,9778	7,6000	7,8000	8,4000	92,1000	2214,9500	11,0000	11,2000	-4,0000	-3,0000	29,0000
0,9922	7,5000	7,6000	8,3000	86,0000	2304,9800	13,7000	12,1000	-5,0000	-3,0000	27,0000
0,9554	6,8000	7,5000	7,6000	104,7000	2555,2800	13,8000	12,9000	0,0000	-1,0000	27,0000
0,9170	6,9000	6,8000	7,6000	102,0000	2799,4300	14,3000	13,2000	5,0000	0,0000	27,0000
0,8858	7,1000	6,9000	7,9000	103,1000	2811,7000	14,0000	13,2000	5,0000	1,0000	32,0000
0,8758	7,3000	7,1000	8,0000	106,0000	2735,7000	14,6000	13,5000	2,0000	0,0000	24,0000
0,8700	7,4000	7,3000	8,2000	96,1000	2745,8800	13,1000	12,4000	6,0000	2,0000	22,0000
0,8833	7,6000	7,4000	8,3000	96,2000	2720,2500	13,2000	12,4000	3,0000	1,0000	22,0000
0,8924	7,6000	7,6000	8,2000	90,7000	2638,5300	11,6000	11,6000	1,0000	-1,0000	23,0000
0,8883	7,5000	7,6000	8,1000	102,3000	2659,8100	13,3000	12,6000	-9,0000	-8,0000	23,0000
0,9059	7,5000	7,5000	8,0000	109,4000	2641,6500	14,4000	13,1000	-26,0000	-18,0000	28,0000
0,9111	6,8000	7,5000	7,8000	101,0000	2604,4200	13,3000	12,3000	-25,0000	-14,0000	36,0000
0,9005	6,4000	6,8000	7,6000	94,7000	2892,6300	11,3000	11,4000	-13,0000	-4,0000	60,0000
0,8607	6,2000	6,4000	7,5000	81,0000	2915,0300	13,2000	11,8000	-6,0000	0,0000	43,0000
0,8532	6,0000	6,2000	6,8000	106,2000	2845,2600	14,1000	13,4000	-1,0000	4,0000	23,0000
0,8742	6,3000	6,0000	6,9000	101,9000	2794,8300	14,0000	13,6000	1,0000	4,0000	15,0000
0,8920	6,3000	6,3000	7,1000	96,4000	2848,9600	12,9000	12,9000	1,0000	3,0000	7,0000
0,9095	6,1000	6,3000	7,3000	110,4000	2833,1800	15,2000	14,5000	-2,0000	3,0000	6,0000
0,9217	6,1000	6,1000	7,4000	100,5000	2995,5500	13,6000	13,3000	2,0000	7,0000	8,0000
0,9383	6,3000	6,1000	7,6000	98,8000	2987,1000	13,7000	13,5000	3,0000	8,0000	5,0000

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114202&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114202&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114202&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Goodness of Fit
Correlation0.8856
R-squared0.7842
RMSE0.3551

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8856 \tabularnewline
R-squared & 0.7842 \tabularnewline
RMSE & 0.3551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114202&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8856[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7842[/C][/ROW]
[ROW][C]RMSE[/C][C]0.3551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114202&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114202&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
Correlation0.8856
R-squared0.7842
RMSE0.3551






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
18.17.995454545454550.104545454545454
28.37.995454545454550.304545454545456
38.18.45428571428571-0.354285714285714
47.47.99545454545455-0.595454545454545
57.37.3764705882353-0.0764705882352947
67.77.37647058823530.323529411764706
787.37647058823530.623529411764705
887.995454545454550.00454545454545485
97.77.99545454545455-0.295454545454545
106.97.3764705882353-0.476470588235294
116.67.3764705882353-0.776470588235295
126.96.421428571428570.478571428571429
137.57.37647058823530.123529411764705
147.97.37647058823530.523529411764706
157.77.99545454545455-0.295454545454545
166.57.3764705882353-0.876470588235295
176.16.42142857142857-0.321428571428572
186.46.42142857142857-0.0214285714285714
196.86.421428571428570.378571428571428
207.16.421428571428570.678571428571428
217.37.3764705882353-0.0764705882352947
227.27.3764705882353-0.176470588235294
2377.3764705882353-0.376470588235295
2477.3764705882353-0.376470588235295
2577.3764705882353-0.376470588235295
267.37.3764705882353-0.0764705882352947
277.57.37647058823530.123529411764705
287.27.3764705882353-0.176470588235294
297.77.37647058823530.323529411764706
3087.37647058823530.623529411764705
317.97.99545454545455-0.0954545454545448
3287.995454545454550.00454545454545485
3387.995454545454550.00454545454545485
347.97.99545454545455-0.0954545454545448
357.97.99545454545455-0.0954545454545448
3687.995454545454550.00454545454545485
378.17.995454545454550.104545454545454
388.17.995454545454550.104545454545454
398.27.995454545454550.204545454545454
4088.45428571428571-0.454285714285714
418.37.995454545454550.304545454545456
428.58.454285714285710.0457142857142863
438.68.454285714285710.145714285714286
448.78.454285714285710.245714285714286
458.78.454285714285710.245714285714286
468.58.454285714285710.0457142857142863
478.48.45428571428571-0.0542857142857134
488.58.454285714285710.0457142857142863
498.78.454285714285710.245714285714286
508.78.454285714285710.245714285714286
518.68.454285714285710.145714285714286
527.98.45428571428571-0.554285714285713
538.17.995454545454550.104545454545454
548.27.995454545454550.204545454545454
558.58.454285714285710.0457142857142863
568.68.454285714285710.145714285714286
578.58.454285714285710.0457142857142863
588.38.45428571428571-0.154285714285713
598.28.45428571428571-0.254285714285714
608.78.454285714285710.245714285714286
619.38.454285714285710.845714285714287
629.38.454285714285710.845714285714287
638.88.454285714285710.345714285714287
647.48.45428571428571-1.05428571428571
657.27.3764705882353-0.176470588235294
667.57.37647058823530.123529411764705
678.37.37647058823530.923529411764706
688.88.454285714285710.345714285714287
698.98.454285714285710.445714285714287
708.68.454285714285710.145714285714286
718.48.45428571428571-0.0542857142857134
728.48.45428571428571-0.0542857142857134
738.48.45428571428571-0.0542857142857134
748.48.45428571428571-0.0542857142857134
758.38.45428571428571-0.154285714285713
767.68.45428571428571-0.854285714285714
777.67.37647058823530.223529411764705
787.97.37647058823530.523529411764706
7987.995454545454550.00454545454545485
808.27.995454545454550.204545454545454
818.38.45428571428571-0.154285714285713
828.28.45428571428571-0.254285714285714
838.18.45428571428571-0.354285714285714
8487.995454545454550.00454545454545485
857.87.99545454545455-0.195454545454545
867.67.37647058823530.223529411764705
877.57.37647058823530.123529411764705
886.87.3764705882353-0.576470588235295
896.96.421428571428570.478571428571429
907.17.3764705882353-0.276470588235295
917.37.3764705882353-0.0764705882352947
927.47.37647058823530.0235294117647058
937.67.37647058823530.223529411764705
947.67.37647058823530.223529411764705
957.57.37647058823530.123529411764705
967.57.37647058823530.123529411764705
976.87.3764705882353-0.576470588235295
986.46.42142857142857-0.0214285714285714
996.26.42142857142857-0.221428571428572
10066.42142857142857-0.421428571428572
1016.36.42142857142857-0.121428571428572
1026.36.42142857142857-0.121428571428572
1036.16.42142857142857-0.321428571428572
1046.16.42142857142857-0.321428571428572
1056.36.42142857142857-0.121428571428572

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 8.1 & 7.99545454545455 & 0.104545454545454 \tabularnewline
2 & 8.3 & 7.99545454545455 & 0.304545454545456 \tabularnewline
3 & 8.1 & 8.45428571428571 & -0.354285714285714 \tabularnewline
4 & 7.4 & 7.99545454545455 & -0.595454545454545 \tabularnewline
5 & 7.3 & 7.3764705882353 & -0.0764705882352947 \tabularnewline
6 & 7.7 & 7.3764705882353 & 0.323529411764706 \tabularnewline
7 & 8 & 7.3764705882353 & 0.623529411764705 \tabularnewline
8 & 8 & 7.99545454545455 & 0.00454545454545485 \tabularnewline
9 & 7.7 & 7.99545454545455 & -0.295454545454545 \tabularnewline
10 & 6.9 & 7.3764705882353 & -0.476470588235294 \tabularnewline
11 & 6.6 & 7.3764705882353 & -0.776470588235295 \tabularnewline
12 & 6.9 & 6.42142857142857 & 0.478571428571429 \tabularnewline
13 & 7.5 & 7.3764705882353 & 0.123529411764705 \tabularnewline
14 & 7.9 & 7.3764705882353 & 0.523529411764706 \tabularnewline
15 & 7.7 & 7.99545454545455 & -0.295454545454545 \tabularnewline
16 & 6.5 & 7.3764705882353 & -0.876470588235295 \tabularnewline
17 & 6.1 & 6.42142857142857 & -0.321428571428572 \tabularnewline
18 & 6.4 & 6.42142857142857 & -0.0214285714285714 \tabularnewline
19 & 6.8 & 6.42142857142857 & 0.378571428571428 \tabularnewline
20 & 7.1 & 6.42142857142857 & 0.678571428571428 \tabularnewline
21 & 7.3 & 7.3764705882353 & -0.0764705882352947 \tabularnewline
22 & 7.2 & 7.3764705882353 & -0.176470588235294 \tabularnewline
23 & 7 & 7.3764705882353 & -0.376470588235295 \tabularnewline
24 & 7 & 7.3764705882353 & -0.376470588235295 \tabularnewline
25 & 7 & 7.3764705882353 & -0.376470588235295 \tabularnewline
26 & 7.3 & 7.3764705882353 & -0.0764705882352947 \tabularnewline
27 & 7.5 & 7.3764705882353 & 0.123529411764705 \tabularnewline
28 & 7.2 & 7.3764705882353 & -0.176470588235294 \tabularnewline
29 & 7.7 & 7.3764705882353 & 0.323529411764706 \tabularnewline
30 & 8 & 7.3764705882353 & 0.623529411764705 \tabularnewline
31 & 7.9 & 7.99545454545455 & -0.0954545454545448 \tabularnewline
32 & 8 & 7.99545454545455 & 0.00454545454545485 \tabularnewline
33 & 8 & 7.99545454545455 & 0.00454545454545485 \tabularnewline
34 & 7.9 & 7.99545454545455 & -0.0954545454545448 \tabularnewline
35 & 7.9 & 7.99545454545455 & -0.0954545454545448 \tabularnewline
36 & 8 & 7.99545454545455 & 0.00454545454545485 \tabularnewline
37 & 8.1 & 7.99545454545455 & 0.104545454545454 \tabularnewline
38 & 8.1 & 7.99545454545455 & 0.104545454545454 \tabularnewline
39 & 8.2 & 7.99545454545455 & 0.204545454545454 \tabularnewline
40 & 8 & 8.45428571428571 & -0.454285714285714 \tabularnewline
41 & 8.3 & 7.99545454545455 & 0.304545454545456 \tabularnewline
42 & 8.5 & 8.45428571428571 & 0.0457142857142863 \tabularnewline
43 & 8.6 & 8.45428571428571 & 0.145714285714286 \tabularnewline
44 & 8.7 & 8.45428571428571 & 0.245714285714286 \tabularnewline
45 & 8.7 & 8.45428571428571 & 0.245714285714286 \tabularnewline
46 & 8.5 & 8.45428571428571 & 0.0457142857142863 \tabularnewline
47 & 8.4 & 8.45428571428571 & -0.0542857142857134 \tabularnewline
48 & 8.5 & 8.45428571428571 & 0.0457142857142863 \tabularnewline
49 & 8.7 & 8.45428571428571 & 0.245714285714286 \tabularnewline
50 & 8.7 & 8.45428571428571 & 0.245714285714286 \tabularnewline
51 & 8.6 & 8.45428571428571 & 0.145714285714286 \tabularnewline
52 & 7.9 & 8.45428571428571 & -0.554285714285713 \tabularnewline
53 & 8.1 & 7.99545454545455 & 0.104545454545454 \tabularnewline
54 & 8.2 & 7.99545454545455 & 0.204545454545454 \tabularnewline
55 & 8.5 & 8.45428571428571 & 0.0457142857142863 \tabularnewline
56 & 8.6 & 8.45428571428571 & 0.145714285714286 \tabularnewline
57 & 8.5 & 8.45428571428571 & 0.0457142857142863 \tabularnewline
58 & 8.3 & 8.45428571428571 & -0.154285714285713 \tabularnewline
59 & 8.2 & 8.45428571428571 & -0.254285714285714 \tabularnewline
60 & 8.7 & 8.45428571428571 & 0.245714285714286 \tabularnewline
61 & 9.3 & 8.45428571428571 & 0.845714285714287 \tabularnewline
62 & 9.3 & 8.45428571428571 & 0.845714285714287 \tabularnewline
63 & 8.8 & 8.45428571428571 & 0.345714285714287 \tabularnewline
64 & 7.4 & 8.45428571428571 & -1.05428571428571 \tabularnewline
65 & 7.2 & 7.3764705882353 & -0.176470588235294 \tabularnewline
66 & 7.5 & 7.3764705882353 & 0.123529411764705 \tabularnewline
67 & 8.3 & 7.3764705882353 & 0.923529411764706 \tabularnewline
68 & 8.8 & 8.45428571428571 & 0.345714285714287 \tabularnewline
69 & 8.9 & 8.45428571428571 & 0.445714285714287 \tabularnewline
70 & 8.6 & 8.45428571428571 & 0.145714285714286 \tabularnewline
71 & 8.4 & 8.45428571428571 & -0.0542857142857134 \tabularnewline
72 & 8.4 & 8.45428571428571 & -0.0542857142857134 \tabularnewline
73 & 8.4 & 8.45428571428571 & -0.0542857142857134 \tabularnewline
74 & 8.4 & 8.45428571428571 & -0.0542857142857134 \tabularnewline
75 & 8.3 & 8.45428571428571 & -0.154285714285713 \tabularnewline
76 & 7.6 & 8.45428571428571 & -0.854285714285714 \tabularnewline
77 & 7.6 & 7.3764705882353 & 0.223529411764705 \tabularnewline
78 & 7.9 & 7.3764705882353 & 0.523529411764706 \tabularnewline
79 & 8 & 7.99545454545455 & 0.00454545454545485 \tabularnewline
80 & 8.2 & 7.99545454545455 & 0.204545454545454 \tabularnewline
81 & 8.3 & 8.45428571428571 & -0.154285714285713 \tabularnewline
82 & 8.2 & 8.45428571428571 & -0.254285714285714 \tabularnewline
83 & 8.1 & 8.45428571428571 & -0.354285714285714 \tabularnewline
84 & 8 & 7.99545454545455 & 0.00454545454545485 \tabularnewline
85 & 7.8 & 7.99545454545455 & -0.195454545454545 \tabularnewline
86 & 7.6 & 7.3764705882353 & 0.223529411764705 \tabularnewline
87 & 7.5 & 7.3764705882353 & 0.123529411764705 \tabularnewline
88 & 6.8 & 7.3764705882353 & -0.576470588235295 \tabularnewline
89 & 6.9 & 6.42142857142857 & 0.478571428571429 \tabularnewline
90 & 7.1 & 7.3764705882353 & -0.276470588235295 \tabularnewline
91 & 7.3 & 7.3764705882353 & -0.0764705882352947 \tabularnewline
92 & 7.4 & 7.3764705882353 & 0.0235294117647058 \tabularnewline
93 & 7.6 & 7.3764705882353 & 0.223529411764705 \tabularnewline
94 & 7.6 & 7.3764705882353 & 0.223529411764705 \tabularnewline
95 & 7.5 & 7.3764705882353 & 0.123529411764705 \tabularnewline
96 & 7.5 & 7.3764705882353 & 0.123529411764705 \tabularnewline
97 & 6.8 & 7.3764705882353 & -0.576470588235295 \tabularnewline
98 & 6.4 & 6.42142857142857 & -0.0214285714285714 \tabularnewline
99 & 6.2 & 6.42142857142857 & -0.221428571428572 \tabularnewline
100 & 6 & 6.42142857142857 & -0.421428571428572 \tabularnewline
101 & 6.3 & 6.42142857142857 & -0.121428571428572 \tabularnewline
102 & 6.3 & 6.42142857142857 & -0.121428571428572 \tabularnewline
103 & 6.1 & 6.42142857142857 & -0.321428571428572 \tabularnewline
104 & 6.1 & 6.42142857142857 & -0.321428571428572 \tabularnewline
105 & 6.3 & 6.42142857142857 & -0.121428571428572 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114202&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]8.1[/C][C]7.99545454545455[/C][C]0.104545454545454[/C][/ROW]
[ROW][C]2[/C][C]8.3[/C][C]7.99545454545455[/C][C]0.304545454545456[/C][/ROW]
[ROW][C]3[/C][C]8.1[/C][C]8.45428571428571[/C][C]-0.354285714285714[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]7.99545454545455[/C][C]-0.595454545454545[/C][/ROW]
[ROW][C]5[/C][C]7.3[/C][C]7.3764705882353[/C][C]-0.0764705882352947[/C][/ROW]
[ROW][C]6[/C][C]7.7[/C][C]7.3764705882353[/C][C]0.323529411764706[/C][/ROW]
[ROW][C]7[/C][C]8[/C][C]7.3764705882353[/C][C]0.623529411764705[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]7.99545454545455[/C][C]0.00454545454545485[/C][/ROW]
[ROW][C]9[/C][C]7.7[/C][C]7.99545454545455[/C][C]-0.295454545454545[/C][/ROW]
[ROW][C]10[/C][C]6.9[/C][C]7.3764705882353[/C][C]-0.476470588235294[/C][/ROW]
[ROW][C]11[/C][C]6.6[/C][C]7.3764705882353[/C][C]-0.776470588235295[/C][/ROW]
[ROW][C]12[/C][C]6.9[/C][C]6.42142857142857[/C][C]0.478571428571429[/C][/ROW]
[ROW][C]13[/C][C]7.5[/C][C]7.3764705882353[/C][C]0.123529411764705[/C][/ROW]
[ROW][C]14[/C][C]7.9[/C][C]7.3764705882353[/C][C]0.523529411764706[/C][/ROW]
[ROW][C]15[/C][C]7.7[/C][C]7.99545454545455[/C][C]-0.295454545454545[/C][/ROW]
[ROW][C]16[/C][C]6.5[/C][C]7.3764705882353[/C][C]-0.876470588235295[/C][/ROW]
[ROW][C]17[/C][C]6.1[/C][C]6.42142857142857[/C][C]-0.321428571428572[/C][/ROW]
[ROW][C]18[/C][C]6.4[/C][C]6.42142857142857[/C][C]-0.0214285714285714[/C][/ROW]
[ROW][C]19[/C][C]6.8[/C][C]6.42142857142857[/C][C]0.378571428571428[/C][/ROW]
[ROW][C]20[/C][C]7.1[/C][C]6.42142857142857[/C][C]0.678571428571428[/C][/ROW]
[ROW][C]21[/C][C]7.3[/C][C]7.3764705882353[/C][C]-0.0764705882352947[/C][/ROW]
[ROW][C]22[/C][C]7.2[/C][C]7.3764705882353[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]7.3764705882353[/C][C]-0.376470588235295[/C][/ROW]
[ROW][C]24[/C][C]7[/C][C]7.3764705882353[/C][C]-0.376470588235295[/C][/ROW]
[ROW][C]25[/C][C]7[/C][C]7.3764705882353[/C][C]-0.376470588235295[/C][/ROW]
[ROW][C]26[/C][C]7.3[/C][C]7.3764705882353[/C][C]-0.0764705882352947[/C][/ROW]
[ROW][C]27[/C][C]7.5[/C][C]7.3764705882353[/C][C]0.123529411764705[/C][/ROW]
[ROW][C]28[/C][C]7.2[/C][C]7.3764705882353[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]29[/C][C]7.7[/C][C]7.3764705882353[/C][C]0.323529411764706[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]7.3764705882353[/C][C]0.623529411764705[/C][/ROW]
[ROW][C]31[/C][C]7.9[/C][C]7.99545454545455[/C][C]-0.0954545454545448[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]7.99545454545455[/C][C]0.00454545454545485[/C][/ROW]
[ROW][C]33[/C][C]8[/C][C]7.99545454545455[/C][C]0.00454545454545485[/C][/ROW]
[ROW][C]34[/C][C]7.9[/C][C]7.99545454545455[/C][C]-0.0954545454545448[/C][/ROW]
[ROW][C]35[/C][C]7.9[/C][C]7.99545454545455[/C][C]-0.0954545454545448[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]7.99545454545455[/C][C]0.00454545454545485[/C][/ROW]
[ROW][C]37[/C][C]8.1[/C][C]7.99545454545455[/C][C]0.104545454545454[/C][/ROW]
[ROW][C]38[/C][C]8.1[/C][C]7.99545454545455[/C][C]0.104545454545454[/C][/ROW]
[ROW][C]39[/C][C]8.2[/C][C]7.99545454545455[/C][C]0.204545454545454[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]8.45428571428571[/C][C]-0.454285714285714[/C][/ROW]
[ROW][C]41[/C][C]8.3[/C][C]7.99545454545455[/C][C]0.304545454545456[/C][/ROW]
[ROW][C]42[/C][C]8.5[/C][C]8.45428571428571[/C][C]0.0457142857142863[/C][/ROW]
[ROW][C]43[/C][C]8.6[/C][C]8.45428571428571[/C][C]0.145714285714286[/C][/ROW]
[ROW][C]44[/C][C]8.7[/C][C]8.45428571428571[/C][C]0.245714285714286[/C][/ROW]
[ROW][C]45[/C][C]8.7[/C][C]8.45428571428571[/C][C]0.245714285714286[/C][/ROW]
[ROW][C]46[/C][C]8.5[/C][C]8.45428571428571[/C][C]0.0457142857142863[/C][/ROW]
[ROW][C]47[/C][C]8.4[/C][C]8.45428571428571[/C][C]-0.0542857142857134[/C][/ROW]
[ROW][C]48[/C][C]8.5[/C][C]8.45428571428571[/C][C]0.0457142857142863[/C][/ROW]
[ROW][C]49[/C][C]8.7[/C][C]8.45428571428571[/C][C]0.245714285714286[/C][/ROW]
[ROW][C]50[/C][C]8.7[/C][C]8.45428571428571[/C][C]0.245714285714286[/C][/ROW]
[ROW][C]51[/C][C]8.6[/C][C]8.45428571428571[/C][C]0.145714285714286[/C][/ROW]
[ROW][C]52[/C][C]7.9[/C][C]8.45428571428571[/C][C]-0.554285714285713[/C][/ROW]
[ROW][C]53[/C][C]8.1[/C][C]7.99545454545455[/C][C]0.104545454545454[/C][/ROW]
[ROW][C]54[/C][C]8.2[/C][C]7.99545454545455[/C][C]0.204545454545454[/C][/ROW]
[ROW][C]55[/C][C]8.5[/C][C]8.45428571428571[/C][C]0.0457142857142863[/C][/ROW]
[ROW][C]56[/C][C]8.6[/C][C]8.45428571428571[/C][C]0.145714285714286[/C][/ROW]
[ROW][C]57[/C][C]8.5[/C][C]8.45428571428571[/C][C]0.0457142857142863[/C][/ROW]
[ROW][C]58[/C][C]8.3[/C][C]8.45428571428571[/C][C]-0.154285714285713[/C][/ROW]
[ROW][C]59[/C][C]8.2[/C][C]8.45428571428571[/C][C]-0.254285714285714[/C][/ROW]
[ROW][C]60[/C][C]8.7[/C][C]8.45428571428571[/C][C]0.245714285714286[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]8.45428571428571[/C][C]0.845714285714287[/C][/ROW]
[ROW][C]62[/C][C]9.3[/C][C]8.45428571428571[/C][C]0.845714285714287[/C][/ROW]
[ROW][C]63[/C][C]8.8[/C][C]8.45428571428571[/C][C]0.345714285714287[/C][/ROW]
[ROW][C]64[/C][C]7.4[/C][C]8.45428571428571[/C][C]-1.05428571428571[/C][/ROW]
[ROW][C]65[/C][C]7.2[/C][C]7.3764705882353[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]66[/C][C]7.5[/C][C]7.3764705882353[/C][C]0.123529411764705[/C][/ROW]
[ROW][C]67[/C][C]8.3[/C][C]7.3764705882353[/C][C]0.923529411764706[/C][/ROW]
[ROW][C]68[/C][C]8.8[/C][C]8.45428571428571[/C][C]0.345714285714287[/C][/ROW]
[ROW][C]69[/C][C]8.9[/C][C]8.45428571428571[/C][C]0.445714285714287[/C][/ROW]
[ROW][C]70[/C][C]8.6[/C][C]8.45428571428571[/C][C]0.145714285714286[/C][/ROW]
[ROW][C]71[/C][C]8.4[/C][C]8.45428571428571[/C][C]-0.0542857142857134[/C][/ROW]
[ROW][C]72[/C][C]8.4[/C][C]8.45428571428571[/C][C]-0.0542857142857134[/C][/ROW]
[ROW][C]73[/C][C]8.4[/C][C]8.45428571428571[/C][C]-0.0542857142857134[/C][/ROW]
[ROW][C]74[/C][C]8.4[/C][C]8.45428571428571[/C][C]-0.0542857142857134[/C][/ROW]
[ROW][C]75[/C][C]8.3[/C][C]8.45428571428571[/C][C]-0.154285714285713[/C][/ROW]
[ROW][C]76[/C][C]7.6[/C][C]8.45428571428571[/C][C]-0.854285714285714[/C][/ROW]
[ROW][C]77[/C][C]7.6[/C][C]7.3764705882353[/C][C]0.223529411764705[/C][/ROW]
[ROW][C]78[/C][C]7.9[/C][C]7.3764705882353[/C][C]0.523529411764706[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]7.99545454545455[/C][C]0.00454545454545485[/C][/ROW]
[ROW][C]80[/C][C]8.2[/C][C]7.99545454545455[/C][C]0.204545454545454[/C][/ROW]
[ROW][C]81[/C][C]8.3[/C][C]8.45428571428571[/C][C]-0.154285714285713[/C][/ROW]
[ROW][C]82[/C][C]8.2[/C][C]8.45428571428571[/C][C]-0.254285714285714[/C][/ROW]
[ROW][C]83[/C][C]8.1[/C][C]8.45428571428571[/C][C]-0.354285714285714[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]7.99545454545455[/C][C]0.00454545454545485[/C][/ROW]
[ROW][C]85[/C][C]7.8[/C][C]7.99545454545455[/C][C]-0.195454545454545[/C][/ROW]
[ROW][C]86[/C][C]7.6[/C][C]7.3764705882353[/C][C]0.223529411764705[/C][/ROW]
[ROW][C]87[/C][C]7.5[/C][C]7.3764705882353[/C][C]0.123529411764705[/C][/ROW]
[ROW][C]88[/C][C]6.8[/C][C]7.3764705882353[/C][C]-0.576470588235295[/C][/ROW]
[ROW][C]89[/C][C]6.9[/C][C]6.42142857142857[/C][C]0.478571428571429[/C][/ROW]
[ROW][C]90[/C][C]7.1[/C][C]7.3764705882353[/C][C]-0.276470588235295[/C][/ROW]
[ROW][C]91[/C][C]7.3[/C][C]7.3764705882353[/C][C]-0.0764705882352947[/C][/ROW]
[ROW][C]92[/C][C]7.4[/C][C]7.3764705882353[/C][C]0.0235294117647058[/C][/ROW]
[ROW][C]93[/C][C]7.6[/C][C]7.3764705882353[/C][C]0.223529411764705[/C][/ROW]
[ROW][C]94[/C][C]7.6[/C][C]7.3764705882353[/C][C]0.223529411764705[/C][/ROW]
[ROW][C]95[/C][C]7.5[/C][C]7.3764705882353[/C][C]0.123529411764705[/C][/ROW]
[ROW][C]96[/C][C]7.5[/C][C]7.3764705882353[/C][C]0.123529411764705[/C][/ROW]
[ROW][C]97[/C][C]6.8[/C][C]7.3764705882353[/C][C]-0.576470588235295[/C][/ROW]
[ROW][C]98[/C][C]6.4[/C][C]6.42142857142857[/C][C]-0.0214285714285714[/C][/ROW]
[ROW][C]99[/C][C]6.2[/C][C]6.42142857142857[/C][C]-0.221428571428572[/C][/ROW]
[ROW][C]100[/C][C]6[/C][C]6.42142857142857[/C][C]-0.421428571428572[/C][/ROW]
[ROW][C]101[/C][C]6.3[/C][C]6.42142857142857[/C][C]-0.121428571428572[/C][/ROW]
[ROW][C]102[/C][C]6.3[/C][C]6.42142857142857[/C][C]-0.121428571428572[/C][/ROW]
[ROW][C]103[/C][C]6.1[/C][C]6.42142857142857[/C][C]-0.321428571428572[/C][/ROW]
[ROW][C]104[/C][C]6.1[/C][C]6.42142857142857[/C][C]-0.321428571428572[/C][/ROW]
[ROW][C]105[/C][C]6.3[/C][C]6.42142857142857[/C][C]-0.121428571428572[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114202&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114202&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
18.17.995454545454550.104545454545454
28.37.995454545454550.304545454545456
38.18.45428571428571-0.354285714285714
47.47.99545454545455-0.595454545454545
57.37.3764705882353-0.0764705882352947
67.77.37647058823530.323529411764706
787.37647058823530.623529411764705
887.995454545454550.00454545454545485
97.77.99545454545455-0.295454545454545
106.97.3764705882353-0.476470588235294
116.67.3764705882353-0.776470588235295
126.96.421428571428570.478571428571429
137.57.37647058823530.123529411764705
147.97.37647058823530.523529411764706
157.77.99545454545455-0.295454545454545
166.57.3764705882353-0.876470588235295
176.16.42142857142857-0.321428571428572
186.46.42142857142857-0.0214285714285714
196.86.421428571428570.378571428571428
207.16.421428571428570.678571428571428
217.37.3764705882353-0.0764705882352947
227.27.3764705882353-0.176470588235294
2377.3764705882353-0.376470588235295
2477.3764705882353-0.376470588235295
2577.3764705882353-0.376470588235295
267.37.3764705882353-0.0764705882352947
277.57.37647058823530.123529411764705
287.27.3764705882353-0.176470588235294
297.77.37647058823530.323529411764706
3087.37647058823530.623529411764705
317.97.99545454545455-0.0954545454545448
3287.995454545454550.00454545454545485
3387.995454545454550.00454545454545485
347.97.99545454545455-0.0954545454545448
357.97.99545454545455-0.0954545454545448
3687.995454545454550.00454545454545485
378.17.995454545454550.104545454545454
388.17.995454545454550.104545454545454
398.27.995454545454550.204545454545454
4088.45428571428571-0.454285714285714
418.37.995454545454550.304545454545456
428.58.454285714285710.0457142857142863
438.68.454285714285710.145714285714286
448.78.454285714285710.245714285714286
458.78.454285714285710.245714285714286
468.58.454285714285710.0457142857142863
478.48.45428571428571-0.0542857142857134
488.58.454285714285710.0457142857142863
498.78.454285714285710.245714285714286
508.78.454285714285710.245714285714286
518.68.454285714285710.145714285714286
527.98.45428571428571-0.554285714285713
538.17.995454545454550.104545454545454
548.27.995454545454550.204545454545454
558.58.454285714285710.0457142857142863
568.68.454285714285710.145714285714286
578.58.454285714285710.0457142857142863
588.38.45428571428571-0.154285714285713
598.28.45428571428571-0.254285714285714
608.78.454285714285710.245714285714286
619.38.454285714285710.845714285714287
629.38.454285714285710.845714285714287
638.88.454285714285710.345714285714287
647.48.45428571428571-1.05428571428571
657.27.3764705882353-0.176470588235294
667.57.37647058823530.123529411764705
678.37.37647058823530.923529411764706
688.88.454285714285710.345714285714287
698.98.454285714285710.445714285714287
708.68.454285714285710.145714285714286
718.48.45428571428571-0.0542857142857134
728.48.45428571428571-0.0542857142857134
738.48.45428571428571-0.0542857142857134
748.48.45428571428571-0.0542857142857134
758.38.45428571428571-0.154285714285713
767.68.45428571428571-0.854285714285714
777.67.37647058823530.223529411764705
787.97.37647058823530.523529411764706
7987.995454545454550.00454545454545485
808.27.995454545454550.204545454545454
818.38.45428571428571-0.154285714285713
828.28.45428571428571-0.254285714285714
838.18.45428571428571-0.354285714285714
8487.995454545454550.00454545454545485
857.87.99545454545455-0.195454545454545
867.67.37647058823530.223529411764705
877.57.37647058823530.123529411764705
886.87.3764705882353-0.576470588235295
896.96.421428571428570.478571428571429
907.17.3764705882353-0.276470588235295
917.37.3764705882353-0.0764705882352947
927.47.37647058823530.0235294117647058
937.67.37647058823530.223529411764705
947.67.37647058823530.223529411764705
957.57.37647058823530.123529411764705
967.57.37647058823530.123529411764705
976.87.3764705882353-0.576470588235295
986.46.42142857142857-0.0214285714285714
996.26.42142857142857-0.221428571428572
10066.42142857142857-0.421428571428572
1016.36.42142857142857-0.121428571428572
1026.36.42142857142857-0.121428571428572
1036.16.42142857142857-0.321428571428572
1046.16.42142857142857-0.321428571428572
1056.36.42142857142857-0.121428571428572


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = none ; par3 = 3 ; 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')
}