Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationFri, 07 Dec 2012 09:33:23 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/07/t1354890805ko9vwa5r85skovt.htm/, Retrieved Thu, 31 Oct 2024 23:51:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197395, Retrieved Thu, 31 Oct 2024 23:51:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [Recusive Partitio...] [2012-12-07 14:29:32] [2bd452e91ac81344e4a11ef6d5439293]
-   P     [Recursive Partitioning (Regression Trees)] [Recusive Partitio...] [2012-12-07 14:31:47] [2bd452e91ac81344e4a11ef6d5439293]
-             [Recursive Partitioning (Regression Trees)] [Recusive Partitio...] [2012-12-07 14:33:23] [e3d79eec5d0d9e3c05706137ffeca8bc] [Current]
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Dataseries X:
210907	1	1	24188	145
120982	1	1	18273	101
176508	1	1	14130	98
179321	1	0	32287	132
123185	1	1	8654	60
52746	1	1	9245	38
385534	1	1	33251	144
33170	1	1	1271	5
101645	1	1	5279	28
149061	1	1	27101	84
165446	1	0	16373	79
237213	1	1	19716	127
173326	1	0	17753	78
133131	1	1	9028	60
258873	1	1	18653	131
180083	1	0	8828	84
324799	1	0	29498	133
230964	1	1	27563	150
236785	1	0	18293	91
135473	1	1	22530	132
202925	1	0	15977	136
215147	1	1	35082	124
344297	1	1	16116	118
153935	1	1	15849	70
132943	1	0	16026	107
174724	1	1	26569	119
174415	1	0	24785	89
225548	1	1	17569	112
223632	1	1	23825	108
124817	1	0	7869	52
221698	1	1	14975	112
210767	1	0	37791	116
170266	1	1	9605	123
260561	1	1	27295	125
84853	1	0	2746	27
294424	1	0	34461	162
101011	1	0	8098	32
215641	1	0	4787	64
325107	1	1	24919	92
7176	1	0	603	0
167542	1	0	16329	83
106408	1	1	12558	41
96560	1	0	7784	47
265769	1	0	28522	120
269651	1	1	22265	105
149112	1	1	14459	79
175824	1	1	14526	65
152871	1	1	22240	70
111665	1	1	11802	55
116408	1	0	7623	39
362301	1	1	11912	67
78800	1	0	7935	21
183167	1	0	18220	127
277965	1	1	19199	152
150629	1	0	19918	113
168809	1	1	21884	99
24188	1	1	2694	7
329267	1	1	15808	141
65029	1	0	3597	21
101097	1	1	5296	35
218946	1	0	25239	109
244052	1	0	29801	133
341570	1	1	18450	123
103597	1	1	7132	26
233328	1	1	34861	230
256462	1	0	35940	166
206161	1	1	16688	68
311473	1	0	24683	147
235800	1	1	46230	179
177939	1	0	10387	61
207176	1	0	21436	101
196553	1	1	30546	108
174184	1	1	19746	90
143246	1	0	15977	114
187559	1	0	22583	103
187681	1	1	17274	142
119016	1	1	16469	79
182192	1	1	14251	88
73566	1	0	3007	25
194979	1	0	16851	83
167488	1	0	21113	113
143756	1	1	17401	118
275541	1	0	23958	110
243199	1	1	23567	129
182999	1	1	13065	51
135649	1	0	15358	93
152299	1	1	14587	76
120221	1	1	12770	49
346485	1	1	24021	118
145790	1	1	9648	38
193339	1	1	20537	141
80953	1	1	7905	58
122774	1	1	4527	27
130585	1	1	30495	91
112611	1	1	7117	48
286468	1	1	17719	63
241066	1	1	27056	56
148446	1	0	33473	144
204713	1	0	9758	73
182079	1	1	21115	168
140344	1	0	7236	64
220516	1	1	13790	97
243060	1	0	32902	117
162765	1	1	25131	100
182613	1	0	30910	149
232138	1	1	35947	187
265318	1	0	29848	127
85574	1	0	6943	37
310839	1	1	42705	245
225060	1	1	31808	87
232317	1	1	26675	177
144966	1	1	8435	49
43287	1	1	7409	49
155754	1	0	14993	73
164709	1	1	36867	177
201940	1	1	33835	94
235454	1	1	24164	117
220801	1	1	12607	60
99466	1	0	22609	55
92661	1	1	5892	39
133328	1	1	17014	64
61361	1	1	5394	26
125930	1	1	9178	64
100750	1	1	6440	58
224549	1	0	21916	95
82316	1	1	4011	25
102010	1	1	5818	26
101523	1	1	18647	76
243511	1	0	20556	129
22938	1	0	238	11
41566	1	1	70	2
152474	1	0	22392	101
61857	1	0	3913	28
99923	1	1	12237	36
132487	1	0	8388	89
317394	1	0	22120	193
21054	1	1	338	4
209641	1	1	11727	84
22648	1	0	3704	23
31414	1	1	3988	39
46698	1	0	3030	14
131698	1	1	13520	78
91735	1	0	1421	14
244749	1	1	20923	101
184510	1	1	20237	82
79863	1	0	3219	24
128423	1	0	3769	36
97839	1	0	12252	75
38214	1	1	1888	16
151101	1	1	14497	55
272458	1	0	28864	131
172494	1	1	21721	131
108043	1	1	4821	39
328107	1	1	33644	144
250579	1	1	15923	139
351067	1	1	42935	211
158015	1	1	18864	78
98866	1	1	4977	50
85439	0	0	7785	39
229242	0	0	17939	90
351619	0	0	23436	166
84207	0	1	325	12
120445	0	0	13539	57
324598	0	0	34538	133
131069	0	1	12198	69
204271	0	1	26924	119
165543	0	0	12716	119
141722	0	0	8172	65
116048	0	1	10855	61
250047	0	0	11932	49
299775	0	1	14300	101
195838	0	1	25515	196
173260	0	0	2805	15
254488	0	1	29402	136
104389	0	0	16440	89
136084	0	1	11221	40
199476	0	1	28732	123
92499	0	1	5250	21
224330	0	0	28608	163
135781	0	0	8092	29
74408	0	0	4473	35
81240	0	1	1572	13
14688	0	1	2065	5
181633	0	0	14817	96
271856	0	1	16714	151
7199	0	0	556	6
46660	0	0	2089	13
17547	0	1	2658	3
133368	0	1	10695	56
95227	0	1	1669	23
152601	0	1	16267	57
98146	0	1	7768	14
79619	0	0	7252	43
59194	0	1	6387	20
139942	0	1	18715	72
118612	0	0	7936	87
72880	0	0	8643	21
65475	0	0	7294	56
99643	0	0	4570	59
71965	0	0	7185	82
77272	0	1	10058	43
49289	0	1	2342	25
135131	0	1	8509	38
108446	0	0	13275	25
89746	0	0	6816	38
44296	0	0	1930	12
77648	0	1	8086	29
181528	0	0	10737	47
134019	0	0	8033	45
124064	0	0	7058	40
92630	0	1	6782	30
121848	0	0	5401	41
52915	0	1	6521	25
81872	0	1	10856	23
58981	0	0	2154	14
53515	0	1	6117	16
60812	0	0	5238	26
56375	0	0	4820	21
65490	0	1	5615	27
80949	0	0	4272	9
76302	0	1	8702	33
104011	0	0	15340	42
98104	0	0	8030	68
67989	0	0	9526	32
30989	0	0	1278	6
135458	0	1	4236	67
73504	0	0	3023	33
63123	0	0	7196	77
61254	0	1	3394	46
74914	0	1	6371	30
31774	0	0	1574	0
81437	0	0	9620	36
87186	0	0	6978	46
50090	0	1	4911	18
65745	0	0	8645	48
56653	0	0	8987	29
158399	0	0	5544	28
46455	0	0	3083	34
73624	0	0	6909	33
38395	0	0	3189	34
91899	0	0	6745	33
139526	0	1	16724	80
52164	0	1	4850	32
51567	0	1	7025	30
70551	0	0	6047	41
84856	0	0	7377	41
102538	0	1	9078	51
86678	0	1	4605	18
85709	0	0	3238	34
34662	0	0	8100	31
150580	0	0	9653	39
99611	0	1	8914	54
19349	0	1	786	14
99373	0	1	6700	24
86230	0	1	5788	24
30837	0	0	593	8
31706	0	0	4506	26
89806	0	0	6382	19
62088	0	0	5621	11
40151	0	1	3997	14
27634	0	0	520	1
76990	0	0	8891	39
37460	0	1	999	5
54157	0	0	7067	37
49862	0	0	4639	32
84337	0	1	5654	38
64175	0	1	6928	47
59382	0	0	1514	47
119308	0	1	9238	37
76702	0	0	8204	51
103425	0	0	5926	45
70344	0	1	5785	21
43410	0	0	4	1
104838	0	1	5930	42
62215	0	0	3710	26
69304	0	0	705	21
53117	0	0	443	4
19764	0	0	2416	10
86680	0	1	7747	43
84105	0	0	5432	34
77945	0	0	4913	31
89113	0	1	2650	19
91005	0	0	2370	34
40248	0	1	775	6
64187	0	0	5576	11
50857	0	0	1352	24
56613	0	1	3080	16
62792	0	1	10205	72




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 8 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197395&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197395&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197395&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Goodness of Fit
Correlation0.9085
R-squared0.8253
RMSE20.364

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9085 \tabularnewline
R-squared & 0.8253 \tabularnewline
RMSE & 20.364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197395&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9085[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8253[/C][/ROW]
[ROW][C]RMSE[/C][C]20.364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197395&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197395&T=1

As an alternative you can also use a QR Code:  

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

Goodness of Fit
Correlation0.9085
R-squared0.8253
RMSE20.364







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1145122.04687522.953125
210180.6420.36
39880.6417.36
4132122.0468759.953125
56047.090909090909112.9090909090909
63847.0909090909091-9.09090909090909
7144122.04687521.953125
858.59259259259259-3.59259259259259
92840.2352941176471-12.2352941176471
1084102.928571428571-18.9285714285714
117980.64-1.64
12127122.0468754.953125
137880.64-2.64
146047.090909090909112.9090909090909
15131122.0468758.953125
168474.36363636363649.63636363636364
17133122.04687510.953125
18150122.04687527.953125
1991122.046875-31.046875
20132102.92857142857129.0714285714286
21136122.04687513.953125
22124175.454545454545-51.4545454545455
23118122.046875-4.046875
247080.64-10.64
2510780.6426.36
26119102.92857142857116.0714285714286
2789102.928571428571-13.9285714285714
28112122.046875-10.046875
29108122.046875-14.046875
305247.09090909090914.90909090909091
31112122.046875-10.046875
32116175.454545454545-59.4545454545455
3312374.363636363636448.6363636363636
34125122.0468752.953125
352726.6750.324999999999999
36162175.454545454545-13.4545454545455
373247.0909090909091-15.0909090909091
386440.235294117647123.7647058823529
3992122.046875-30.046875
4008.59259259259259-8.59259259259259
418380.642.36
424147.0909090909091-6.09090909090909
434747.0909090909091-0.0909090909090935
44120122.046875-2.046875
45105122.046875-17.046875
467980.64-1.64
476580.64-15.64
4870102.928571428571-32.9285714285714
495547.09090909090917.90909090909091
503947.0909090909091-8.09090909090909
516774.3636363636364-7.36363636363636
522147.0909090909091-26.0909090909091
53127122.0468754.953125
54152122.04687529.953125
55113102.92857142857110.0714285714286
5699102.928571428571-3.92857142857143
5778.59259259259259-1.59259259259259
58141122.04687518.953125
592126.675-5.675
603540.2352941176471-5.23529411764706
61109122.046875-13.046875
62133122.04687510.953125
63123122.0468750.953125
642647.0909090909091-21.0909090909091
65230175.45454545454554.5454545454545
66166175.454545454545-9.45454545454547
6768122.046875-54.046875
68147122.04687524.953125
69179175.4545454545453.54545454545453
706174.3636363636364-13.3636363636364
71101122.046875-21.046875
72108122.046875-14.046875
739080.649.36
7411480.6433.36
75103122.046875-19.046875
76142122.04687519.953125
777980.64-1.64
7888122.046875-34.046875
792526.675-1.675
8083122.046875-39.046875
81113102.92857142857110.0714285714286
8211880.6437.36
83110122.046875-12.046875
84129122.0468756.953125
855174.3636363636364-23.3636363636364
869380.6412.36
877680.64-4.64
884947.09090909090911.90909090909091
89118122.046875-4.046875
903847.0909090909091-9.09090909090909
91141122.04687518.953125
925847.090909090909110.9090909090909
932740.2352941176471-13.2352941176471
9491102.928571428571-11.9285714285714
954847.09090909090910.909090909090907
9663122.046875-59.046875
9756122.046875-66.046875
98144102.92857142857141.0714285714286
997374.3636363636364-1.36363636363636
100168122.04687545.953125
1016447.090909090909116.9090909090909
10297122.046875-25.046875
103117122.046875-5.046875
104100102.928571428571-2.92857142857143
105149122.04687526.953125
106187175.45454545454511.5454545454545
107127122.0468754.953125
1083747.0909090909091-10.0909090909091
109245175.45454545454569.5454545454545
11087122.046875-35.046875
111177122.04687554.953125
1124947.09090909090911.90909090909091
1134947.09090909090911.90909090909091
1147380.64-7.64
115177175.4545454545451.54545454545453
11694122.046875-28.046875
117117122.046875-5.046875
1186074.3636363636364-14.3636363636364
11955102.928571428571-47.9285714285714
1203926.67512.325
1216480.64-16.64
1222626.675-0.675000000000001
1236447.090909090909116.9090909090909
1245840.235294117647117.7647058823529
12595122.046875-27.046875
1262526.675-1.675
1272640.2352941176471-14.2352941176471
1287680.64-4.64
129129122.0468756.953125
130118.592592592592592.40740740740741
13128.59259259259259-6.59259259259259
132101102.928571428571-1.92857142857143
1332826.6751.325
1343647.0909090909091-11.0909090909091
1358947.090909090909141.9090909090909
136193122.04687570.953125
13748.59259259259259-4.59259259259259
1388474.36363636363649.63636363636364
1392325.6923076923077-2.69230769230769
1403925.692307692307713.3076923076923
141148.592592592592595.40740740740741
1427847.090909090909130.9090909090909
1431426.675-12.675
144101122.046875-21.046875
14582122.046875-40.046875
1462426.675-2.675
1473640.2352941176471-4.23529411764706
1487547.090909090909127.9090909090909
149168.592592592592597.40740740740741
1505580.64-25.64
151131122.0468758.953125
152131102.92857142857128.0714285714286
1533940.2352941176471-1.23529411764706
154144122.04687521.953125
155139122.04687516.953125
156211175.45454545454535.5454545454545
1577880.64-2.64
1585040.23529411764719.76470588235294
1593947.0909090909091-8.09090909090909
16090122.046875-32.046875
161166122.04687543.953125
1621226.675-14.675
1635747.09090909090919.90909090909091
164133175.454545454545-42.4545454545455
1656947.090909090909121.9090909090909
166119122.046875-3.046875
16711974.363636363636444.6363636363636
1686547.090909090909117.9090909090909
1696147.090909090909113.9090909090909
1704974.3636363636364-25.3636363636364
171101122.046875-21.046875
172196122.04687573.953125
1731540.2352941176471-25.2352941176471
174136122.04687513.953125
1758980.648.36
1764047.0909090909091-7.09090909090909
177123122.0468750.953125
1782126.675-5.675
179163122.04687540.953125
1802947.0909090909091-18.0909090909091
1813526.6758.325
1821326.675-13.675
18358.59259259259259-3.59259259259259
18496122.046875-26.046875
185151122.04687528.953125
18668.59259259259259-2.59259259259259
187138.592592592592594.40740740740741
18838.59259259259259-5.59259259259259
1895647.09090909090918.90909090909091
1902326.675-3.675
1915780.64-23.64
1921447.0909090909091-33.0909090909091
1934347.0909090909091-4.09090909090909
1942025.6923076923077-5.69230769230769
1957280.64-8.64
1968747.090909090909139.9090909090909
1972147.0909090909091-26.0909090909091
1985647.09090909090918.90909090909091
1995940.235294117647118.7647058823529
2008247.090909090909134.9090909090909
2014347.0909090909091-4.09090909090909
202258.5925925925925916.4074074074074
2033847.0909090909091-9.09090909090909
2042547.0909090909091-22.0909090909091
2053826.67511.325
206128.592592592592593.40740740740741
2072947.0909090909091-18.0909090909091
2084774.3636363636364-27.3636363636364
2094547.0909090909091-2.09090909090909
2104047.0909090909091-7.09090909090909
2113026.6753.325
2124140.23529411764710.764705882352942
2132525.6923076923077-0.692307692307693
2142347.0909090909091-24.0909090909091
215148.592592592592595.40740740740741
2161625.6923076923077-9.69230769230769
2172626.675-0.675000000000001
2182125.6923076923077-4.69230769230769
2192726.6750.324999999999999
220926.675-17.675
2213347.0909090909091-14.0909090909091
2224280.64-38.64
2236847.090909090909120.9090909090909
2243247.0909090909091-15.0909090909091
22568.59259259259259-2.59259259259259
2266740.235294117647126.7647058823529
2273326.6756.325
2287747.090909090909129.9090909090909
2294626.67519.325
2303026.6753.325
23108.59259259259259-8.59259259259259
2323647.0909090909091-11.0909090909091
2334647.0909090909091-1.09090909090909
2341825.6923076923077-7.69230769230769
2354847.09090909090910.909090909090907
2362947.0909090909091-18.0909090909091
2372840.2352941176471-12.2352941176471
2383425.69230769230778.30769230769231
2393326.6756.325
2403425.69230769230778.30769230769231
2413326.6756.325
2428080.64-0.640000000000001
2433225.69230769230776.30769230769231
2443047.0909090909091-17.0909090909091
2454126.67514.325
2464147.0909090909091-6.09090909090909
2475147.09090909090913.90909090909091
2481826.675-8.675
2493426.6757.325
2503147.0909090909091-16.0909090909091
2513947.0909090909091-8.09090909090909
2525447.09090909090916.90909090909091
253148.592592592592595.40740740740741
2542440.2352941176471-16.2352941176471
2552426.675-2.675
25688.59259259259259-0.592592592592593
2572625.69230769230770.307692307692307
2581926.675-7.675
2591126.675-15.675
2601425.6923076923077-11.6923076923077
26118.59259259259259-7.59259259259259
2623947.0909090909091-8.09090909090909
26358.59259259259259-3.59259259259259
2643747.0909090909091-10.0909090909091
2653225.69230769230776.30769230769231
2663826.67511.325
2674747.0909090909091-0.0909090909090935
2684726.67520.325
2693747.0909090909091-10.0909090909091
2705147.09090909090913.90909090909091
2714540.23529411764714.76470588235294
2722126.675-5.675
27318.59259259259259-7.59259259259259
2744240.23529411764711.76470588235294
2752626.675-0.675000000000001
2762126.675-5.675
27748.59259259259259-4.59259259259259
278108.592592592592591.40740740740741
2794347.0909090909091-4.09090909090909
2803426.6757.325
2813126.6754.325
2821926.675-7.675
2833426.6757.325
28468.59259259259259-2.59259259259259
2851126.675-15.675
286248.5925925925925915.4074074074074
287168.592592592592597.40740740740741
2887247.090909090909124.9090909090909

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 145 & 122.046875 & 22.953125 \tabularnewline
2 & 101 & 80.64 & 20.36 \tabularnewline
3 & 98 & 80.64 & 17.36 \tabularnewline
4 & 132 & 122.046875 & 9.953125 \tabularnewline
5 & 60 & 47.0909090909091 & 12.9090909090909 \tabularnewline
6 & 38 & 47.0909090909091 & -9.09090909090909 \tabularnewline
7 & 144 & 122.046875 & 21.953125 \tabularnewline
8 & 5 & 8.59259259259259 & -3.59259259259259 \tabularnewline
9 & 28 & 40.2352941176471 & -12.2352941176471 \tabularnewline
10 & 84 & 102.928571428571 & -18.9285714285714 \tabularnewline
11 & 79 & 80.64 & -1.64 \tabularnewline
12 & 127 & 122.046875 & 4.953125 \tabularnewline
13 & 78 & 80.64 & -2.64 \tabularnewline
14 & 60 & 47.0909090909091 & 12.9090909090909 \tabularnewline
15 & 131 & 122.046875 & 8.953125 \tabularnewline
16 & 84 & 74.3636363636364 & 9.63636363636364 \tabularnewline
17 & 133 & 122.046875 & 10.953125 \tabularnewline
18 & 150 & 122.046875 & 27.953125 \tabularnewline
19 & 91 & 122.046875 & -31.046875 \tabularnewline
20 & 132 & 102.928571428571 & 29.0714285714286 \tabularnewline
21 & 136 & 122.046875 & 13.953125 \tabularnewline
22 & 124 & 175.454545454545 & -51.4545454545455 \tabularnewline
23 & 118 & 122.046875 & -4.046875 \tabularnewline
24 & 70 & 80.64 & -10.64 \tabularnewline
25 & 107 & 80.64 & 26.36 \tabularnewline
26 & 119 & 102.928571428571 & 16.0714285714286 \tabularnewline
27 & 89 & 102.928571428571 & -13.9285714285714 \tabularnewline
28 & 112 & 122.046875 & -10.046875 \tabularnewline
29 & 108 & 122.046875 & -14.046875 \tabularnewline
30 & 52 & 47.0909090909091 & 4.90909090909091 \tabularnewline
31 & 112 & 122.046875 & -10.046875 \tabularnewline
32 & 116 & 175.454545454545 & -59.4545454545455 \tabularnewline
33 & 123 & 74.3636363636364 & 48.6363636363636 \tabularnewline
34 & 125 & 122.046875 & 2.953125 \tabularnewline
35 & 27 & 26.675 & 0.324999999999999 \tabularnewline
36 & 162 & 175.454545454545 & -13.4545454545455 \tabularnewline
37 & 32 & 47.0909090909091 & -15.0909090909091 \tabularnewline
38 & 64 & 40.2352941176471 & 23.7647058823529 \tabularnewline
39 & 92 & 122.046875 & -30.046875 \tabularnewline
40 & 0 & 8.59259259259259 & -8.59259259259259 \tabularnewline
41 & 83 & 80.64 & 2.36 \tabularnewline
42 & 41 & 47.0909090909091 & -6.09090909090909 \tabularnewline
43 & 47 & 47.0909090909091 & -0.0909090909090935 \tabularnewline
44 & 120 & 122.046875 & -2.046875 \tabularnewline
45 & 105 & 122.046875 & -17.046875 \tabularnewline
46 & 79 & 80.64 & -1.64 \tabularnewline
47 & 65 & 80.64 & -15.64 \tabularnewline
48 & 70 & 102.928571428571 & -32.9285714285714 \tabularnewline
49 & 55 & 47.0909090909091 & 7.90909090909091 \tabularnewline
50 & 39 & 47.0909090909091 & -8.09090909090909 \tabularnewline
51 & 67 & 74.3636363636364 & -7.36363636363636 \tabularnewline
52 & 21 & 47.0909090909091 & -26.0909090909091 \tabularnewline
53 & 127 & 122.046875 & 4.953125 \tabularnewline
54 & 152 & 122.046875 & 29.953125 \tabularnewline
55 & 113 & 102.928571428571 & 10.0714285714286 \tabularnewline
56 & 99 & 102.928571428571 & -3.92857142857143 \tabularnewline
57 & 7 & 8.59259259259259 & -1.59259259259259 \tabularnewline
58 & 141 & 122.046875 & 18.953125 \tabularnewline
59 & 21 & 26.675 & -5.675 \tabularnewline
60 & 35 & 40.2352941176471 & -5.23529411764706 \tabularnewline
61 & 109 & 122.046875 & -13.046875 \tabularnewline
62 & 133 & 122.046875 & 10.953125 \tabularnewline
63 & 123 & 122.046875 & 0.953125 \tabularnewline
64 & 26 & 47.0909090909091 & -21.0909090909091 \tabularnewline
65 & 230 & 175.454545454545 & 54.5454545454545 \tabularnewline
66 & 166 & 175.454545454545 & -9.45454545454547 \tabularnewline
67 & 68 & 122.046875 & -54.046875 \tabularnewline
68 & 147 & 122.046875 & 24.953125 \tabularnewline
69 & 179 & 175.454545454545 & 3.54545454545453 \tabularnewline
70 & 61 & 74.3636363636364 & -13.3636363636364 \tabularnewline
71 & 101 & 122.046875 & -21.046875 \tabularnewline
72 & 108 & 122.046875 & -14.046875 \tabularnewline
73 & 90 & 80.64 & 9.36 \tabularnewline
74 & 114 & 80.64 & 33.36 \tabularnewline
75 & 103 & 122.046875 & -19.046875 \tabularnewline
76 & 142 & 122.046875 & 19.953125 \tabularnewline
77 & 79 & 80.64 & -1.64 \tabularnewline
78 & 88 & 122.046875 & -34.046875 \tabularnewline
79 & 25 & 26.675 & -1.675 \tabularnewline
80 & 83 & 122.046875 & -39.046875 \tabularnewline
81 & 113 & 102.928571428571 & 10.0714285714286 \tabularnewline
82 & 118 & 80.64 & 37.36 \tabularnewline
83 & 110 & 122.046875 & -12.046875 \tabularnewline
84 & 129 & 122.046875 & 6.953125 \tabularnewline
85 & 51 & 74.3636363636364 & -23.3636363636364 \tabularnewline
86 & 93 & 80.64 & 12.36 \tabularnewline
87 & 76 & 80.64 & -4.64 \tabularnewline
88 & 49 & 47.0909090909091 & 1.90909090909091 \tabularnewline
89 & 118 & 122.046875 & -4.046875 \tabularnewline
90 & 38 & 47.0909090909091 & -9.09090909090909 \tabularnewline
91 & 141 & 122.046875 & 18.953125 \tabularnewline
92 & 58 & 47.0909090909091 & 10.9090909090909 \tabularnewline
93 & 27 & 40.2352941176471 & -13.2352941176471 \tabularnewline
94 & 91 & 102.928571428571 & -11.9285714285714 \tabularnewline
95 & 48 & 47.0909090909091 & 0.909090909090907 \tabularnewline
96 & 63 & 122.046875 & -59.046875 \tabularnewline
97 & 56 & 122.046875 & -66.046875 \tabularnewline
98 & 144 & 102.928571428571 & 41.0714285714286 \tabularnewline
99 & 73 & 74.3636363636364 & -1.36363636363636 \tabularnewline
100 & 168 & 122.046875 & 45.953125 \tabularnewline
101 & 64 & 47.0909090909091 & 16.9090909090909 \tabularnewline
102 & 97 & 122.046875 & -25.046875 \tabularnewline
103 & 117 & 122.046875 & -5.046875 \tabularnewline
104 & 100 & 102.928571428571 & -2.92857142857143 \tabularnewline
105 & 149 & 122.046875 & 26.953125 \tabularnewline
106 & 187 & 175.454545454545 & 11.5454545454545 \tabularnewline
107 & 127 & 122.046875 & 4.953125 \tabularnewline
108 & 37 & 47.0909090909091 & -10.0909090909091 \tabularnewline
109 & 245 & 175.454545454545 & 69.5454545454545 \tabularnewline
110 & 87 & 122.046875 & -35.046875 \tabularnewline
111 & 177 & 122.046875 & 54.953125 \tabularnewline
112 & 49 & 47.0909090909091 & 1.90909090909091 \tabularnewline
113 & 49 & 47.0909090909091 & 1.90909090909091 \tabularnewline
114 & 73 & 80.64 & -7.64 \tabularnewline
115 & 177 & 175.454545454545 & 1.54545454545453 \tabularnewline
116 & 94 & 122.046875 & -28.046875 \tabularnewline
117 & 117 & 122.046875 & -5.046875 \tabularnewline
118 & 60 & 74.3636363636364 & -14.3636363636364 \tabularnewline
119 & 55 & 102.928571428571 & -47.9285714285714 \tabularnewline
120 & 39 & 26.675 & 12.325 \tabularnewline
121 & 64 & 80.64 & -16.64 \tabularnewline
122 & 26 & 26.675 & -0.675000000000001 \tabularnewline
123 & 64 & 47.0909090909091 & 16.9090909090909 \tabularnewline
124 & 58 & 40.2352941176471 & 17.7647058823529 \tabularnewline
125 & 95 & 122.046875 & -27.046875 \tabularnewline
126 & 25 & 26.675 & -1.675 \tabularnewline
127 & 26 & 40.2352941176471 & -14.2352941176471 \tabularnewline
128 & 76 & 80.64 & -4.64 \tabularnewline
129 & 129 & 122.046875 & 6.953125 \tabularnewline
130 & 11 & 8.59259259259259 & 2.40740740740741 \tabularnewline
131 & 2 & 8.59259259259259 & -6.59259259259259 \tabularnewline
132 & 101 & 102.928571428571 & -1.92857142857143 \tabularnewline
133 & 28 & 26.675 & 1.325 \tabularnewline
134 & 36 & 47.0909090909091 & -11.0909090909091 \tabularnewline
135 & 89 & 47.0909090909091 & 41.9090909090909 \tabularnewline
136 & 193 & 122.046875 & 70.953125 \tabularnewline
137 & 4 & 8.59259259259259 & -4.59259259259259 \tabularnewline
138 & 84 & 74.3636363636364 & 9.63636363636364 \tabularnewline
139 & 23 & 25.6923076923077 & -2.69230769230769 \tabularnewline
140 & 39 & 25.6923076923077 & 13.3076923076923 \tabularnewline
141 & 14 & 8.59259259259259 & 5.40740740740741 \tabularnewline
142 & 78 & 47.0909090909091 & 30.9090909090909 \tabularnewline
143 & 14 & 26.675 & -12.675 \tabularnewline
144 & 101 & 122.046875 & -21.046875 \tabularnewline
145 & 82 & 122.046875 & -40.046875 \tabularnewline
146 & 24 & 26.675 & -2.675 \tabularnewline
147 & 36 & 40.2352941176471 & -4.23529411764706 \tabularnewline
148 & 75 & 47.0909090909091 & 27.9090909090909 \tabularnewline
149 & 16 & 8.59259259259259 & 7.40740740740741 \tabularnewline
150 & 55 & 80.64 & -25.64 \tabularnewline
151 & 131 & 122.046875 & 8.953125 \tabularnewline
152 & 131 & 102.928571428571 & 28.0714285714286 \tabularnewline
153 & 39 & 40.2352941176471 & -1.23529411764706 \tabularnewline
154 & 144 & 122.046875 & 21.953125 \tabularnewline
155 & 139 & 122.046875 & 16.953125 \tabularnewline
156 & 211 & 175.454545454545 & 35.5454545454545 \tabularnewline
157 & 78 & 80.64 & -2.64 \tabularnewline
158 & 50 & 40.2352941176471 & 9.76470588235294 \tabularnewline
159 & 39 & 47.0909090909091 & -8.09090909090909 \tabularnewline
160 & 90 & 122.046875 & -32.046875 \tabularnewline
161 & 166 & 122.046875 & 43.953125 \tabularnewline
162 & 12 & 26.675 & -14.675 \tabularnewline
163 & 57 & 47.0909090909091 & 9.90909090909091 \tabularnewline
164 & 133 & 175.454545454545 & -42.4545454545455 \tabularnewline
165 & 69 & 47.0909090909091 & 21.9090909090909 \tabularnewline
166 & 119 & 122.046875 & -3.046875 \tabularnewline
167 & 119 & 74.3636363636364 & 44.6363636363636 \tabularnewline
168 & 65 & 47.0909090909091 & 17.9090909090909 \tabularnewline
169 & 61 & 47.0909090909091 & 13.9090909090909 \tabularnewline
170 & 49 & 74.3636363636364 & -25.3636363636364 \tabularnewline
171 & 101 & 122.046875 & -21.046875 \tabularnewline
172 & 196 & 122.046875 & 73.953125 \tabularnewline
173 & 15 & 40.2352941176471 & -25.2352941176471 \tabularnewline
174 & 136 & 122.046875 & 13.953125 \tabularnewline
175 & 89 & 80.64 & 8.36 \tabularnewline
176 & 40 & 47.0909090909091 & -7.09090909090909 \tabularnewline
177 & 123 & 122.046875 & 0.953125 \tabularnewline
178 & 21 & 26.675 & -5.675 \tabularnewline
179 & 163 & 122.046875 & 40.953125 \tabularnewline
180 & 29 & 47.0909090909091 & -18.0909090909091 \tabularnewline
181 & 35 & 26.675 & 8.325 \tabularnewline
182 & 13 & 26.675 & -13.675 \tabularnewline
183 & 5 & 8.59259259259259 & -3.59259259259259 \tabularnewline
184 & 96 & 122.046875 & -26.046875 \tabularnewline
185 & 151 & 122.046875 & 28.953125 \tabularnewline
186 & 6 & 8.59259259259259 & -2.59259259259259 \tabularnewline
187 & 13 & 8.59259259259259 & 4.40740740740741 \tabularnewline
188 & 3 & 8.59259259259259 & -5.59259259259259 \tabularnewline
189 & 56 & 47.0909090909091 & 8.90909090909091 \tabularnewline
190 & 23 & 26.675 & -3.675 \tabularnewline
191 & 57 & 80.64 & -23.64 \tabularnewline
192 & 14 & 47.0909090909091 & -33.0909090909091 \tabularnewline
193 & 43 & 47.0909090909091 & -4.09090909090909 \tabularnewline
194 & 20 & 25.6923076923077 & -5.69230769230769 \tabularnewline
195 & 72 & 80.64 & -8.64 \tabularnewline
196 & 87 & 47.0909090909091 & 39.9090909090909 \tabularnewline
197 & 21 & 47.0909090909091 & -26.0909090909091 \tabularnewline
198 & 56 & 47.0909090909091 & 8.90909090909091 \tabularnewline
199 & 59 & 40.2352941176471 & 18.7647058823529 \tabularnewline
200 & 82 & 47.0909090909091 & 34.9090909090909 \tabularnewline
201 & 43 & 47.0909090909091 & -4.09090909090909 \tabularnewline
202 & 25 & 8.59259259259259 & 16.4074074074074 \tabularnewline
203 & 38 & 47.0909090909091 & -9.09090909090909 \tabularnewline
204 & 25 & 47.0909090909091 & -22.0909090909091 \tabularnewline
205 & 38 & 26.675 & 11.325 \tabularnewline
206 & 12 & 8.59259259259259 & 3.40740740740741 \tabularnewline
207 & 29 & 47.0909090909091 & -18.0909090909091 \tabularnewline
208 & 47 & 74.3636363636364 & -27.3636363636364 \tabularnewline
209 & 45 & 47.0909090909091 & -2.09090909090909 \tabularnewline
210 & 40 & 47.0909090909091 & -7.09090909090909 \tabularnewline
211 & 30 & 26.675 & 3.325 \tabularnewline
212 & 41 & 40.2352941176471 & 0.764705882352942 \tabularnewline
213 & 25 & 25.6923076923077 & -0.692307692307693 \tabularnewline
214 & 23 & 47.0909090909091 & -24.0909090909091 \tabularnewline
215 & 14 & 8.59259259259259 & 5.40740740740741 \tabularnewline
216 & 16 & 25.6923076923077 & -9.69230769230769 \tabularnewline
217 & 26 & 26.675 & -0.675000000000001 \tabularnewline
218 & 21 & 25.6923076923077 & -4.69230769230769 \tabularnewline
219 & 27 & 26.675 & 0.324999999999999 \tabularnewline
220 & 9 & 26.675 & -17.675 \tabularnewline
221 & 33 & 47.0909090909091 & -14.0909090909091 \tabularnewline
222 & 42 & 80.64 & -38.64 \tabularnewline
223 & 68 & 47.0909090909091 & 20.9090909090909 \tabularnewline
224 & 32 & 47.0909090909091 & -15.0909090909091 \tabularnewline
225 & 6 & 8.59259259259259 & -2.59259259259259 \tabularnewline
226 & 67 & 40.2352941176471 & 26.7647058823529 \tabularnewline
227 & 33 & 26.675 & 6.325 \tabularnewline
228 & 77 & 47.0909090909091 & 29.9090909090909 \tabularnewline
229 & 46 & 26.675 & 19.325 \tabularnewline
230 & 30 & 26.675 & 3.325 \tabularnewline
231 & 0 & 8.59259259259259 & -8.59259259259259 \tabularnewline
232 & 36 & 47.0909090909091 & -11.0909090909091 \tabularnewline
233 & 46 & 47.0909090909091 & -1.09090909090909 \tabularnewline
234 & 18 & 25.6923076923077 & -7.69230769230769 \tabularnewline
235 & 48 & 47.0909090909091 & 0.909090909090907 \tabularnewline
236 & 29 & 47.0909090909091 & -18.0909090909091 \tabularnewline
237 & 28 & 40.2352941176471 & -12.2352941176471 \tabularnewline
238 & 34 & 25.6923076923077 & 8.30769230769231 \tabularnewline
239 & 33 & 26.675 & 6.325 \tabularnewline
240 & 34 & 25.6923076923077 & 8.30769230769231 \tabularnewline
241 & 33 & 26.675 & 6.325 \tabularnewline
242 & 80 & 80.64 & -0.640000000000001 \tabularnewline
243 & 32 & 25.6923076923077 & 6.30769230769231 \tabularnewline
244 & 30 & 47.0909090909091 & -17.0909090909091 \tabularnewline
245 & 41 & 26.675 & 14.325 \tabularnewline
246 & 41 & 47.0909090909091 & -6.09090909090909 \tabularnewline
247 & 51 & 47.0909090909091 & 3.90909090909091 \tabularnewline
248 & 18 & 26.675 & -8.675 \tabularnewline
249 & 34 & 26.675 & 7.325 \tabularnewline
250 & 31 & 47.0909090909091 & -16.0909090909091 \tabularnewline
251 & 39 & 47.0909090909091 & -8.09090909090909 \tabularnewline
252 & 54 & 47.0909090909091 & 6.90909090909091 \tabularnewline
253 & 14 & 8.59259259259259 & 5.40740740740741 \tabularnewline
254 & 24 & 40.2352941176471 & -16.2352941176471 \tabularnewline
255 & 24 & 26.675 & -2.675 \tabularnewline
256 & 8 & 8.59259259259259 & -0.592592592592593 \tabularnewline
257 & 26 & 25.6923076923077 & 0.307692307692307 \tabularnewline
258 & 19 & 26.675 & -7.675 \tabularnewline
259 & 11 & 26.675 & -15.675 \tabularnewline
260 & 14 & 25.6923076923077 & -11.6923076923077 \tabularnewline
261 & 1 & 8.59259259259259 & -7.59259259259259 \tabularnewline
262 & 39 & 47.0909090909091 & -8.09090909090909 \tabularnewline
263 & 5 & 8.59259259259259 & -3.59259259259259 \tabularnewline
264 & 37 & 47.0909090909091 & -10.0909090909091 \tabularnewline
265 & 32 & 25.6923076923077 & 6.30769230769231 \tabularnewline
266 & 38 & 26.675 & 11.325 \tabularnewline
267 & 47 & 47.0909090909091 & -0.0909090909090935 \tabularnewline
268 & 47 & 26.675 & 20.325 \tabularnewline
269 & 37 & 47.0909090909091 & -10.0909090909091 \tabularnewline
270 & 51 & 47.0909090909091 & 3.90909090909091 \tabularnewline
271 & 45 & 40.2352941176471 & 4.76470588235294 \tabularnewline
272 & 21 & 26.675 & -5.675 \tabularnewline
273 & 1 & 8.59259259259259 & -7.59259259259259 \tabularnewline
274 & 42 & 40.2352941176471 & 1.76470588235294 \tabularnewline
275 & 26 & 26.675 & -0.675000000000001 \tabularnewline
276 & 21 & 26.675 & -5.675 \tabularnewline
277 & 4 & 8.59259259259259 & -4.59259259259259 \tabularnewline
278 & 10 & 8.59259259259259 & 1.40740740740741 \tabularnewline
279 & 43 & 47.0909090909091 & -4.09090909090909 \tabularnewline
280 & 34 & 26.675 & 7.325 \tabularnewline
281 & 31 & 26.675 & 4.325 \tabularnewline
282 & 19 & 26.675 & -7.675 \tabularnewline
283 & 34 & 26.675 & 7.325 \tabularnewline
284 & 6 & 8.59259259259259 & -2.59259259259259 \tabularnewline
285 & 11 & 26.675 & -15.675 \tabularnewline
286 & 24 & 8.59259259259259 & 15.4074074074074 \tabularnewline
287 & 16 & 8.59259259259259 & 7.40740740740741 \tabularnewline
288 & 72 & 47.0909090909091 & 24.9090909090909 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197395&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]145[/C][C]122.046875[/C][C]22.953125[/C][/ROW]
[ROW][C]2[/C][C]101[/C][C]80.64[/C][C]20.36[/C][/ROW]
[ROW][C]3[/C][C]98[/C][C]80.64[/C][C]17.36[/C][/ROW]
[ROW][C]4[/C][C]132[/C][C]122.046875[/C][C]9.953125[/C][/ROW]
[ROW][C]5[/C][C]60[/C][C]47.0909090909091[/C][C]12.9090909090909[/C][/ROW]
[ROW][C]6[/C][C]38[/C][C]47.0909090909091[/C][C]-9.09090909090909[/C][/ROW]
[ROW][C]7[/C][C]144[/C][C]122.046875[/C][C]21.953125[/C][/ROW]
[ROW][C]8[/C][C]5[/C][C]8.59259259259259[/C][C]-3.59259259259259[/C][/ROW]
[ROW][C]9[/C][C]28[/C][C]40.2352941176471[/C][C]-12.2352941176471[/C][/ROW]
[ROW][C]10[/C][C]84[/C][C]102.928571428571[/C][C]-18.9285714285714[/C][/ROW]
[ROW][C]11[/C][C]79[/C][C]80.64[/C][C]-1.64[/C][/ROW]
[ROW][C]12[/C][C]127[/C][C]122.046875[/C][C]4.953125[/C][/ROW]
[ROW][C]13[/C][C]78[/C][C]80.64[/C][C]-2.64[/C][/ROW]
[ROW][C]14[/C][C]60[/C][C]47.0909090909091[/C][C]12.9090909090909[/C][/ROW]
[ROW][C]15[/C][C]131[/C][C]122.046875[/C][C]8.953125[/C][/ROW]
[ROW][C]16[/C][C]84[/C][C]74.3636363636364[/C][C]9.63636363636364[/C][/ROW]
[ROW][C]17[/C][C]133[/C][C]122.046875[/C][C]10.953125[/C][/ROW]
[ROW][C]18[/C][C]150[/C][C]122.046875[/C][C]27.953125[/C][/ROW]
[ROW][C]19[/C][C]91[/C][C]122.046875[/C][C]-31.046875[/C][/ROW]
[ROW][C]20[/C][C]132[/C][C]102.928571428571[/C][C]29.0714285714286[/C][/ROW]
[ROW][C]21[/C][C]136[/C][C]122.046875[/C][C]13.953125[/C][/ROW]
[ROW][C]22[/C][C]124[/C][C]175.454545454545[/C][C]-51.4545454545455[/C][/ROW]
[ROW][C]23[/C][C]118[/C][C]122.046875[/C][C]-4.046875[/C][/ROW]
[ROW][C]24[/C][C]70[/C][C]80.64[/C][C]-10.64[/C][/ROW]
[ROW][C]25[/C][C]107[/C][C]80.64[/C][C]26.36[/C][/ROW]
[ROW][C]26[/C][C]119[/C][C]102.928571428571[/C][C]16.0714285714286[/C][/ROW]
[ROW][C]27[/C][C]89[/C][C]102.928571428571[/C][C]-13.9285714285714[/C][/ROW]
[ROW][C]28[/C][C]112[/C][C]122.046875[/C][C]-10.046875[/C][/ROW]
[ROW][C]29[/C][C]108[/C][C]122.046875[/C][C]-14.046875[/C][/ROW]
[ROW][C]30[/C][C]52[/C][C]47.0909090909091[/C][C]4.90909090909091[/C][/ROW]
[ROW][C]31[/C][C]112[/C][C]122.046875[/C][C]-10.046875[/C][/ROW]
[ROW][C]32[/C][C]116[/C][C]175.454545454545[/C][C]-59.4545454545455[/C][/ROW]
[ROW][C]33[/C][C]123[/C][C]74.3636363636364[/C][C]48.6363636363636[/C][/ROW]
[ROW][C]34[/C][C]125[/C][C]122.046875[/C][C]2.953125[/C][/ROW]
[ROW][C]35[/C][C]27[/C][C]26.675[/C][C]0.324999999999999[/C][/ROW]
[ROW][C]36[/C][C]162[/C][C]175.454545454545[/C][C]-13.4545454545455[/C][/ROW]
[ROW][C]37[/C][C]32[/C][C]47.0909090909091[/C][C]-15.0909090909091[/C][/ROW]
[ROW][C]38[/C][C]64[/C][C]40.2352941176471[/C][C]23.7647058823529[/C][/ROW]
[ROW][C]39[/C][C]92[/C][C]122.046875[/C][C]-30.046875[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]8.59259259259259[/C][C]-8.59259259259259[/C][/ROW]
[ROW][C]41[/C][C]83[/C][C]80.64[/C][C]2.36[/C][/ROW]
[ROW][C]42[/C][C]41[/C][C]47.0909090909091[/C][C]-6.09090909090909[/C][/ROW]
[ROW][C]43[/C][C]47[/C][C]47.0909090909091[/C][C]-0.0909090909090935[/C][/ROW]
[ROW][C]44[/C][C]120[/C][C]122.046875[/C][C]-2.046875[/C][/ROW]
[ROW][C]45[/C][C]105[/C][C]122.046875[/C][C]-17.046875[/C][/ROW]
[ROW][C]46[/C][C]79[/C][C]80.64[/C][C]-1.64[/C][/ROW]
[ROW][C]47[/C][C]65[/C][C]80.64[/C][C]-15.64[/C][/ROW]
[ROW][C]48[/C][C]70[/C][C]102.928571428571[/C][C]-32.9285714285714[/C][/ROW]
[ROW][C]49[/C][C]55[/C][C]47.0909090909091[/C][C]7.90909090909091[/C][/ROW]
[ROW][C]50[/C][C]39[/C][C]47.0909090909091[/C][C]-8.09090909090909[/C][/ROW]
[ROW][C]51[/C][C]67[/C][C]74.3636363636364[/C][C]-7.36363636363636[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]47.0909090909091[/C][C]-26.0909090909091[/C][/ROW]
[ROW][C]53[/C][C]127[/C][C]122.046875[/C][C]4.953125[/C][/ROW]
[ROW][C]54[/C][C]152[/C][C]122.046875[/C][C]29.953125[/C][/ROW]
[ROW][C]55[/C][C]113[/C][C]102.928571428571[/C][C]10.0714285714286[/C][/ROW]
[ROW][C]56[/C][C]99[/C][C]102.928571428571[/C][C]-3.92857142857143[/C][/ROW]
[ROW][C]57[/C][C]7[/C][C]8.59259259259259[/C][C]-1.59259259259259[/C][/ROW]
[ROW][C]58[/C][C]141[/C][C]122.046875[/C][C]18.953125[/C][/ROW]
[ROW][C]59[/C][C]21[/C][C]26.675[/C][C]-5.675[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]40.2352941176471[/C][C]-5.23529411764706[/C][/ROW]
[ROW][C]61[/C][C]109[/C][C]122.046875[/C][C]-13.046875[/C][/ROW]
[ROW][C]62[/C][C]133[/C][C]122.046875[/C][C]10.953125[/C][/ROW]
[ROW][C]63[/C][C]123[/C][C]122.046875[/C][C]0.953125[/C][/ROW]
[ROW][C]64[/C][C]26[/C][C]47.0909090909091[/C][C]-21.0909090909091[/C][/ROW]
[ROW][C]65[/C][C]230[/C][C]175.454545454545[/C][C]54.5454545454545[/C][/ROW]
[ROW][C]66[/C][C]166[/C][C]175.454545454545[/C][C]-9.45454545454547[/C][/ROW]
[ROW][C]67[/C][C]68[/C][C]122.046875[/C][C]-54.046875[/C][/ROW]
[ROW][C]68[/C][C]147[/C][C]122.046875[/C][C]24.953125[/C][/ROW]
[ROW][C]69[/C][C]179[/C][C]175.454545454545[/C][C]3.54545454545453[/C][/ROW]
[ROW][C]70[/C][C]61[/C][C]74.3636363636364[/C][C]-13.3636363636364[/C][/ROW]
[ROW][C]71[/C][C]101[/C][C]122.046875[/C][C]-21.046875[/C][/ROW]
[ROW][C]72[/C][C]108[/C][C]122.046875[/C][C]-14.046875[/C][/ROW]
[ROW][C]73[/C][C]90[/C][C]80.64[/C][C]9.36[/C][/ROW]
[ROW][C]74[/C][C]114[/C][C]80.64[/C][C]33.36[/C][/ROW]
[ROW][C]75[/C][C]103[/C][C]122.046875[/C][C]-19.046875[/C][/ROW]
[ROW][C]76[/C][C]142[/C][C]122.046875[/C][C]19.953125[/C][/ROW]
[ROW][C]77[/C][C]79[/C][C]80.64[/C][C]-1.64[/C][/ROW]
[ROW][C]78[/C][C]88[/C][C]122.046875[/C][C]-34.046875[/C][/ROW]
[ROW][C]79[/C][C]25[/C][C]26.675[/C][C]-1.675[/C][/ROW]
[ROW][C]80[/C][C]83[/C][C]122.046875[/C][C]-39.046875[/C][/ROW]
[ROW][C]81[/C][C]113[/C][C]102.928571428571[/C][C]10.0714285714286[/C][/ROW]
[ROW][C]82[/C][C]118[/C][C]80.64[/C][C]37.36[/C][/ROW]
[ROW][C]83[/C][C]110[/C][C]122.046875[/C][C]-12.046875[/C][/ROW]
[ROW][C]84[/C][C]129[/C][C]122.046875[/C][C]6.953125[/C][/ROW]
[ROW][C]85[/C][C]51[/C][C]74.3636363636364[/C][C]-23.3636363636364[/C][/ROW]
[ROW][C]86[/C][C]93[/C][C]80.64[/C][C]12.36[/C][/ROW]
[ROW][C]87[/C][C]76[/C][C]80.64[/C][C]-4.64[/C][/ROW]
[ROW][C]88[/C][C]49[/C][C]47.0909090909091[/C][C]1.90909090909091[/C][/ROW]
[ROW][C]89[/C][C]118[/C][C]122.046875[/C][C]-4.046875[/C][/ROW]
[ROW][C]90[/C][C]38[/C][C]47.0909090909091[/C][C]-9.09090909090909[/C][/ROW]
[ROW][C]91[/C][C]141[/C][C]122.046875[/C][C]18.953125[/C][/ROW]
[ROW][C]92[/C][C]58[/C][C]47.0909090909091[/C][C]10.9090909090909[/C][/ROW]
[ROW][C]93[/C][C]27[/C][C]40.2352941176471[/C][C]-13.2352941176471[/C][/ROW]
[ROW][C]94[/C][C]91[/C][C]102.928571428571[/C][C]-11.9285714285714[/C][/ROW]
[ROW][C]95[/C][C]48[/C][C]47.0909090909091[/C][C]0.909090909090907[/C][/ROW]
[ROW][C]96[/C][C]63[/C][C]122.046875[/C][C]-59.046875[/C][/ROW]
[ROW][C]97[/C][C]56[/C][C]122.046875[/C][C]-66.046875[/C][/ROW]
[ROW][C]98[/C][C]144[/C][C]102.928571428571[/C][C]41.0714285714286[/C][/ROW]
[ROW][C]99[/C][C]73[/C][C]74.3636363636364[/C][C]-1.36363636363636[/C][/ROW]
[ROW][C]100[/C][C]168[/C][C]122.046875[/C][C]45.953125[/C][/ROW]
[ROW][C]101[/C][C]64[/C][C]47.0909090909091[/C][C]16.9090909090909[/C][/ROW]
[ROW][C]102[/C][C]97[/C][C]122.046875[/C][C]-25.046875[/C][/ROW]
[ROW][C]103[/C][C]117[/C][C]122.046875[/C][C]-5.046875[/C][/ROW]
[ROW][C]104[/C][C]100[/C][C]102.928571428571[/C][C]-2.92857142857143[/C][/ROW]
[ROW][C]105[/C][C]149[/C][C]122.046875[/C][C]26.953125[/C][/ROW]
[ROW][C]106[/C][C]187[/C][C]175.454545454545[/C][C]11.5454545454545[/C][/ROW]
[ROW][C]107[/C][C]127[/C][C]122.046875[/C][C]4.953125[/C][/ROW]
[ROW][C]108[/C][C]37[/C][C]47.0909090909091[/C][C]-10.0909090909091[/C][/ROW]
[ROW][C]109[/C][C]245[/C][C]175.454545454545[/C][C]69.5454545454545[/C][/ROW]
[ROW][C]110[/C][C]87[/C][C]122.046875[/C][C]-35.046875[/C][/ROW]
[ROW][C]111[/C][C]177[/C][C]122.046875[/C][C]54.953125[/C][/ROW]
[ROW][C]112[/C][C]49[/C][C]47.0909090909091[/C][C]1.90909090909091[/C][/ROW]
[ROW][C]113[/C][C]49[/C][C]47.0909090909091[/C][C]1.90909090909091[/C][/ROW]
[ROW][C]114[/C][C]73[/C][C]80.64[/C][C]-7.64[/C][/ROW]
[ROW][C]115[/C][C]177[/C][C]175.454545454545[/C][C]1.54545454545453[/C][/ROW]
[ROW][C]116[/C][C]94[/C][C]122.046875[/C][C]-28.046875[/C][/ROW]
[ROW][C]117[/C][C]117[/C][C]122.046875[/C][C]-5.046875[/C][/ROW]
[ROW][C]118[/C][C]60[/C][C]74.3636363636364[/C][C]-14.3636363636364[/C][/ROW]
[ROW][C]119[/C][C]55[/C][C]102.928571428571[/C][C]-47.9285714285714[/C][/ROW]
[ROW][C]120[/C][C]39[/C][C]26.675[/C][C]12.325[/C][/ROW]
[ROW][C]121[/C][C]64[/C][C]80.64[/C][C]-16.64[/C][/ROW]
[ROW][C]122[/C][C]26[/C][C]26.675[/C][C]-0.675000000000001[/C][/ROW]
[ROW][C]123[/C][C]64[/C][C]47.0909090909091[/C][C]16.9090909090909[/C][/ROW]
[ROW][C]124[/C][C]58[/C][C]40.2352941176471[/C][C]17.7647058823529[/C][/ROW]
[ROW][C]125[/C][C]95[/C][C]122.046875[/C][C]-27.046875[/C][/ROW]
[ROW][C]126[/C][C]25[/C][C]26.675[/C][C]-1.675[/C][/ROW]
[ROW][C]127[/C][C]26[/C][C]40.2352941176471[/C][C]-14.2352941176471[/C][/ROW]
[ROW][C]128[/C][C]76[/C][C]80.64[/C][C]-4.64[/C][/ROW]
[ROW][C]129[/C][C]129[/C][C]122.046875[/C][C]6.953125[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]8.59259259259259[/C][C]2.40740740740741[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]8.59259259259259[/C][C]-6.59259259259259[/C][/ROW]
[ROW][C]132[/C][C]101[/C][C]102.928571428571[/C][C]-1.92857142857143[/C][/ROW]
[ROW][C]133[/C][C]28[/C][C]26.675[/C][C]1.325[/C][/ROW]
[ROW][C]134[/C][C]36[/C][C]47.0909090909091[/C][C]-11.0909090909091[/C][/ROW]
[ROW][C]135[/C][C]89[/C][C]47.0909090909091[/C][C]41.9090909090909[/C][/ROW]
[ROW][C]136[/C][C]193[/C][C]122.046875[/C][C]70.953125[/C][/ROW]
[ROW][C]137[/C][C]4[/C][C]8.59259259259259[/C][C]-4.59259259259259[/C][/ROW]
[ROW][C]138[/C][C]84[/C][C]74.3636363636364[/C][C]9.63636363636364[/C][/ROW]
[ROW][C]139[/C][C]23[/C][C]25.6923076923077[/C][C]-2.69230769230769[/C][/ROW]
[ROW][C]140[/C][C]39[/C][C]25.6923076923077[/C][C]13.3076923076923[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]8.59259259259259[/C][C]5.40740740740741[/C][/ROW]
[ROW][C]142[/C][C]78[/C][C]47.0909090909091[/C][C]30.9090909090909[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]26.675[/C][C]-12.675[/C][/ROW]
[ROW][C]144[/C][C]101[/C][C]122.046875[/C][C]-21.046875[/C][/ROW]
[ROW][C]145[/C][C]82[/C][C]122.046875[/C][C]-40.046875[/C][/ROW]
[ROW][C]146[/C][C]24[/C][C]26.675[/C][C]-2.675[/C][/ROW]
[ROW][C]147[/C][C]36[/C][C]40.2352941176471[/C][C]-4.23529411764706[/C][/ROW]
[ROW][C]148[/C][C]75[/C][C]47.0909090909091[/C][C]27.9090909090909[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]8.59259259259259[/C][C]7.40740740740741[/C][/ROW]
[ROW][C]150[/C][C]55[/C][C]80.64[/C][C]-25.64[/C][/ROW]
[ROW][C]151[/C][C]131[/C][C]122.046875[/C][C]8.953125[/C][/ROW]
[ROW][C]152[/C][C]131[/C][C]102.928571428571[/C][C]28.0714285714286[/C][/ROW]
[ROW][C]153[/C][C]39[/C][C]40.2352941176471[/C][C]-1.23529411764706[/C][/ROW]
[ROW][C]154[/C][C]144[/C][C]122.046875[/C][C]21.953125[/C][/ROW]
[ROW][C]155[/C][C]139[/C][C]122.046875[/C][C]16.953125[/C][/ROW]
[ROW][C]156[/C][C]211[/C][C]175.454545454545[/C][C]35.5454545454545[/C][/ROW]
[ROW][C]157[/C][C]78[/C][C]80.64[/C][C]-2.64[/C][/ROW]
[ROW][C]158[/C][C]50[/C][C]40.2352941176471[/C][C]9.76470588235294[/C][/ROW]
[ROW][C]159[/C][C]39[/C][C]47.0909090909091[/C][C]-8.09090909090909[/C][/ROW]
[ROW][C]160[/C][C]90[/C][C]122.046875[/C][C]-32.046875[/C][/ROW]
[ROW][C]161[/C][C]166[/C][C]122.046875[/C][C]43.953125[/C][/ROW]
[ROW][C]162[/C][C]12[/C][C]26.675[/C][C]-14.675[/C][/ROW]
[ROW][C]163[/C][C]57[/C][C]47.0909090909091[/C][C]9.90909090909091[/C][/ROW]
[ROW][C]164[/C][C]133[/C][C]175.454545454545[/C][C]-42.4545454545455[/C][/ROW]
[ROW][C]165[/C][C]69[/C][C]47.0909090909091[/C][C]21.9090909090909[/C][/ROW]
[ROW][C]166[/C][C]119[/C][C]122.046875[/C][C]-3.046875[/C][/ROW]
[ROW][C]167[/C][C]119[/C][C]74.3636363636364[/C][C]44.6363636363636[/C][/ROW]
[ROW][C]168[/C][C]65[/C][C]47.0909090909091[/C][C]17.9090909090909[/C][/ROW]
[ROW][C]169[/C][C]61[/C][C]47.0909090909091[/C][C]13.9090909090909[/C][/ROW]
[ROW][C]170[/C][C]49[/C][C]74.3636363636364[/C][C]-25.3636363636364[/C][/ROW]
[ROW][C]171[/C][C]101[/C][C]122.046875[/C][C]-21.046875[/C][/ROW]
[ROW][C]172[/C][C]196[/C][C]122.046875[/C][C]73.953125[/C][/ROW]
[ROW][C]173[/C][C]15[/C][C]40.2352941176471[/C][C]-25.2352941176471[/C][/ROW]
[ROW][C]174[/C][C]136[/C][C]122.046875[/C][C]13.953125[/C][/ROW]
[ROW][C]175[/C][C]89[/C][C]80.64[/C][C]8.36[/C][/ROW]
[ROW][C]176[/C][C]40[/C][C]47.0909090909091[/C][C]-7.09090909090909[/C][/ROW]
[ROW][C]177[/C][C]123[/C][C]122.046875[/C][C]0.953125[/C][/ROW]
[ROW][C]178[/C][C]21[/C][C]26.675[/C][C]-5.675[/C][/ROW]
[ROW][C]179[/C][C]163[/C][C]122.046875[/C][C]40.953125[/C][/ROW]
[ROW][C]180[/C][C]29[/C][C]47.0909090909091[/C][C]-18.0909090909091[/C][/ROW]
[ROW][C]181[/C][C]35[/C][C]26.675[/C][C]8.325[/C][/ROW]
[ROW][C]182[/C][C]13[/C][C]26.675[/C][C]-13.675[/C][/ROW]
[ROW][C]183[/C][C]5[/C][C]8.59259259259259[/C][C]-3.59259259259259[/C][/ROW]
[ROW][C]184[/C][C]96[/C][C]122.046875[/C][C]-26.046875[/C][/ROW]
[ROW][C]185[/C][C]151[/C][C]122.046875[/C][C]28.953125[/C][/ROW]
[ROW][C]186[/C][C]6[/C][C]8.59259259259259[/C][C]-2.59259259259259[/C][/ROW]
[ROW][C]187[/C][C]13[/C][C]8.59259259259259[/C][C]4.40740740740741[/C][/ROW]
[ROW][C]188[/C][C]3[/C][C]8.59259259259259[/C][C]-5.59259259259259[/C][/ROW]
[ROW][C]189[/C][C]56[/C][C]47.0909090909091[/C][C]8.90909090909091[/C][/ROW]
[ROW][C]190[/C][C]23[/C][C]26.675[/C][C]-3.675[/C][/ROW]
[ROW][C]191[/C][C]57[/C][C]80.64[/C][C]-23.64[/C][/ROW]
[ROW][C]192[/C][C]14[/C][C]47.0909090909091[/C][C]-33.0909090909091[/C][/ROW]
[ROW][C]193[/C][C]43[/C][C]47.0909090909091[/C][C]-4.09090909090909[/C][/ROW]
[ROW][C]194[/C][C]20[/C][C]25.6923076923077[/C][C]-5.69230769230769[/C][/ROW]
[ROW][C]195[/C][C]72[/C][C]80.64[/C][C]-8.64[/C][/ROW]
[ROW][C]196[/C][C]87[/C][C]47.0909090909091[/C][C]39.9090909090909[/C][/ROW]
[ROW][C]197[/C][C]21[/C][C]47.0909090909091[/C][C]-26.0909090909091[/C][/ROW]
[ROW][C]198[/C][C]56[/C][C]47.0909090909091[/C][C]8.90909090909091[/C][/ROW]
[ROW][C]199[/C][C]59[/C][C]40.2352941176471[/C][C]18.7647058823529[/C][/ROW]
[ROW][C]200[/C][C]82[/C][C]47.0909090909091[/C][C]34.9090909090909[/C][/ROW]
[ROW][C]201[/C][C]43[/C][C]47.0909090909091[/C][C]-4.09090909090909[/C][/ROW]
[ROW][C]202[/C][C]25[/C][C]8.59259259259259[/C][C]16.4074074074074[/C][/ROW]
[ROW][C]203[/C][C]38[/C][C]47.0909090909091[/C][C]-9.09090909090909[/C][/ROW]
[ROW][C]204[/C][C]25[/C][C]47.0909090909091[/C][C]-22.0909090909091[/C][/ROW]
[ROW][C]205[/C][C]38[/C][C]26.675[/C][C]11.325[/C][/ROW]
[ROW][C]206[/C][C]12[/C][C]8.59259259259259[/C][C]3.40740740740741[/C][/ROW]
[ROW][C]207[/C][C]29[/C][C]47.0909090909091[/C][C]-18.0909090909091[/C][/ROW]
[ROW][C]208[/C][C]47[/C][C]74.3636363636364[/C][C]-27.3636363636364[/C][/ROW]
[ROW][C]209[/C][C]45[/C][C]47.0909090909091[/C][C]-2.09090909090909[/C][/ROW]
[ROW][C]210[/C][C]40[/C][C]47.0909090909091[/C][C]-7.09090909090909[/C][/ROW]
[ROW][C]211[/C][C]30[/C][C]26.675[/C][C]3.325[/C][/ROW]
[ROW][C]212[/C][C]41[/C][C]40.2352941176471[/C][C]0.764705882352942[/C][/ROW]
[ROW][C]213[/C][C]25[/C][C]25.6923076923077[/C][C]-0.692307692307693[/C][/ROW]
[ROW][C]214[/C][C]23[/C][C]47.0909090909091[/C][C]-24.0909090909091[/C][/ROW]
[ROW][C]215[/C][C]14[/C][C]8.59259259259259[/C][C]5.40740740740741[/C][/ROW]
[ROW][C]216[/C][C]16[/C][C]25.6923076923077[/C][C]-9.69230769230769[/C][/ROW]
[ROW][C]217[/C][C]26[/C][C]26.675[/C][C]-0.675000000000001[/C][/ROW]
[ROW][C]218[/C][C]21[/C][C]25.6923076923077[/C][C]-4.69230769230769[/C][/ROW]
[ROW][C]219[/C][C]27[/C][C]26.675[/C][C]0.324999999999999[/C][/ROW]
[ROW][C]220[/C][C]9[/C][C]26.675[/C][C]-17.675[/C][/ROW]
[ROW][C]221[/C][C]33[/C][C]47.0909090909091[/C][C]-14.0909090909091[/C][/ROW]
[ROW][C]222[/C][C]42[/C][C]80.64[/C][C]-38.64[/C][/ROW]
[ROW][C]223[/C][C]68[/C][C]47.0909090909091[/C][C]20.9090909090909[/C][/ROW]
[ROW][C]224[/C][C]32[/C][C]47.0909090909091[/C][C]-15.0909090909091[/C][/ROW]
[ROW][C]225[/C][C]6[/C][C]8.59259259259259[/C][C]-2.59259259259259[/C][/ROW]
[ROW][C]226[/C][C]67[/C][C]40.2352941176471[/C][C]26.7647058823529[/C][/ROW]
[ROW][C]227[/C][C]33[/C][C]26.675[/C][C]6.325[/C][/ROW]
[ROW][C]228[/C][C]77[/C][C]47.0909090909091[/C][C]29.9090909090909[/C][/ROW]
[ROW][C]229[/C][C]46[/C][C]26.675[/C][C]19.325[/C][/ROW]
[ROW][C]230[/C][C]30[/C][C]26.675[/C][C]3.325[/C][/ROW]
[ROW][C]231[/C][C]0[/C][C]8.59259259259259[/C][C]-8.59259259259259[/C][/ROW]
[ROW][C]232[/C][C]36[/C][C]47.0909090909091[/C][C]-11.0909090909091[/C][/ROW]
[ROW][C]233[/C][C]46[/C][C]47.0909090909091[/C][C]-1.09090909090909[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]25.6923076923077[/C][C]-7.69230769230769[/C][/ROW]
[ROW][C]235[/C][C]48[/C][C]47.0909090909091[/C][C]0.909090909090907[/C][/ROW]
[ROW][C]236[/C][C]29[/C][C]47.0909090909091[/C][C]-18.0909090909091[/C][/ROW]
[ROW][C]237[/C][C]28[/C][C]40.2352941176471[/C][C]-12.2352941176471[/C][/ROW]
[ROW][C]238[/C][C]34[/C][C]25.6923076923077[/C][C]8.30769230769231[/C][/ROW]
[ROW][C]239[/C][C]33[/C][C]26.675[/C][C]6.325[/C][/ROW]
[ROW][C]240[/C][C]34[/C][C]25.6923076923077[/C][C]8.30769230769231[/C][/ROW]
[ROW][C]241[/C][C]33[/C][C]26.675[/C][C]6.325[/C][/ROW]
[ROW][C]242[/C][C]80[/C][C]80.64[/C][C]-0.640000000000001[/C][/ROW]
[ROW][C]243[/C][C]32[/C][C]25.6923076923077[/C][C]6.30769230769231[/C][/ROW]
[ROW][C]244[/C][C]30[/C][C]47.0909090909091[/C][C]-17.0909090909091[/C][/ROW]
[ROW][C]245[/C][C]41[/C][C]26.675[/C][C]14.325[/C][/ROW]
[ROW][C]246[/C][C]41[/C][C]47.0909090909091[/C][C]-6.09090909090909[/C][/ROW]
[ROW][C]247[/C][C]51[/C][C]47.0909090909091[/C][C]3.90909090909091[/C][/ROW]
[ROW][C]248[/C][C]18[/C][C]26.675[/C][C]-8.675[/C][/ROW]
[ROW][C]249[/C][C]34[/C][C]26.675[/C][C]7.325[/C][/ROW]
[ROW][C]250[/C][C]31[/C][C]47.0909090909091[/C][C]-16.0909090909091[/C][/ROW]
[ROW][C]251[/C][C]39[/C][C]47.0909090909091[/C][C]-8.09090909090909[/C][/ROW]
[ROW][C]252[/C][C]54[/C][C]47.0909090909091[/C][C]6.90909090909091[/C][/ROW]
[ROW][C]253[/C][C]14[/C][C]8.59259259259259[/C][C]5.40740740740741[/C][/ROW]
[ROW][C]254[/C][C]24[/C][C]40.2352941176471[/C][C]-16.2352941176471[/C][/ROW]
[ROW][C]255[/C][C]24[/C][C]26.675[/C][C]-2.675[/C][/ROW]
[ROW][C]256[/C][C]8[/C][C]8.59259259259259[/C][C]-0.592592592592593[/C][/ROW]
[ROW][C]257[/C][C]26[/C][C]25.6923076923077[/C][C]0.307692307692307[/C][/ROW]
[ROW][C]258[/C][C]19[/C][C]26.675[/C][C]-7.675[/C][/ROW]
[ROW][C]259[/C][C]11[/C][C]26.675[/C][C]-15.675[/C][/ROW]
[ROW][C]260[/C][C]14[/C][C]25.6923076923077[/C][C]-11.6923076923077[/C][/ROW]
[ROW][C]261[/C][C]1[/C][C]8.59259259259259[/C][C]-7.59259259259259[/C][/ROW]
[ROW][C]262[/C][C]39[/C][C]47.0909090909091[/C][C]-8.09090909090909[/C][/ROW]
[ROW][C]263[/C][C]5[/C][C]8.59259259259259[/C][C]-3.59259259259259[/C][/ROW]
[ROW][C]264[/C][C]37[/C][C]47.0909090909091[/C][C]-10.0909090909091[/C][/ROW]
[ROW][C]265[/C][C]32[/C][C]25.6923076923077[/C][C]6.30769230769231[/C][/ROW]
[ROW][C]266[/C][C]38[/C][C]26.675[/C][C]11.325[/C][/ROW]
[ROW][C]267[/C][C]47[/C][C]47.0909090909091[/C][C]-0.0909090909090935[/C][/ROW]
[ROW][C]268[/C][C]47[/C][C]26.675[/C][C]20.325[/C][/ROW]
[ROW][C]269[/C][C]37[/C][C]47.0909090909091[/C][C]-10.0909090909091[/C][/ROW]
[ROW][C]270[/C][C]51[/C][C]47.0909090909091[/C][C]3.90909090909091[/C][/ROW]
[ROW][C]271[/C][C]45[/C][C]40.2352941176471[/C][C]4.76470588235294[/C][/ROW]
[ROW][C]272[/C][C]21[/C][C]26.675[/C][C]-5.675[/C][/ROW]
[ROW][C]273[/C][C]1[/C][C]8.59259259259259[/C][C]-7.59259259259259[/C][/ROW]
[ROW][C]274[/C][C]42[/C][C]40.2352941176471[/C][C]1.76470588235294[/C][/ROW]
[ROW][C]275[/C][C]26[/C][C]26.675[/C][C]-0.675000000000001[/C][/ROW]
[ROW][C]276[/C][C]21[/C][C]26.675[/C][C]-5.675[/C][/ROW]
[ROW][C]277[/C][C]4[/C][C]8.59259259259259[/C][C]-4.59259259259259[/C][/ROW]
[ROW][C]278[/C][C]10[/C][C]8.59259259259259[/C][C]1.40740740740741[/C][/ROW]
[ROW][C]279[/C][C]43[/C][C]47.0909090909091[/C][C]-4.09090909090909[/C][/ROW]
[ROW][C]280[/C][C]34[/C][C]26.675[/C][C]7.325[/C][/ROW]
[ROW][C]281[/C][C]31[/C][C]26.675[/C][C]4.325[/C][/ROW]
[ROW][C]282[/C][C]19[/C][C]26.675[/C][C]-7.675[/C][/ROW]
[ROW][C]283[/C][C]34[/C][C]26.675[/C][C]7.325[/C][/ROW]
[ROW][C]284[/C][C]6[/C][C]8.59259259259259[/C][C]-2.59259259259259[/C][/ROW]
[ROW][C]285[/C][C]11[/C][C]26.675[/C][C]-15.675[/C][/ROW]
[ROW][C]286[/C][C]24[/C][C]8.59259259259259[/C][C]15.4074074074074[/C][/ROW]
[ROW][C]287[/C][C]16[/C][C]8.59259259259259[/C][C]7.40740740740741[/C][/ROW]
[ROW][C]288[/C][C]72[/C][C]47.0909090909091[/C][C]24.9090909090909[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197395&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197395&T=2

As an alternative you can also use a QR Code:  

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

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1145122.04687522.953125
210180.6420.36
39880.6417.36
4132122.0468759.953125
56047.090909090909112.9090909090909
63847.0909090909091-9.09090909090909
7144122.04687521.953125
858.59259259259259-3.59259259259259
92840.2352941176471-12.2352941176471
1084102.928571428571-18.9285714285714
117980.64-1.64
12127122.0468754.953125
137880.64-2.64
146047.090909090909112.9090909090909
15131122.0468758.953125
168474.36363636363649.63636363636364
17133122.04687510.953125
18150122.04687527.953125
1991122.046875-31.046875
20132102.92857142857129.0714285714286
21136122.04687513.953125
22124175.454545454545-51.4545454545455
23118122.046875-4.046875
247080.64-10.64
2510780.6426.36
26119102.92857142857116.0714285714286
2789102.928571428571-13.9285714285714
28112122.046875-10.046875
29108122.046875-14.046875
305247.09090909090914.90909090909091
31112122.046875-10.046875
32116175.454545454545-59.4545454545455
3312374.363636363636448.6363636363636
34125122.0468752.953125
352726.6750.324999999999999
36162175.454545454545-13.4545454545455
373247.0909090909091-15.0909090909091
386440.235294117647123.7647058823529
3992122.046875-30.046875
4008.59259259259259-8.59259259259259
418380.642.36
424147.0909090909091-6.09090909090909
434747.0909090909091-0.0909090909090935
44120122.046875-2.046875
45105122.046875-17.046875
467980.64-1.64
476580.64-15.64
4870102.928571428571-32.9285714285714
495547.09090909090917.90909090909091
503947.0909090909091-8.09090909090909
516774.3636363636364-7.36363636363636
522147.0909090909091-26.0909090909091
53127122.0468754.953125
54152122.04687529.953125
55113102.92857142857110.0714285714286
5699102.928571428571-3.92857142857143
5778.59259259259259-1.59259259259259
58141122.04687518.953125
592126.675-5.675
603540.2352941176471-5.23529411764706
61109122.046875-13.046875
62133122.04687510.953125
63123122.0468750.953125
642647.0909090909091-21.0909090909091
65230175.45454545454554.5454545454545
66166175.454545454545-9.45454545454547
6768122.046875-54.046875
68147122.04687524.953125
69179175.4545454545453.54545454545453
706174.3636363636364-13.3636363636364
71101122.046875-21.046875
72108122.046875-14.046875
739080.649.36
7411480.6433.36
75103122.046875-19.046875
76142122.04687519.953125
777980.64-1.64
7888122.046875-34.046875
792526.675-1.675
8083122.046875-39.046875
81113102.92857142857110.0714285714286
8211880.6437.36
83110122.046875-12.046875
84129122.0468756.953125
855174.3636363636364-23.3636363636364
869380.6412.36
877680.64-4.64
884947.09090909090911.90909090909091
89118122.046875-4.046875
903847.0909090909091-9.09090909090909
91141122.04687518.953125
925847.090909090909110.9090909090909
932740.2352941176471-13.2352941176471
9491102.928571428571-11.9285714285714
954847.09090909090910.909090909090907
9663122.046875-59.046875
9756122.046875-66.046875
98144102.92857142857141.0714285714286
997374.3636363636364-1.36363636363636
100168122.04687545.953125
1016447.090909090909116.9090909090909
10297122.046875-25.046875
103117122.046875-5.046875
104100102.928571428571-2.92857142857143
105149122.04687526.953125
106187175.45454545454511.5454545454545
107127122.0468754.953125
1083747.0909090909091-10.0909090909091
109245175.45454545454569.5454545454545
11087122.046875-35.046875
111177122.04687554.953125
1124947.09090909090911.90909090909091
1134947.09090909090911.90909090909091
1147380.64-7.64
115177175.4545454545451.54545454545453
11694122.046875-28.046875
117117122.046875-5.046875
1186074.3636363636364-14.3636363636364
11955102.928571428571-47.9285714285714
1203926.67512.325
1216480.64-16.64
1222626.675-0.675000000000001
1236447.090909090909116.9090909090909
1245840.235294117647117.7647058823529
12595122.046875-27.046875
1262526.675-1.675
1272640.2352941176471-14.2352941176471
1287680.64-4.64
129129122.0468756.953125
130118.592592592592592.40740740740741
13128.59259259259259-6.59259259259259
132101102.928571428571-1.92857142857143
1332826.6751.325
1343647.0909090909091-11.0909090909091
1358947.090909090909141.9090909090909
136193122.04687570.953125
13748.59259259259259-4.59259259259259
1388474.36363636363649.63636363636364
1392325.6923076923077-2.69230769230769
1403925.692307692307713.3076923076923
141148.592592592592595.40740740740741
1427847.090909090909130.9090909090909
1431426.675-12.675
144101122.046875-21.046875
14582122.046875-40.046875
1462426.675-2.675
1473640.2352941176471-4.23529411764706
1487547.090909090909127.9090909090909
149168.592592592592597.40740740740741
1505580.64-25.64
151131122.0468758.953125
152131102.92857142857128.0714285714286
1533940.2352941176471-1.23529411764706
154144122.04687521.953125
155139122.04687516.953125
156211175.45454545454535.5454545454545
1577880.64-2.64
1585040.23529411764719.76470588235294
1593947.0909090909091-8.09090909090909
16090122.046875-32.046875
161166122.04687543.953125
1621226.675-14.675
1635747.09090909090919.90909090909091
164133175.454545454545-42.4545454545455
1656947.090909090909121.9090909090909
166119122.046875-3.046875
16711974.363636363636444.6363636363636
1686547.090909090909117.9090909090909
1696147.090909090909113.9090909090909
1704974.3636363636364-25.3636363636364
171101122.046875-21.046875
172196122.04687573.953125
1731540.2352941176471-25.2352941176471
174136122.04687513.953125
1758980.648.36
1764047.0909090909091-7.09090909090909
177123122.0468750.953125
1782126.675-5.675
179163122.04687540.953125
1802947.0909090909091-18.0909090909091
1813526.6758.325
1821326.675-13.675
18358.59259259259259-3.59259259259259
18496122.046875-26.046875
185151122.04687528.953125
18668.59259259259259-2.59259259259259
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Parameters (Session):
par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 5 ; 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')
}