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Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationThu, 27 Dec 2012 08:17:56 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/27/t1356614296m4x1r0keede45f3.htm/, Retrieved Tue, 07 Feb 2023 17:21:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204766, Retrieved Tue, 07 Feb 2023 17:21:13 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [Regressieboom Lon...] [2012-12-16 18:29:13] [ae5ad0a586ac3fd0e6f11dab2358eb50]
-    D    [Recursive Partitioning (Regression Trees)] [3 NA] [2012-12-27 13:17:56] [ae7a5a1cf44a58c30c60b8a64f459c03] [Current]
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Dataseries X:
0	3289,5	0,66	0,814	0,526	1
6	25299,2	0,384	0,743	0,587	2
0	52,8	NA	0,891	0,799	NA
0	10335,1	NA	0,334	0,464	2
0	62,2	NA	0,771	0,676	1
1	32642,4	0,681	0,813	0,612	0
64	17096,2	0,952	0,895	0,779	0
4	7670,5	0,709	0,875	0,793	0
1	256,1	NA	0,778	0,778	0
0	492,9	0,602	0,825	0,754	1
0	105256	0,253	0,623	0,277	0
0	259,5	NA	0,862	0,72	0
4	9949	0,764	0,884	0,791	0
0	190,2	0,615	0,828	0,54	0
0	4773,1	0,203	0,453	0,342	0
0	558,5	NA	0,513	0,415	2
0	6658,5	0,604	0,612	0,476	0
0	4308,2	NA	0,737	NA	2
0	1382,4	0,497	0,696	0,607	0
12	149650,2	NA	0,73	0,608	0
0	9324	NA	0,45	0,283	1
0	12180,8	0,346	0,525	0,428	2
47	27700,9	0,875	0,904	0,796	0
0	2934,8	0,219	0,455	0,3	1
0	6011,2	NA	0,484	0,3	2
0	13187,8	0,677	0,847	0,592	0
1	33203,3	0,39	0,762	0,582	1
0	36406,2	0,235	0,424	NA	2
0	3070,2	0,548	0,879	0,587	0
4	4517,2	0,624	0,819	NA	1
93	10570,3	0,69	0,859	0,523	2
0	766,7	0,626	0,892	0,747	0
25	10302,7	0,762	0,819	NA	NA
25	20143,2	NA	0,801	NA	2
11	5141	0,774	0,866	0,79	0
0	562,3	NA	0,495	NA	2
0	7194,7	0,499	0,751	0,513	0
0	10260,6	0,486	0,771	0,557	0
0	56843,3	0,376	0,663	0,493	1
0	5332,8	0,386	0,726	0,513	1
0	373,9	NA	0,422	0,419	2
6	1567,6	0,716	0,78	0,661	1
7	48333,3	NA	0,427	0,245	1
0	728,4	0,674	0,718	0,503	1
11	4986,4	0,74	0,871	0,776	0
66	56708,3	0,666	0,895	0,787	0
0	929	0,484	0,652	0,702	1
235	79098,1	0,721	0,874	0,796	0
1	14793,4	0,406	0,581	0,31	1
43	57214,5	0,688	0,877	0,781	0
10	10160,5	0,671	0,9	0,742	0
0	96,2	NA	0,774	0,554	0
0	8923,1	0,296	0,666	0,499	1
0	5759,4	NA	0,373	0,292	1
0	724,9	0,541	0,646	0,335	1
0	7124,9	0,294	0,554	0,384	2
0	4889,3	0,405	0,731	0,457	0
0	5793,9	0,687	0,907	0,779	NA
86	10376,3	0,661	0,778	0,683	0
0	254,8	0,727	0,917	0,79	0
0	873785,4	0,318	0,605	0,359	1
15	184345,9	0,39	0,664	0,43	1
0	17373,8	0,345	0,749	NA	2
7	3531,2	0,76	0,863	0,729	0
4	54870,6	NA	0,659	0,592	2
3	4499,9	0,784	0,891	0,739	0
48	56832,3	0,636	0,897	0,781	0
7	2364,9	0,592	0,8	0,571	0
42	122251,2	0,761	0,931	0,798	0
0	3415,6	0,526	0,795	0,493	1
20	23447,2	0,408	0,621	0,375	1
82	42980,4	0,738	0,816	0,678	0
0	2087,7	0,514	0,828	0,848	1
5	2663,9	0,652	0,772	0,661	1
0	2948,4	NA	0,768	0,607	1
0	1639,2	0,431	0,623	0,388	1
0	29	NA	0,885	0,886	0
6	3695,9	0,688	0,801	NA	0
0	381,2	0,656	0,868	0,86	0
0	11280,6	NA	0,485	0,329	1
0	9380,9	0,239	0,428	0,242	2
1	18208,6	0,534	0,789	0,595	1
0	219,5	0,447	0,645	NA	2
0	8672,9	0,082	0,382	0,27	0
0	367,5	0,686	0,872	0,713	0
0	1995,5	0,194	0,568	0,4	2
0	1059,5	NA	0,78	0,588	0
2	84306,6	0,518	0,802	0,657	1
2	2192,6	0,58	0,639	0,426	0
8	24781,1	0,254	0,696	0,466	1
0	13547,1	0,116	0,367	0,189	1
0	39268,3	0,258	0,588	0,175	NA
4	1414,8	0,524	0,644	0,53	0
0	19081,1	0,257	0,536	0,287	0
29	14891,7	0,814	0,899	0,797	0
18	3398	0,873	0,871	0,745	0
0	7788,2	0,079	0,338	0,27	1
7	97552,1	NA	0,404	0,364	1
19	4241,5	0,82	0,891	0,823	0
0	1868,1	0,71	0,798	0,718	1
1	111844,7	0,241	0,642	0,411	1
0	2415,9	0,599	0,825	0,581	1
0	4157,7	0,222	0,561	0,4	0
0	4243,9	0,466	0,757	0,532	1
140	1145195,2	0,437	0,78	0,345	2
2	21685,5	NA	0,719	0,54	1
1	61628,7	0,578	0,712	0,452	1
37	38056,2	0,676	0,803	NA	0
0	9925,5	0,568	0,856	0,729	0
40	23206,7	0,705	0,779	0,624	1
0	7109,5	0,217	0,202	0,286	2
0	161,3	NA	0,708	NA	0
0	161,3	NA	0,708	NA	0
0	24,1	NA	0,925	NA	0
0	16139,1	0,57	0,767	0,762	2
0	7241,6	0,247	0,524	0,375	1
0	71	NA	0,794	0,698	1
0	3981,6	0,182	0,296	0,258	2
0	3016,6	NA	0,877	0,788	1
2	1926,7	0,67	0,837	NA	0
0	309,5	NA	0,578	NA	0
4	36793,9	0,572	0,655	0,622	1
81	38889,2	0,619	0,9	0,756	0
0	17337	0,59	0,781	0,43	1
0	107,4	0,665	0,777	0,536	0
0	26494,2	0,163	0,513	0,315	2
1	406,9	NA	0,748	0,567	1
0	862,9	0,451	0,622	0,517	1
23	8558,8	0,759	0,909	0,786	0
5	6673,7	0,762	0,909	0,836	0
0	12324,1	0,425	0,806	0,48	2
1	57072,1	0,417	0,828	0,526	1
0	966,2	0,179	0,522	0,34	0
0	3665,5	0,312	0,521	0,306	2
0	95,2	NA	0,782	0,491	1
0	1215,5	0,609	0,771	0,659	0
0	8215,1	0,396	0,767	0,524	1
16	54130,3	0,41	0,679	0,622	1
0	17699,7	0,262	0,431	0,235	2
0	1808,6	0,462	0,817	0,871	1
290	253339,1	0,917	0,871	0,825	0
0	3109,1	0,64	0,828	0,609	0
0	146,6	NA	0,679	0,512	0
0	19685,2	0,476	0,805	0,651	1
0	67101,6	0,371	0,719	0,307	NA
0	11948,2	0,096	0,571	NA	NA
0	7860,1	0,406	0,434	0,346	0
0	10469,2	0,451	0,64	0,266	1


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204766&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204766&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204766&T=0

As an alternative you can also use a QR Code:

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

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 6 seconds R Server 'Gertrude Mary Cox' @ cox.wessa.net

 Goodness of Fit Correlation 0.5949 R-squared 0.3539 RMSE 29.0017

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5949 \tabularnewline
R-squared & 0.3539 \tabularnewline
RMSE & 29.0017 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204766&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5949[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3539[/C][/ROW]
[ROW][C]RMSE[/C][C]29.0017[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204766&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204766&T=1

As an alternative you can also use a QR Code:

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

 Goodness of Fit Correlation 0.5949 R-squared 0.3539 RMSE 29.0017

 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 0 0.338028169014085 -0.338028169014085 2 6 3.03125 2.96875 3 0 0.338028169014085 -0.338028169014085 4 0 3.03125 -3.03125 5 0 0.338028169014085 -0.338028169014085 6 1 49.4666666666667 -48.4666666666667 7 64 49.4666666666667 14.5333333333333 8 4 2.8 1.2 9 1 0.338028169014085 0.661971830985915 10 0 0.338028169014085 -0.338028169014085 11 0 67.6363636363636 -67.6363636363636 12 0 0.338028169014085 -0.338028169014085 13 4 11.2222222222222 -7.22222222222222 14 0 0.338028169014085 -0.338028169014085 15 0 0.338028169014085 -0.338028169014085 16 0 0.338028169014085 -0.338028169014085 17 0 0.338028169014085 -0.338028169014085 18 0 0.338028169014085 -0.338028169014085 19 0 0.338028169014085 -0.338028169014085 20 12 67.6363636363636 -55.6363636363636 21 0 0.338028169014085 -0.338028169014085 22 0 3.03125 -3.03125 23 47 49.4666666666667 -2.46666666666667 24 0 0.338028169014085 -0.338028169014085 25 0 0.338028169014085 -0.338028169014085 26 0 49.4666666666667 -49.4666666666667 27 1 3.03125 -2.03125 28 0 3.03125 -3.03125 29 0 0.338028169014085 -0.338028169014085 30 4 0.338028169014085 3.66197183098592 31 93 49.4666666666667 43.5333333333333 32 0 0.338028169014085 -0.338028169014085 33 25 49.4666666666667 -24.4666666666667 34 25 3.03125 21.96875 35 11 11.2222222222222 -0.222222222222221 36 0 0.338028169014085 -0.338028169014085 37 0 0.338028169014085 -0.338028169014085 38 0 0.338028169014085 -0.338028169014085 39 0 3.03125 -3.03125 40 0 0.338028169014085 -0.338028169014085 41 0 0.338028169014085 -0.338028169014085 42 6 2.8 3.2 43 7 3.03125 3.96875 44 0 2.8 -2.8 45 11 11.2222222222222 -0.222222222222221 46 66 49.4666666666667 16.5333333333333 47 0 0.338028169014085 -0.338028169014085 48 235 67.6363636363636 167.363636363636 49 1 3.03125 -2.03125 50 43 49.4666666666667 -6.46666666666667 51 10 2.8 7.2 52 0 0.338028169014085 -0.338028169014085 53 0 0.338028169014085 -0.338028169014085 54 0 0.338028169014085 -0.338028169014085 55 0 0.338028169014085 -0.338028169014085 56 0 0.338028169014085 -0.338028169014085 57 0 0.338028169014085 -0.338028169014085 58 0 2.8 -2.8 59 86 49.4666666666667 36.5333333333333 60 0 2.8 -2.8 61 0 67.6363636363636 -67.6363636363636 62 15 67.6363636363636 -52.6363636363636 63 0 3.03125 -3.03125 64 7 11.2222222222222 -4.22222222222222 65 4 3.03125 0.96875 66 3 11.2222222222222 -8.22222222222222 67 48 49.4666666666667 -1.46666666666667 68 7 0.338028169014085 6.66197183098592 69 42 67.6363636363636 -25.6363636363636 70 0 0.338028169014085 -0.338028169014085 71 20 3.03125 16.96875 72 82 49.4666666666667 32.5333333333333 73 0 0.338028169014085 -0.338028169014085 74 5 0.338028169014085 4.66197183098592 75 0 0.338028169014085 -0.338028169014085 76 0 0.338028169014085 -0.338028169014085 77 0 0.338028169014085 -0.338028169014085 78 6 2.8 3.2 79 0 0.338028169014085 -0.338028169014085 80 0 3.03125 -3.03125 81 0 0.338028169014085 -0.338028169014085 82 1 3.03125 -2.03125 83 0 0.338028169014085 -0.338028169014085 84 0 0.338028169014085 -0.338028169014085 85 0 2.8 -2.8 86 0 0.338028169014085 -0.338028169014085 87 0 0.338028169014085 -0.338028169014085 88 2 67.6363636363636 -65.6363636363636 89 2 0.338028169014085 1.66197183098592 90 8 3.03125 4.96875 91 0 3.03125 -3.03125 92 0 3.03125 -3.03125 93 4 0.338028169014085 3.66197183098592 94 0 3.03125 -3.03125 95 29 49.4666666666667 -20.4666666666667 96 18 11.2222222222222 6.77777777777778 97 0 0.338028169014085 -0.338028169014085 98 7 67.6363636363636 -60.6363636363636 99 19 11.2222222222222 7.77777777777778 100 0 2.8 -2.8 101 1 67.6363636363636 -66.6363636363636 102 0 0.338028169014085 -0.338028169014085 103 0 0.338028169014085 -0.338028169014085 104 0 0.338028169014085 -0.338028169014085 105 140 67.6363636363636 72.3636363636364 106 2 3.03125 -1.03125 107 1 3.03125 -2.03125 108 37 49.4666666666667 -12.4666666666667 109 0 0.338028169014085 -0.338028169014085 110 40 49.4666666666667 -9.46666666666667 111 0 0.338028169014085 -0.338028169014085 112 0 0.338028169014085 -0.338028169014085 113 0 0.338028169014085 -0.338028169014085 114 0 0.338028169014085 -0.338028169014085 115 0 3.03125 -3.03125 116 0 0.338028169014085 -0.338028169014085 117 0 0.338028169014085 -0.338028169014085 118 0 0.338028169014085 -0.338028169014085 119 0 0.338028169014085 -0.338028169014085 120 2 2.8 -0.8 121 0 0.338028169014085 -0.338028169014085 122 4 3.03125 0.96875 123 81 49.4666666666667 31.5333333333333 124 0 3.03125 -3.03125 125 0 0.338028169014085 -0.338028169014085 126 0 3.03125 -3.03125 127 1 0.338028169014085 0.661971830985915 128 0 0.338028169014085 -0.338028169014085 129 23 11.2222222222222 11.7777777777778 130 5 11.2222222222222 -6.22222222222222 131 0 3.03125 -3.03125 132 1 3.03125 -2.03125 133 0 0.338028169014085 -0.338028169014085 134 0 0.338028169014085 -0.338028169014085 135 0 0.338028169014085 -0.338028169014085 136 0 0.338028169014085 -0.338028169014085 137 0 0.338028169014085 -0.338028169014085 138 16 3.03125 12.96875 139 0 3.03125 -3.03125 140 0 0.338028169014085 -0.338028169014085 141 290 67.6363636363636 222.363636363636 142 0 0.338028169014085 -0.338028169014085 143 0 0.338028169014085 -0.338028169014085 144 0 3.03125 -3.03125 145 0 3.03125 -3.03125 146 0 3.03125 -3.03125 147 0 0.338028169014085 -0.338028169014085 148 0 3.03125 -3.03125

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
2 & 6 & 3.03125 & 2.96875 \tabularnewline
3 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
4 & 0 & 3.03125 & -3.03125 \tabularnewline
5 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
6 & 1 & 49.4666666666667 & -48.4666666666667 \tabularnewline
7 & 64 & 49.4666666666667 & 14.5333333333333 \tabularnewline
8 & 4 & 2.8 & 1.2 \tabularnewline
9 & 1 & 0.338028169014085 & 0.661971830985915 \tabularnewline
10 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
11 & 0 & 67.6363636363636 & -67.6363636363636 \tabularnewline
12 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
13 & 4 & 11.2222222222222 & -7.22222222222222 \tabularnewline
14 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
15 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
16 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
17 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
18 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
19 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
20 & 12 & 67.6363636363636 & -55.6363636363636 \tabularnewline
21 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
22 & 0 & 3.03125 & -3.03125 \tabularnewline
23 & 47 & 49.4666666666667 & -2.46666666666667 \tabularnewline
24 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
25 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
26 & 0 & 49.4666666666667 & -49.4666666666667 \tabularnewline
27 & 1 & 3.03125 & -2.03125 \tabularnewline
28 & 0 & 3.03125 & -3.03125 \tabularnewline
29 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
30 & 4 & 0.338028169014085 & 3.66197183098592 \tabularnewline
31 & 93 & 49.4666666666667 & 43.5333333333333 \tabularnewline
32 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
33 & 25 & 49.4666666666667 & -24.4666666666667 \tabularnewline
34 & 25 & 3.03125 & 21.96875 \tabularnewline
35 & 11 & 11.2222222222222 & -0.222222222222221 \tabularnewline
36 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
37 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
38 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
39 & 0 & 3.03125 & -3.03125 \tabularnewline
40 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
41 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
42 & 6 & 2.8 & 3.2 \tabularnewline
43 & 7 & 3.03125 & 3.96875 \tabularnewline
44 & 0 & 2.8 & -2.8 \tabularnewline
45 & 11 & 11.2222222222222 & -0.222222222222221 \tabularnewline
46 & 66 & 49.4666666666667 & 16.5333333333333 \tabularnewline
47 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
48 & 235 & 67.6363636363636 & 167.363636363636 \tabularnewline
49 & 1 & 3.03125 & -2.03125 \tabularnewline
50 & 43 & 49.4666666666667 & -6.46666666666667 \tabularnewline
51 & 10 & 2.8 & 7.2 \tabularnewline
52 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
53 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
54 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
55 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
56 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
57 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
58 & 0 & 2.8 & -2.8 \tabularnewline
59 & 86 & 49.4666666666667 & 36.5333333333333 \tabularnewline
60 & 0 & 2.8 & -2.8 \tabularnewline
61 & 0 & 67.6363636363636 & -67.6363636363636 \tabularnewline
62 & 15 & 67.6363636363636 & -52.6363636363636 \tabularnewline
63 & 0 & 3.03125 & -3.03125 \tabularnewline
64 & 7 & 11.2222222222222 & -4.22222222222222 \tabularnewline
65 & 4 & 3.03125 & 0.96875 \tabularnewline
66 & 3 & 11.2222222222222 & -8.22222222222222 \tabularnewline
67 & 48 & 49.4666666666667 & -1.46666666666667 \tabularnewline
68 & 7 & 0.338028169014085 & 6.66197183098592 \tabularnewline
69 & 42 & 67.6363636363636 & -25.6363636363636 \tabularnewline
70 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
71 & 20 & 3.03125 & 16.96875 \tabularnewline
72 & 82 & 49.4666666666667 & 32.5333333333333 \tabularnewline
73 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
74 & 5 & 0.338028169014085 & 4.66197183098592 \tabularnewline
75 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
76 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
77 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
78 & 6 & 2.8 & 3.2 \tabularnewline
79 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
80 & 0 & 3.03125 & -3.03125 \tabularnewline
81 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
82 & 1 & 3.03125 & -2.03125 \tabularnewline
83 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
84 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
85 & 0 & 2.8 & -2.8 \tabularnewline
86 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
87 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
88 & 2 & 67.6363636363636 & -65.6363636363636 \tabularnewline
89 & 2 & 0.338028169014085 & 1.66197183098592 \tabularnewline
90 & 8 & 3.03125 & 4.96875 \tabularnewline
91 & 0 & 3.03125 & -3.03125 \tabularnewline
92 & 0 & 3.03125 & -3.03125 \tabularnewline
93 & 4 & 0.338028169014085 & 3.66197183098592 \tabularnewline
94 & 0 & 3.03125 & -3.03125 \tabularnewline
95 & 29 & 49.4666666666667 & -20.4666666666667 \tabularnewline
96 & 18 & 11.2222222222222 & 6.77777777777778 \tabularnewline
97 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
98 & 7 & 67.6363636363636 & -60.6363636363636 \tabularnewline
99 & 19 & 11.2222222222222 & 7.77777777777778 \tabularnewline
100 & 0 & 2.8 & -2.8 \tabularnewline
101 & 1 & 67.6363636363636 & -66.6363636363636 \tabularnewline
102 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
103 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
104 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
105 & 140 & 67.6363636363636 & 72.3636363636364 \tabularnewline
106 & 2 & 3.03125 & -1.03125 \tabularnewline
107 & 1 & 3.03125 & -2.03125 \tabularnewline
108 & 37 & 49.4666666666667 & -12.4666666666667 \tabularnewline
109 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
110 & 40 & 49.4666666666667 & -9.46666666666667 \tabularnewline
111 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
112 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
113 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
114 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
115 & 0 & 3.03125 & -3.03125 \tabularnewline
116 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
117 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
118 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
119 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
120 & 2 & 2.8 & -0.8 \tabularnewline
121 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
122 & 4 & 3.03125 & 0.96875 \tabularnewline
123 & 81 & 49.4666666666667 & 31.5333333333333 \tabularnewline
124 & 0 & 3.03125 & -3.03125 \tabularnewline
125 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
126 & 0 & 3.03125 & -3.03125 \tabularnewline
127 & 1 & 0.338028169014085 & 0.661971830985915 \tabularnewline
128 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
129 & 23 & 11.2222222222222 & 11.7777777777778 \tabularnewline
130 & 5 & 11.2222222222222 & -6.22222222222222 \tabularnewline
131 & 0 & 3.03125 & -3.03125 \tabularnewline
132 & 1 & 3.03125 & -2.03125 \tabularnewline
133 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
134 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
135 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
136 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
137 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
138 & 16 & 3.03125 & 12.96875 \tabularnewline
139 & 0 & 3.03125 & -3.03125 \tabularnewline
140 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
141 & 290 & 67.6363636363636 & 222.363636363636 \tabularnewline
142 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
143 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
144 & 0 & 3.03125 & -3.03125 \tabularnewline
145 & 0 & 3.03125 & -3.03125 \tabularnewline
146 & 0 & 3.03125 & -3.03125 \tabularnewline
147 & 0 & 0.338028169014085 & -0.338028169014085 \tabularnewline
148 & 0 & 3.03125 & -3.03125 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204766&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[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]3.03125[/C][C]2.96875[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]49.4666666666667[/C][C]-48.4666666666667[/C][/ROW]
[ROW][C]7[/C][C]64[/C][C]49.4666666666667[/C][C]14.5333333333333[/C][/ROW]
[ROW][C]8[/C][C]4[/C][C]2.8[/C][C]1.2[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.338028169014085[/C][C]0.661971830985915[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]67.6363636363636[/C][C]-67.6363636363636[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]11.2222222222222[/C][C]-7.22222222222222[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]67.6363636363636[/C][C]-55.6363636363636[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]23[/C][C]47[/C][C]49.4666666666667[/C][C]-2.46666666666667[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]49.4666666666667[/C][C]-49.4666666666667[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]3.03125[/C][C]-2.03125[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]0.338028169014085[/C][C]3.66197183098592[/C][/ROW]
[ROW][C]31[/C][C]93[/C][C]49.4666666666667[/C][C]43.5333333333333[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]33[/C][C]25[/C][C]49.4666666666667[/C][C]-24.4666666666667[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]3.03125[/C][C]21.96875[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]11.2222222222222[/C][C]-0.222222222222221[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]42[/C][C]6[/C][C]2.8[/C][C]3.2[/C][/ROW]
[ROW][C]43[/C][C]7[/C][C]3.03125[/C][C]3.96875[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]2.8[/C][C]-2.8[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.2222222222222[/C][C]-0.222222222222221[/C][/ROW]
[ROW][C]46[/C][C]66[/C][C]49.4666666666667[/C][C]16.5333333333333[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]48[/C][C]235[/C][C]67.6363636363636[/C][C]167.363636363636[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]3.03125[/C][C]-2.03125[/C][/ROW]
[ROW][C]50[/C][C]43[/C][C]49.4666666666667[/C][C]-6.46666666666667[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]2.8[/C][C]7.2[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]2.8[/C][C]-2.8[/C][/ROW]
[ROW][C]59[/C][C]86[/C][C]49.4666666666667[/C][C]36.5333333333333[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]2.8[/C][C]-2.8[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]67.6363636363636[/C][C]-67.6363636363636[/C][/ROW]
[ROW][C]62[/C][C]15[/C][C]67.6363636363636[/C][C]-52.6363636363636[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]64[/C][C]7[/C][C]11.2222222222222[/C][C]-4.22222222222222[/C][/ROW]
[ROW][C]65[/C][C]4[/C][C]3.03125[/C][C]0.96875[/C][/ROW]
[ROW][C]66[/C][C]3[/C][C]11.2222222222222[/C][C]-8.22222222222222[/C][/ROW]
[ROW][C]67[/C][C]48[/C][C]49.4666666666667[/C][C]-1.46666666666667[/C][/ROW]
[ROW][C]68[/C][C]7[/C][C]0.338028169014085[/C][C]6.66197183098592[/C][/ROW]
[ROW][C]69[/C][C]42[/C][C]67.6363636363636[/C][C]-25.6363636363636[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]71[/C][C]20[/C][C]3.03125[/C][C]16.96875[/C][/ROW]
[ROW][C]72[/C][C]82[/C][C]49.4666666666667[/C][C]32.5333333333333[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]74[/C][C]5[/C][C]0.338028169014085[/C][C]4.66197183098592[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]78[/C][C]6[/C][C]2.8[/C][C]3.2[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]3.03125[/C][C]-2.03125[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]2.8[/C][C]-2.8[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]67.6363636363636[/C][C]-65.6363636363636[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]0.338028169014085[/C][C]1.66197183098592[/C][/ROW]
[ROW][C]90[/C][C]8[/C][C]3.03125[/C][C]4.96875[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]0.338028169014085[/C][C]3.66197183098592[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]49.4666666666667[/C][C]-20.4666666666667[/C][/ROW]
[ROW][C]96[/C][C]18[/C][C]11.2222222222222[/C][C]6.77777777777778[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]98[/C][C]7[/C][C]67.6363636363636[/C][C]-60.6363636363636[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]11.2222222222222[/C][C]7.77777777777778[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]2.8[/C][C]-2.8[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]67.6363636363636[/C][C]-66.6363636363636[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]105[/C][C]140[/C][C]67.6363636363636[/C][C]72.3636363636364[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]3.03125[/C][C]-1.03125[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]3.03125[/C][C]-2.03125[/C][/ROW]
[ROW][C]108[/C][C]37[/C][C]49.4666666666667[/C][C]-12.4666666666667[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]110[/C][C]40[/C][C]49.4666666666667[/C][C]-9.46666666666667[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]2.8[/C][C]-0.8[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]3.03125[/C][C]0.96875[/C][/ROW]
[ROW][C]123[/C][C]81[/C][C]49.4666666666667[/C][C]31.5333333333333[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]0.338028169014085[/C][C]0.661971830985915[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]129[/C][C]23[/C][C]11.2222222222222[/C][C]11.7777777777778[/C][/ROW]
[ROW][C]130[/C][C]5[/C][C]11.2222222222222[/C][C]-6.22222222222222[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]3.03125[/C][C]-2.03125[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]3.03125[/C][C]12.96875[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]141[/C][C]290[/C][C]67.6363636363636[/C][C]222.363636363636[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.338028169014085[/C][C]-0.338028169014085[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]3.03125[/C][C]-3.03125[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204766&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204766&T=2

As an alternative you can also use a QR Code:

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

 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 0 0.338028169014085 -0.338028169014085 2 6 3.03125 2.96875 3 0 0.338028169014085 -0.338028169014085 4 0 3.03125 -3.03125 5 0 0.338028169014085 -0.338028169014085 6 1 49.4666666666667 -48.4666666666667 7 64 49.4666666666667 14.5333333333333 8 4 2.8 1.2 9 1 0.338028169014085 0.661971830985915 10 0 0.338028169014085 -0.338028169014085 11 0 67.6363636363636 -67.6363636363636 12 0 0.338028169014085 -0.338028169014085 13 4 11.2222222222222 -7.22222222222222 14 0 0.338028169014085 -0.338028169014085 15 0 0.338028169014085 -0.338028169014085 16 0 0.338028169014085 -0.338028169014085 17 0 0.338028169014085 -0.338028169014085 18 0 0.338028169014085 -0.338028169014085 19 0 0.338028169014085 -0.338028169014085 20 12 67.6363636363636 -55.6363636363636 21 0 0.338028169014085 -0.338028169014085 22 0 3.03125 -3.03125 23 47 49.4666666666667 -2.46666666666667 24 0 0.338028169014085 -0.338028169014085 25 0 0.338028169014085 -0.338028169014085 26 0 49.4666666666667 -49.4666666666667 27 1 3.03125 -2.03125 28 0 3.03125 -3.03125 29 0 0.338028169014085 -0.338028169014085 30 4 0.338028169014085 3.66197183098592 31 93 49.4666666666667 43.5333333333333 32 0 0.338028169014085 -0.338028169014085 33 25 49.4666666666667 -24.4666666666667 34 25 3.03125 21.96875 35 11 11.2222222222222 -0.222222222222221 36 0 0.338028169014085 -0.338028169014085 37 0 0.338028169014085 -0.338028169014085 38 0 0.338028169014085 -0.338028169014085 39 0 3.03125 -3.03125 40 0 0.338028169014085 -0.338028169014085 41 0 0.338028169014085 -0.338028169014085 42 6 2.8 3.2 43 7 3.03125 3.96875 44 0 2.8 -2.8 45 11 11.2222222222222 -0.222222222222221 46 66 49.4666666666667 16.5333333333333 47 0 0.338028169014085 -0.338028169014085 48 235 67.6363636363636 167.363636363636 49 1 3.03125 -2.03125 50 43 49.4666666666667 -6.46666666666667 51 10 2.8 7.2 52 0 0.338028169014085 -0.338028169014085 53 0 0.338028169014085 -0.338028169014085 54 0 0.338028169014085 -0.338028169014085 55 0 0.338028169014085 -0.338028169014085 56 0 0.338028169014085 -0.338028169014085 57 0 0.338028169014085 -0.338028169014085 58 0 2.8 -2.8 59 86 49.4666666666667 36.5333333333333 60 0 2.8 -2.8 61 0 67.6363636363636 -67.6363636363636 62 15 67.6363636363636 -52.6363636363636 63 0 3.03125 -3.03125 64 7 11.2222222222222 -4.22222222222222 65 4 3.03125 0.96875 66 3 11.2222222222222 -8.22222222222222 67 48 49.4666666666667 -1.46666666666667 68 7 0.338028169014085 6.66197183098592 69 42 67.6363636363636 -25.6363636363636 70 0 0.338028169014085 -0.338028169014085 71 20 3.03125 16.96875 72 82 49.4666666666667 32.5333333333333 73 0 0.338028169014085 -0.338028169014085 74 5 0.338028169014085 4.66197183098592 75 0 0.338028169014085 -0.338028169014085 76 0 0.338028169014085 -0.338028169014085 77 0 0.338028169014085 -0.338028169014085 78 6 2.8 3.2 79 0 0.338028169014085 -0.338028169014085 80 0 3.03125 -3.03125 81 0 0.338028169014085 -0.338028169014085 82 1 3.03125 -2.03125 83 0 0.338028169014085 -0.338028169014085 84 0 0.338028169014085 -0.338028169014085 85 0 2.8 -2.8 86 0 0.338028169014085 -0.338028169014085 87 0 0.338028169014085 -0.338028169014085 88 2 67.6363636363636 -65.6363636363636 89 2 0.338028169014085 1.66197183098592 90 8 3.03125 4.96875 91 0 3.03125 -3.03125 92 0 3.03125 -3.03125 93 4 0.338028169014085 3.66197183098592 94 0 3.03125 -3.03125 95 29 49.4666666666667 -20.4666666666667 96 18 11.2222222222222 6.77777777777778 97 0 0.338028169014085 -0.338028169014085 98 7 67.6363636363636 -60.6363636363636 99 19 11.2222222222222 7.77777777777778 100 0 2.8 -2.8 101 1 67.6363636363636 -66.6363636363636 102 0 0.338028169014085 -0.338028169014085 103 0 0.338028169014085 -0.338028169014085 104 0 0.338028169014085 -0.338028169014085 105 140 67.6363636363636 72.3636363636364 106 2 3.03125 -1.03125 107 1 3.03125 -2.03125 108 37 49.4666666666667 -12.4666666666667 109 0 0.338028169014085 -0.338028169014085 110 40 49.4666666666667 -9.46666666666667 111 0 0.338028169014085 -0.338028169014085 112 0 0.338028169014085 -0.338028169014085 113 0 0.338028169014085 -0.338028169014085 114 0 0.338028169014085 -0.338028169014085 115 0 3.03125 -3.03125 116 0 0.338028169014085 -0.338028169014085 117 0 0.338028169014085 -0.338028169014085 118 0 0.338028169014085 -0.338028169014085 119 0 0.338028169014085 -0.338028169014085 120 2 2.8 -0.8 121 0 0.338028169014085 -0.338028169014085 122 4 3.03125 0.96875 123 81 49.4666666666667 31.5333333333333 124 0 3.03125 -3.03125 125 0 0.338028169014085 -0.338028169014085 126 0 3.03125 -3.03125 127 1 0.338028169014085 0.661971830985915 128 0 0.338028169014085 -0.338028169014085 129 23 11.2222222222222 11.7777777777778 130 5 11.2222222222222 -6.22222222222222 131 0 3.03125 -3.03125 132 1 3.03125 -2.03125 133 0 0.338028169014085 -0.338028169014085 134 0 0.338028169014085 -0.338028169014085 135 0 0.338028169014085 -0.338028169014085 136 0 0.338028169014085 -0.338028169014085 137 0 0.338028169014085 -0.338028169014085 138 16 3.03125 12.96875 139 0 3.03125 -3.03125 140 0 0.338028169014085 -0.338028169014085 141 290 67.6363636363636 222.363636363636 142 0 0.338028169014085 -0.338028169014085 143 0 0.338028169014085 -0.338028169014085 144 0 3.03125 -3.03125 145 0 3.03125 -3.03125 146 0 3.03125 -3.03125 147 0 0.338028169014085 -0.338028169014085 148 0 3.03125 -3.03125

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 <- 0for (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 <- 0for (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')}}mbitmap(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')}