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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationTue, 20 Dec 2011 04:39:54 -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/2011/Dec/20/t1324375191mds8atyxwrhr69z.htm/, Retrieved Sun, 05 May 2024 21:13:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157863, Retrieved Sun, 05 May 2024 21:13:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [Workshop 10, Recu...] [2010-12-10 13:08:40] [3635fb7041b1998c5a1332cf9de22bce]
- R       [Recursive Partitioning (Regression Trees)] [] [2011-12-10 13:17:20] [c505444e07acba7694d29053ca5d114e]
-    D      [Recursive Partitioning (Regression Trees)] [] [2011-12-18 14:00:14] [c505444e07acba7694d29053ca5d114e]
-             [Recursive Partitioning (Regression Trees)] [] [2011-12-18 14:06:26] [c505444e07acba7694d29053ca5d114e]
-    D            [Recursive Partitioning (Regression Trees)] [] [2011-12-20 09:39:54] [274a40ad31da88f12aea425a159a1f93] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
140824	272545	96	32033
110459	179444	71	20654
105079	222373	70	16346
112098	218443	134	35926
43929	171533	67	10621
76173	70849	8	10024
187326	517243	149	43068
22807	33186	1	1271
144408	217320	83	34416
66485	213274	82	20318
79089	307153	92	24409
81625	239766	117	20648
68788	185384	53	12347
103297	364569	139	21857
69446	251622	80	11034
114948	384524	175	33433
167949	325587	114	35902
125081	343085	100	22355
125818	176082	103	31219
136588	266736	135	21983
112431	278265	123	40085
103037	442703	87	18507
82317	180393	66	16278
118906	189897	103	24662
83515	238434	144	31452
104581	238006	113	32580
103129	267268	99	22883
83243	270787	117	27652
37110	155915	57	9845
113344	359764	138	20190
139165	283441	123	46201
86652	216853	44	10971
112302	336277	140	34811
69652	101014	43	3029
119442	396684	138	38941
69867	273950	83	4958
101629	425253	112	32344
70168	227636	79	19433
31081	115658	33	12558
103925	369702	135	36524
92622	329639	123	26041
79011	186409	76	16637
93487	206196	78	28395
64520	182286	68	16747
93473	153778	50	9105
114360	455401	101	11941
33032	78800	20	7935
96125	208277	101	19499
151911	358127	149	22938
89256	176357	99	25314
95676	224649	95	28527
5950	24188	8	2694
149695	380576	88	20867
32551	65029	21	3597
31701	101097	30	5296
100087	279128	97	32982
169707	328024	130	38975
150491	345663	132	42721
120192	367112	161	41455
95893	261528	89	23923
151715	385286	159	26719
176225	304468	139	53405
59900	272297	102	12526
104767	264889	92	26584
114799	229585	52	37062
72128	225460	107	25696
143592	203663	93	24634
89626	239986	85	27269
131072	238482	137	25270
126817	175816	143	24634
81351	239337	99	17828
22618	73566	22	3007
88977	242622	78	20065
92059	194855	83	24648
81897	209049	131	21588
108146	363250	140	25217
126372	350641	142	30927
249771	217036	80	18487
71154	208804	133	18050
71571	195793	95	17696
55918	153613	62	17326
160141	475859	161	39361
38692	145943	30	9648
102812	287359	118	26759
56622	80953	49	7905
15986	150216	52	4527
123534	179317	76	41517
108535	384720	81	21261
93879	342522	146	36099
144551	179811	165	39039
56750	273486	83	13841
127654	262811	161	23841
65594	190312	48	8589
59938	276134	149	15049
146975	328875	75	39038
161100	189654	83	34974
168553	259191	110	39932
183500	297464	164	43840
165986	393266	152	43146
184923	399801	165	50099
140358	291970	121	40312
149959	296670	150	32616
57224	186856	73	11338
43750	43287	13	7409
48029	185468	89	18213
104978	250254	105	45873
100046	268391	129	39844
101047	314131	169	28317
197426	153059	28	24797
160902	158511	118	7471
147172	316505	76	27259
109432	300526	147	23201
1168	23623	12	238
83248	195817	146	28830
25162	61857	23	3913
45724	163871	83	9935
110529	428191	163	27738
855	21054	4	338
101382	252805	81	13326
14116	31961	18	3988
89506	329583	114	24347
135356	243164	76	27111
116066	175582	55	3938
144244	152043	44	17416
8773	38214	16	1888
102153	224597	81	18700
117440	357602	137	36809
104128	198104	50	24959
134238	417399	142	37343
134047	338606	157	21849
279488	410575	141	49809
79756	187992	71	21654
66089	102424	42	8728
102070	301282	94	20920
146760	425430	115	27195
154771	143860	63	1037
165933	395382	127	42570
64593	162422	55	17672
92280	278077	117	34245
67150	282410	110	16786
128692	217665	38	20954
124089	384177	95	16378
125386	246963	128	31852
37238	173260	41	2805
140015	325961	145	38086
150047	168994	147	21166
154451	253330	119	34672
156349	305217	185	36171
6023	14688	4	2065
84601	251752	72	19354
68946	409163	157	22124
1644	7199	7	556
6179	46660	12	2089
3926	17547	0	2658
52789	116969	37	1813
100350	206501	52	17372

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157863&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157863&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157863&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Goodness of Fit
Correlation0.7827
R-squared0.6126
RMSE30199.1559

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7827 \tabularnewline
R-squared & 0.6126 \tabularnewline
RMSE & 30199.1559 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157863&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7827[/C][/ROW]
[ROW][C]R-squared[/C][C]0.6126[/C][/ROW]
[ROW][C]RMSE[/C][C]30199.1559[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157863&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157863&T=1

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


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

Goodness of Fit
Correlation0.7827
R-squared0.6126
RMSE30199.1559






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1140824111984.628839.4
2110459111984.6-1525.60000000001
310507973685.414634146331393.5853658537
4112098134120.125-22022.125
54392973685.4146341463-29756.4146341463
67617319045.437557127.5625
7187326173801.513524.5
82280719045.43753761.5625
9144408134120.12510287.875
1066485111984.6-45499.6
1179089111984.6-32895.6
1281625111984.6-30359.6
136878873685.4146341463-4897.41463414633
14103297111984.6-8687.60000000001
156944673685.4146341463-4239.41463414633
16114948111984.62963.39999999999
17167949134120.12533828.875
18125081111984.613096.4
19125818111984.613833.4
20136588111984.624603.4
21112431134120.125-21689.125
22103037111984.6-8947.60000000001
238231773685.41463414638631.58536585367
24118906111984.66921.39999999999
2583515111984.6-28469.6
26104581111984.6-7403.60000000001
27103129111984.6-8855.60000000001
2883243111984.6-28741.6
293711073685.4146341463-36575.4146341463
30113344111984.61359.39999999999
31139165173801.5-34636.5
328665273685.414634146312966.5853658537
33112302134120.125-21818.125
346965273685.4146341463-4033.41463414633
35119442134120.125-14678.125
366986773685.4146341463-3818.41463414633
37101629111984.6-10355.6
3870168111984.6-41816.6
393108173685.4146341463-42604.4146341463
40103925134120.125-30195.125
4192622111984.6-19362.6
427901173685.41463414635325.58536585367
4393487111984.6-18497.6
446452073685.4146341463-9165.41463414633
459347373685.414634146319787.5853658537
4611436073685.414634146340674.5853658537
473303219045.437513986.5625
4896125111984.6-15859.6
49151911111984.639926.4
5089256111984.6-22728.6
5195676111984.6-16308.6
52595019045.4375-13095.4375
53149695111984.637710.4
543255119045.437513505.5625
553170173685.4146341463-41984.4146341463
56100087111984.6-11897.6
57169707134120.12535586.875
58150491173801.5-23310.5
59120192134120.125-13928.125
6095893111984.6-16091.6
61151715111984.639730.4
62176225173801.52423.5
635990073685.4146341463-13785.4146341463
64104767111984.6-7217.60000000001
65114799134120.125-19321.125
6672128111984.6-39856.6
67143592111984.631607.4
6889626111984.6-22358.6
69131072111984.619087.4
70126817111984.614832.4
718135173685.41463414637665.58536585367
722261819045.43753572.5625
7388977111984.6-23007.6
7492059111984.6-19925.6
7581897111984.6-30087.6
76108146111984.6-3838.60000000001
77126372111984.614387.4
78249771111984.6137786.4
797115473685.4146341463-2531.41463414633
807157173685.4146341463-2114.41463414633
815591873685.4146341463-17767.4146341463
82160141134120.12526020.875
833869273685.4146341463-34993.4146341463
84102812111984.6-9172.60000000001
855662273685.4146341463-17063.4146341463
861598673685.4146341463-57699.4146341463
87123534134120.125-10586.125
88108535111984.6-3449.60000000001
8993879134120.125-40241.125
90144551134120.12510430.875
915675073685.4146341463-16935.4146341463
92127654111984.615669.4
936559473685.4146341463-8091.41463414633
945993873685.4146341463-13747.4146341463
95146975134120.12512854.875
96161100134120.12526979.875
97168553134120.12534432.875
98183500173801.59698.5
99165986173801.5-7815.5
100184923173801.511121.5
101140358134120.1256237.875
102149959111984.637974.4
1035722473685.4146341463-16461.4146341463
1044375019045.437524704.5625
1054802973685.4146341463-25656.4146341463
106104978173801.5-68823.5
107100046134120.125-34074.125
108101047111984.6-10937.6
109197426111984.685441.4
11016090273685.414634146387216.5853658537
111147172111984.635187.4
112109432111984.6-2552.60000000001
113116819045.4375-17877.4375
11483248111984.6-28736.6
1152516219045.43756116.5625
1164572473685.4146341463-27961.4146341463
117110529111984.6-1455.60000000001
11885519045.4375-18190.4375
11910138273685.414634146327696.5853658537
1201411619045.4375-4929.4375
12189506111984.6-22478.6
122135356111984.623371.4
12311606673685.414634146342380.5853658537
12414424473685.414634146370558.5853658537
125877319045.4375-10272.4375
126102153111984.6-9831.60000000001
127117440134120.125-16680.125
128104128111984.6-7856.60000000001
129134238134120.125117.875
130134047111984.622062.4
131279488173801.5105686.5
13279756111984.6-32228.6
1336608973685.4146341463-7596.41463414633
134102070111984.6-9914.60000000001
135146760111984.634775.4
13615477173685.414634146381085.5853658537
137165933173801.5-7868.5
1386459373685.4146341463-9092.41463414633
13992280111984.6-19704.6
1406715073685.4146341463-6535.41463414633
141128692111984.616707.4
14212408973685.414634146350403.5853658537
143125386111984.613401.4
1443723873685.4146341463-36447.4146341463
145140015134120.1255894.875
146150047111984.638062.4
147154451134120.12520330.875
148156349134120.12522228.875
149602319045.4375-13022.4375
15084601111984.6-27383.6
15168946111984.6-43038.6
152164419045.4375-17401.4375
153617919045.4375-12866.4375
154392619045.4375-15119.4375
1555278973685.4146341463-20896.4146341463
15610035073685.414634146326664.5853658537

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 140824 & 111984.6 & 28839.4 \tabularnewline
2 & 110459 & 111984.6 & -1525.60000000001 \tabularnewline
3 & 105079 & 73685.4146341463 & 31393.5853658537 \tabularnewline
4 & 112098 & 134120.125 & -22022.125 \tabularnewline
5 & 43929 & 73685.4146341463 & -29756.4146341463 \tabularnewline
6 & 76173 & 19045.4375 & 57127.5625 \tabularnewline
7 & 187326 & 173801.5 & 13524.5 \tabularnewline
8 & 22807 & 19045.4375 & 3761.5625 \tabularnewline
9 & 144408 & 134120.125 & 10287.875 \tabularnewline
10 & 66485 & 111984.6 & -45499.6 \tabularnewline
11 & 79089 & 111984.6 & -32895.6 \tabularnewline
12 & 81625 & 111984.6 & -30359.6 \tabularnewline
13 & 68788 & 73685.4146341463 & -4897.41463414633 \tabularnewline
14 & 103297 & 111984.6 & -8687.60000000001 \tabularnewline
15 & 69446 & 73685.4146341463 & -4239.41463414633 \tabularnewline
16 & 114948 & 111984.6 & 2963.39999999999 \tabularnewline
17 & 167949 & 134120.125 & 33828.875 \tabularnewline
18 & 125081 & 111984.6 & 13096.4 \tabularnewline
19 & 125818 & 111984.6 & 13833.4 \tabularnewline
20 & 136588 & 111984.6 & 24603.4 \tabularnewline
21 & 112431 & 134120.125 & -21689.125 \tabularnewline
22 & 103037 & 111984.6 & -8947.60000000001 \tabularnewline
23 & 82317 & 73685.4146341463 & 8631.58536585367 \tabularnewline
24 & 118906 & 111984.6 & 6921.39999999999 \tabularnewline
25 & 83515 & 111984.6 & -28469.6 \tabularnewline
26 & 104581 & 111984.6 & -7403.60000000001 \tabularnewline
27 & 103129 & 111984.6 & -8855.60000000001 \tabularnewline
28 & 83243 & 111984.6 & -28741.6 \tabularnewline
29 & 37110 & 73685.4146341463 & -36575.4146341463 \tabularnewline
30 & 113344 & 111984.6 & 1359.39999999999 \tabularnewline
31 & 139165 & 173801.5 & -34636.5 \tabularnewline
32 & 86652 & 73685.4146341463 & 12966.5853658537 \tabularnewline
33 & 112302 & 134120.125 & -21818.125 \tabularnewline
34 & 69652 & 73685.4146341463 & -4033.41463414633 \tabularnewline
35 & 119442 & 134120.125 & -14678.125 \tabularnewline
36 & 69867 & 73685.4146341463 & -3818.41463414633 \tabularnewline
37 & 101629 & 111984.6 & -10355.6 \tabularnewline
38 & 70168 & 111984.6 & -41816.6 \tabularnewline
39 & 31081 & 73685.4146341463 & -42604.4146341463 \tabularnewline
40 & 103925 & 134120.125 & -30195.125 \tabularnewline
41 & 92622 & 111984.6 & -19362.6 \tabularnewline
42 & 79011 & 73685.4146341463 & 5325.58536585367 \tabularnewline
43 & 93487 & 111984.6 & -18497.6 \tabularnewline
44 & 64520 & 73685.4146341463 & -9165.41463414633 \tabularnewline
45 & 93473 & 73685.4146341463 & 19787.5853658537 \tabularnewline
46 & 114360 & 73685.4146341463 & 40674.5853658537 \tabularnewline
47 & 33032 & 19045.4375 & 13986.5625 \tabularnewline
48 & 96125 & 111984.6 & -15859.6 \tabularnewline
49 & 151911 & 111984.6 & 39926.4 \tabularnewline
50 & 89256 & 111984.6 & -22728.6 \tabularnewline
51 & 95676 & 111984.6 & -16308.6 \tabularnewline
52 & 5950 & 19045.4375 & -13095.4375 \tabularnewline
53 & 149695 & 111984.6 & 37710.4 \tabularnewline
54 & 32551 & 19045.4375 & 13505.5625 \tabularnewline
55 & 31701 & 73685.4146341463 & -41984.4146341463 \tabularnewline
56 & 100087 & 111984.6 & -11897.6 \tabularnewline
57 & 169707 & 134120.125 & 35586.875 \tabularnewline
58 & 150491 & 173801.5 & -23310.5 \tabularnewline
59 & 120192 & 134120.125 & -13928.125 \tabularnewline
60 & 95893 & 111984.6 & -16091.6 \tabularnewline
61 & 151715 & 111984.6 & 39730.4 \tabularnewline
62 & 176225 & 173801.5 & 2423.5 \tabularnewline
63 & 59900 & 73685.4146341463 & -13785.4146341463 \tabularnewline
64 & 104767 & 111984.6 & -7217.60000000001 \tabularnewline
65 & 114799 & 134120.125 & -19321.125 \tabularnewline
66 & 72128 & 111984.6 & -39856.6 \tabularnewline
67 & 143592 & 111984.6 & 31607.4 \tabularnewline
68 & 89626 & 111984.6 & -22358.6 \tabularnewline
69 & 131072 & 111984.6 & 19087.4 \tabularnewline
70 & 126817 & 111984.6 & 14832.4 \tabularnewline
71 & 81351 & 73685.4146341463 & 7665.58536585367 \tabularnewline
72 & 22618 & 19045.4375 & 3572.5625 \tabularnewline
73 & 88977 & 111984.6 & -23007.6 \tabularnewline
74 & 92059 & 111984.6 & -19925.6 \tabularnewline
75 & 81897 & 111984.6 & -30087.6 \tabularnewline
76 & 108146 & 111984.6 & -3838.60000000001 \tabularnewline
77 & 126372 & 111984.6 & 14387.4 \tabularnewline
78 & 249771 & 111984.6 & 137786.4 \tabularnewline
79 & 71154 & 73685.4146341463 & -2531.41463414633 \tabularnewline
80 & 71571 & 73685.4146341463 & -2114.41463414633 \tabularnewline
81 & 55918 & 73685.4146341463 & -17767.4146341463 \tabularnewline
82 & 160141 & 134120.125 & 26020.875 \tabularnewline
83 & 38692 & 73685.4146341463 & -34993.4146341463 \tabularnewline
84 & 102812 & 111984.6 & -9172.60000000001 \tabularnewline
85 & 56622 & 73685.4146341463 & -17063.4146341463 \tabularnewline
86 & 15986 & 73685.4146341463 & -57699.4146341463 \tabularnewline
87 & 123534 & 134120.125 & -10586.125 \tabularnewline
88 & 108535 & 111984.6 & -3449.60000000001 \tabularnewline
89 & 93879 & 134120.125 & -40241.125 \tabularnewline
90 & 144551 & 134120.125 & 10430.875 \tabularnewline
91 & 56750 & 73685.4146341463 & -16935.4146341463 \tabularnewline
92 & 127654 & 111984.6 & 15669.4 \tabularnewline
93 & 65594 & 73685.4146341463 & -8091.41463414633 \tabularnewline
94 & 59938 & 73685.4146341463 & -13747.4146341463 \tabularnewline
95 & 146975 & 134120.125 & 12854.875 \tabularnewline
96 & 161100 & 134120.125 & 26979.875 \tabularnewline
97 & 168553 & 134120.125 & 34432.875 \tabularnewline
98 & 183500 & 173801.5 & 9698.5 \tabularnewline
99 & 165986 & 173801.5 & -7815.5 \tabularnewline
100 & 184923 & 173801.5 & 11121.5 \tabularnewline
101 & 140358 & 134120.125 & 6237.875 \tabularnewline
102 & 149959 & 111984.6 & 37974.4 \tabularnewline
103 & 57224 & 73685.4146341463 & -16461.4146341463 \tabularnewline
104 & 43750 & 19045.4375 & 24704.5625 \tabularnewline
105 & 48029 & 73685.4146341463 & -25656.4146341463 \tabularnewline
106 & 104978 & 173801.5 & -68823.5 \tabularnewline
107 & 100046 & 134120.125 & -34074.125 \tabularnewline
108 & 101047 & 111984.6 & -10937.6 \tabularnewline
109 & 197426 & 111984.6 & 85441.4 \tabularnewline
110 & 160902 & 73685.4146341463 & 87216.5853658537 \tabularnewline
111 & 147172 & 111984.6 & 35187.4 \tabularnewline
112 & 109432 & 111984.6 & -2552.60000000001 \tabularnewline
113 & 1168 & 19045.4375 & -17877.4375 \tabularnewline
114 & 83248 & 111984.6 & -28736.6 \tabularnewline
115 & 25162 & 19045.4375 & 6116.5625 \tabularnewline
116 & 45724 & 73685.4146341463 & -27961.4146341463 \tabularnewline
117 & 110529 & 111984.6 & -1455.60000000001 \tabularnewline
118 & 855 & 19045.4375 & -18190.4375 \tabularnewline
119 & 101382 & 73685.4146341463 & 27696.5853658537 \tabularnewline
120 & 14116 & 19045.4375 & -4929.4375 \tabularnewline
121 & 89506 & 111984.6 & -22478.6 \tabularnewline
122 & 135356 & 111984.6 & 23371.4 \tabularnewline
123 & 116066 & 73685.4146341463 & 42380.5853658537 \tabularnewline
124 & 144244 & 73685.4146341463 & 70558.5853658537 \tabularnewline
125 & 8773 & 19045.4375 & -10272.4375 \tabularnewline
126 & 102153 & 111984.6 & -9831.60000000001 \tabularnewline
127 & 117440 & 134120.125 & -16680.125 \tabularnewline
128 & 104128 & 111984.6 & -7856.60000000001 \tabularnewline
129 & 134238 & 134120.125 & 117.875 \tabularnewline
130 & 134047 & 111984.6 & 22062.4 \tabularnewline
131 & 279488 & 173801.5 & 105686.5 \tabularnewline
132 & 79756 & 111984.6 & -32228.6 \tabularnewline
133 & 66089 & 73685.4146341463 & -7596.41463414633 \tabularnewline
134 & 102070 & 111984.6 & -9914.60000000001 \tabularnewline
135 & 146760 & 111984.6 & 34775.4 \tabularnewline
136 & 154771 & 73685.4146341463 & 81085.5853658537 \tabularnewline
137 & 165933 & 173801.5 & -7868.5 \tabularnewline
138 & 64593 & 73685.4146341463 & -9092.41463414633 \tabularnewline
139 & 92280 & 111984.6 & -19704.6 \tabularnewline
140 & 67150 & 73685.4146341463 & -6535.41463414633 \tabularnewline
141 & 128692 & 111984.6 & 16707.4 \tabularnewline
142 & 124089 & 73685.4146341463 & 50403.5853658537 \tabularnewline
143 & 125386 & 111984.6 & 13401.4 \tabularnewline
144 & 37238 & 73685.4146341463 & -36447.4146341463 \tabularnewline
145 & 140015 & 134120.125 & 5894.875 \tabularnewline
146 & 150047 & 111984.6 & 38062.4 \tabularnewline
147 & 154451 & 134120.125 & 20330.875 \tabularnewline
148 & 156349 & 134120.125 & 22228.875 \tabularnewline
149 & 6023 & 19045.4375 & -13022.4375 \tabularnewline
150 & 84601 & 111984.6 & -27383.6 \tabularnewline
151 & 68946 & 111984.6 & -43038.6 \tabularnewline
152 & 1644 & 19045.4375 & -17401.4375 \tabularnewline
153 & 6179 & 19045.4375 & -12866.4375 \tabularnewline
154 & 3926 & 19045.4375 & -15119.4375 \tabularnewline
155 & 52789 & 73685.4146341463 & -20896.4146341463 \tabularnewline
156 & 100350 & 73685.4146341463 & 26664.5853658537 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157863&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]140824[/C][C]111984.6[/C][C]28839.4[/C][/ROW]
[ROW][C]2[/C][C]110459[/C][C]111984.6[/C][C]-1525.60000000001[/C][/ROW]
[ROW][C]3[/C][C]105079[/C][C]73685.4146341463[/C][C]31393.5853658537[/C][/ROW]
[ROW][C]4[/C][C]112098[/C][C]134120.125[/C][C]-22022.125[/C][/ROW]
[ROW][C]5[/C][C]43929[/C][C]73685.4146341463[/C][C]-29756.4146341463[/C][/ROW]
[ROW][C]6[/C][C]76173[/C][C]19045.4375[/C][C]57127.5625[/C][/ROW]
[ROW][C]7[/C][C]187326[/C][C]173801.5[/C][C]13524.5[/C][/ROW]
[ROW][C]8[/C][C]22807[/C][C]19045.4375[/C][C]3761.5625[/C][/ROW]
[ROW][C]9[/C][C]144408[/C][C]134120.125[/C][C]10287.875[/C][/ROW]
[ROW][C]10[/C][C]66485[/C][C]111984.6[/C][C]-45499.6[/C][/ROW]
[ROW][C]11[/C][C]79089[/C][C]111984.6[/C][C]-32895.6[/C][/ROW]
[ROW][C]12[/C][C]81625[/C][C]111984.6[/C][C]-30359.6[/C][/ROW]
[ROW][C]13[/C][C]68788[/C][C]73685.4146341463[/C][C]-4897.41463414633[/C][/ROW]
[ROW][C]14[/C][C]103297[/C][C]111984.6[/C][C]-8687.60000000001[/C][/ROW]
[ROW][C]15[/C][C]69446[/C][C]73685.4146341463[/C][C]-4239.41463414633[/C][/ROW]
[ROW][C]16[/C][C]114948[/C][C]111984.6[/C][C]2963.39999999999[/C][/ROW]
[ROW][C]17[/C][C]167949[/C][C]134120.125[/C][C]33828.875[/C][/ROW]
[ROW][C]18[/C][C]125081[/C][C]111984.6[/C][C]13096.4[/C][/ROW]
[ROW][C]19[/C][C]125818[/C][C]111984.6[/C][C]13833.4[/C][/ROW]
[ROW][C]20[/C][C]136588[/C][C]111984.6[/C][C]24603.4[/C][/ROW]
[ROW][C]21[/C][C]112431[/C][C]134120.125[/C][C]-21689.125[/C][/ROW]
[ROW][C]22[/C][C]103037[/C][C]111984.6[/C][C]-8947.60000000001[/C][/ROW]
[ROW][C]23[/C][C]82317[/C][C]73685.4146341463[/C][C]8631.58536585367[/C][/ROW]
[ROW][C]24[/C][C]118906[/C][C]111984.6[/C][C]6921.39999999999[/C][/ROW]
[ROW][C]25[/C][C]83515[/C][C]111984.6[/C][C]-28469.6[/C][/ROW]
[ROW][C]26[/C][C]104581[/C][C]111984.6[/C][C]-7403.60000000001[/C][/ROW]
[ROW][C]27[/C][C]103129[/C][C]111984.6[/C][C]-8855.60000000001[/C][/ROW]
[ROW][C]28[/C][C]83243[/C][C]111984.6[/C][C]-28741.6[/C][/ROW]
[ROW][C]29[/C][C]37110[/C][C]73685.4146341463[/C][C]-36575.4146341463[/C][/ROW]
[ROW][C]30[/C][C]113344[/C][C]111984.6[/C][C]1359.39999999999[/C][/ROW]
[ROW][C]31[/C][C]139165[/C][C]173801.5[/C][C]-34636.5[/C][/ROW]
[ROW][C]32[/C][C]86652[/C][C]73685.4146341463[/C][C]12966.5853658537[/C][/ROW]
[ROW][C]33[/C][C]112302[/C][C]134120.125[/C][C]-21818.125[/C][/ROW]
[ROW][C]34[/C][C]69652[/C][C]73685.4146341463[/C][C]-4033.41463414633[/C][/ROW]
[ROW][C]35[/C][C]119442[/C][C]134120.125[/C][C]-14678.125[/C][/ROW]
[ROW][C]36[/C][C]69867[/C][C]73685.4146341463[/C][C]-3818.41463414633[/C][/ROW]
[ROW][C]37[/C][C]101629[/C][C]111984.6[/C][C]-10355.6[/C][/ROW]
[ROW][C]38[/C][C]70168[/C][C]111984.6[/C][C]-41816.6[/C][/ROW]
[ROW][C]39[/C][C]31081[/C][C]73685.4146341463[/C][C]-42604.4146341463[/C][/ROW]
[ROW][C]40[/C][C]103925[/C][C]134120.125[/C][C]-30195.125[/C][/ROW]
[ROW][C]41[/C][C]92622[/C][C]111984.6[/C][C]-19362.6[/C][/ROW]
[ROW][C]42[/C][C]79011[/C][C]73685.4146341463[/C][C]5325.58536585367[/C][/ROW]
[ROW][C]43[/C][C]93487[/C][C]111984.6[/C][C]-18497.6[/C][/ROW]
[ROW][C]44[/C][C]64520[/C][C]73685.4146341463[/C][C]-9165.41463414633[/C][/ROW]
[ROW][C]45[/C][C]93473[/C][C]73685.4146341463[/C][C]19787.5853658537[/C][/ROW]
[ROW][C]46[/C][C]114360[/C][C]73685.4146341463[/C][C]40674.5853658537[/C][/ROW]
[ROW][C]47[/C][C]33032[/C][C]19045.4375[/C][C]13986.5625[/C][/ROW]
[ROW][C]48[/C][C]96125[/C][C]111984.6[/C][C]-15859.6[/C][/ROW]
[ROW][C]49[/C][C]151911[/C][C]111984.6[/C][C]39926.4[/C][/ROW]
[ROW][C]50[/C][C]89256[/C][C]111984.6[/C][C]-22728.6[/C][/ROW]
[ROW][C]51[/C][C]95676[/C][C]111984.6[/C][C]-16308.6[/C][/ROW]
[ROW][C]52[/C][C]5950[/C][C]19045.4375[/C][C]-13095.4375[/C][/ROW]
[ROW][C]53[/C][C]149695[/C][C]111984.6[/C][C]37710.4[/C][/ROW]
[ROW][C]54[/C][C]32551[/C][C]19045.4375[/C][C]13505.5625[/C][/ROW]
[ROW][C]55[/C][C]31701[/C][C]73685.4146341463[/C][C]-41984.4146341463[/C][/ROW]
[ROW][C]56[/C][C]100087[/C][C]111984.6[/C][C]-11897.6[/C][/ROW]
[ROW][C]57[/C][C]169707[/C][C]134120.125[/C][C]35586.875[/C][/ROW]
[ROW][C]58[/C][C]150491[/C][C]173801.5[/C][C]-23310.5[/C][/ROW]
[ROW][C]59[/C][C]120192[/C][C]134120.125[/C][C]-13928.125[/C][/ROW]
[ROW][C]60[/C][C]95893[/C][C]111984.6[/C][C]-16091.6[/C][/ROW]
[ROW][C]61[/C][C]151715[/C][C]111984.6[/C][C]39730.4[/C][/ROW]
[ROW][C]62[/C][C]176225[/C][C]173801.5[/C][C]2423.5[/C][/ROW]
[ROW][C]63[/C][C]59900[/C][C]73685.4146341463[/C][C]-13785.4146341463[/C][/ROW]
[ROW][C]64[/C][C]104767[/C][C]111984.6[/C][C]-7217.60000000001[/C][/ROW]
[ROW][C]65[/C][C]114799[/C][C]134120.125[/C][C]-19321.125[/C][/ROW]
[ROW][C]66[/C][C]72128[/C][C]111984.6[/C][C]-39856.6[/C][/ROW]
[ROW][C]67[/C][C]143592[/C][C]111984.6[/C][C]31607.4[/C][/ROW]
[ROW][C]68[/C][C]89626[/C][C]111984.6[/C][C]-22358.6[/C][/ROW]
[ROW][C]69[/C][C]131072[/C][C]111984.6[/C][C]19087.4[/C][/ROW]
[ROW][C]70[/C][C]126817[/C][C]111984.6[/C][C]14832.4[/C][/ROW]
[ROW][C]71[/C][C]81351[/C][C]73685.4146341463[/C][C]7665.58536585367[/C][/ROW]
[ROW][C]72[/C][C]22618[/C][C]19045.4375[/C][C]3572.5625[/C][/ROW]
[ROW][C]73[/C][C]88977[/C][C]111984.6[/C][C]-23007.6[/C][/ROW]
[ROW][C]74[/C][C]92059[/C][C]111984.6[/C][C]-19925.6[/C][/ROW]
[ROW][C]75[/C][C]81897[/C][C]111984.6[/C][C]-30087.6[/C][/ROW]
[ROW][C]76[/C][C]108146[/C][C]111984.6[/C][C]-3838.60000000001[/C][/ROW]
[ROW][C]77[/C][C]126372[/C][C]111984.6[/C][C]14387.4[/C][/ROW]
[ROW][C]78[/C][C]249771[/C][C]111984.6[/C][C]137786.4[/C][/ROW]
[ROW][C]79[/C][C]71154[/C][C]73685.4146341463[/C][C]-2531.41463414633[/C][/ROW]
[ROW][C]80[/C][C]71571[/C][C]73685.4146341463[/C][C]-2114.41463414633[/C][/ROW]
[ROW][C]81[/C][C]55918[/C][C]73685.4146341463[/C][C]-17767.4146341463[/C][/ROW]
[ROW][C]82[/C][C]160141[/C][C]134120.125[/C][C]26020.875[/C][/ROW]
[ROW][C]83[/C][C]38692[/C][C]73685.4146341463[/C][C]-34993.4146341463[/C][/ROW]
[ROW][C]84[/C][C]102812[/C][C]111984.6[/C][C]-9172.60000000001[/C][/ROW]
[ROW][C]85[/C][C]56622[/C][C]73685.4146341463[/C][C]-17063.4146341463[/C][/ROW]
[ROW][C]86[/C][C]15986[/C][C]73685.4146341463[/C][C]-57699.4146341463[/C][/ROW]
[ROW][C]87[/C][C]123534[/C][C]134120.125[/C][C]-10586.125[/C][/ROW]
[ROW][C]88[/C][C]108535[/C][C]111984.6[/C][C]-3449.60000000001[/C][/ROW]
[ROW][C]89[/C][C]93879[/C][C]134120.125[/C][C]-40241.125[/C][/ROW]
[ROW][C]90[/C][C]144551[/C][C]134120.125[/C][C]10430.875[/C][/ROW]
[ROW][C]91[/C][C]56750[/C][C]73685.4146341463[/C][C]-16935.4146341463[/C][/ROW]
[ROW][C]92[/C][C]127654[/C][C]111984.6[/C][C]15669.4[/C][/ROW]
[ROW][C]93[/C][C]65594[/C][C]73685.4146341463[/C][C]-8091.41463414633[/C][/ROW]
[ROW][C]94[/C][C]59938[/C][C]73685.4146341463[/C][C]-13747.4146341463[/C][/ROW]
[ROW][C]95[/C][C]146975[/C][C]134120.125[/C][C]12854.875[/C][/ROW]
[ROW][C]96[/C][C]161100[/C][C]134120.125[/C][C]26979.875[/C][/ROW]
[ROW][C]97[/C][C]168553[/C][C]134120.125[/C][C]34432.875[/C][/ROW]
[ROW][C]98[/C][C]183500[/C][C]173801.5[/C][C]9698.5[/C][/ROW]
[ROW][C]99[/C][C]165986[/C][C]173801.5[/C][C]-7815.5[/C][/ROW]
[ROW][C]100[/C][C]184923[/C][C]173801.5[/C][C]11121.5[/C][/ROW]
[ROW][C]101[/C][C]140358[/C][C]134120.125[/C][C]6237.875[/C][/ROW]
[ROW][C]102[/C][C]149959[/C][C]111984.6[/C][C]37974.4[/C][/ROW]
[ROW][C]103[/C][C]57224[/C][C]73685.4146341463[/C][C]-16461.4146341463[/C][/ROW]
[ROW][C]104[/C][C]43750[/C][C]19045.4375[/C][C]24704.5625[/C][/ROW]
[ROW][C]105[/C][C]48029[/C][C]73685.4146341463[/C][C]-25656.4146341463[/C][/ROW]
[ROW][C]106[/C][C]104978[/C][C]173801.5[/C][C]-68823.5[/C][/ROW]
[ROW][C]107[/C][C]100046[/C][C]134120.125[/C][C]-34074.125[/C][/ROW]
[ROW][C]108[/C][C]101047[/C][C]111984.6[/C][C]-10937.6[/C][/ROW]
[ROW][C]109[/C][C]197426[/C][C]111984.6[/C][C]85441.4[/C][/ROW]
[ROW][C]110[/C][C]160902[/C][C]73685.4146341463[/C][C]87216.5853658537[/C][/ROW]
[ROW][C]111[/C][C]147172[/C][C]111984.6[/C][C]35187.4[/C][/ROW]
[ROW][C]112[/C][C]109432[/C][C]111984.6[/C][C]-2552.60000000001[/C][/ROW]
[ROW][C]113[/C][C]1168[/C][C]19045.4375[/C][C]-17877.4375[/C][/ROW]
[ROW][C]114[/C][C]83248[/C][C]111984.6[/C][C]-28736.6[/C][/ROW]
[ROW][C]115[/C][C]25162[/C][C]19045.4375[/C][C]6116.5625[/C][/ROW]
[ROW][C]116[/C][C]45724[/C][C]73685.4146341463[/C][C]-27961.4146341463[/C][/ROW]
[ROW][C]117[/C][C]110529[/C][C]111984.6[/C][C]-1455.60000000001[/C][/ROW]
[ROW][C]118[/C][C]855[/C][C]19045.4375[/C][C]-18190.4375[/C][/ROW]
[ROW][C]119[/C][C]101382[/C][C]73685.4146341463[/C][C]27696.5853658537[/C][/ROW]
[ROW][C]120[/C][C]14116[/C][C]19045.4375[/C][C]-4929.4375[/C][/ROW]
[ROW][C]121[/C][C]89506[/C][C]111984.6[/C][C]-22478.6[/C][/ROW]
[ROW][C]122[/C][C]135356[/C][C]111984.6[/C][C]23371.4[/C][/ROW]
[ROW][C]123[/C][C]116066[/C][C]73685.4146341463[/C][C]42380.5853658537[/C][/ROW]
[ROW][C]124[/C][C]144244[/C][C]73685.4146341463[/C][C]70558.5853658537[/C][/ROW]
[ROW][C]125[/C][C]8773[/C][C]19045.4375[/C][C]-10272.4375[/C][/ROW]
[ROW][C]126[/C][C]102153[/C][C]111984.6[/C][C]-9831.60000000001[/C][/ROW]
[ROW][C]127[/C][C]117440[/C][C]134120.125[/C][C]-16680.125[/C][/ROW]
[ROW][C]128[/C][C]104128[/C][C]111984.6[/C][C]-7856.60000000001[/C][/ROW]
[ROW][C]129[/C][C]134238[/C][C]134120.125[/C][C]117.875[/C][/ROW]
[ROW][C]130[/C][C]134047[/C][C]111984.6[/C][C]22062.4[/C][/ROW]
[ROW][C]131[/C][C]279488[/C][C]173801.5[/C][C]105686.5[/C][/ROW]
[ROW][C]132[/C][C]79756[/C][C]111984.6[/C][C]-32228.6[/C][/ROW]
[ROW][C]133[/C][C]66089[/C][C]73685.4146341463[/C][C]-7596.41463414633[/C][/ROW]
[ROW][C]134[/C][C]102070[/C][C]111984.6[/C][C]-9914.60000000001[/C][/ROW]
[ROW][C]135[/C][C]146760[/C][C]111984.6[/C][C]34775.4[/C][/ROW]
[ROW][C]136[/C][C]154771[/C][C]73685.4146341463[/C][C]81085.5853658537[/C][/ROW]
[ROW][C]137[/C][C]165933[/C][C]173801.5[/C][C]-7868.5[/C][/ROW]
[ROW][C]138[/C][C]64593[/C][C]73685.4146341463[/C][C]-9092.41463414633[/C][/ROW]
[ROW][C]139[/C][C]92280[/C][C]111984.6[/C][C]-19704.6[/C][/ROW]
[ROW][C]140[/C][C]67150[/C][C]73685.4146341463[/C][C]-6535.41463414633[/C][/ROW]
[ROW][C]141[/C][C]128692[/C][C]111984.6[/C][C]16707.4[/C][/ROW]
[ROW][C]142[/C][C]124089[/C][C]73685.4146341463[/C][C]50403.5853658537[/C][/ROW]
[ROW][C]143[/C][C]125386[/C][C]111984.6[/C][C]13401.4[/C][/ROW]
[ROW][C]144[/C][C]37238[/C][C]73685.4146341463[/C][C]-36447.4146341463[/C][/ROW]
[ROW][C]145[/C][C]140015[/C][C]134120.125[/C][C]5894.875[/C][/ROW]
[ROW][C]146[/C][C]150047[/C][C]111984.6[/C][C]38062.4[/C][/ROW]
[ROW][C]147[/C][C]154451[/C][C]134120.125[/C][C]20330.875[/C][/ROW]
[ROW][C]148[/C][C]156349[/C][C]134120.125[/C][C]22228.875[/C][/ROW]
[ROW][C]149[/C][C]6023[/C][C]19045.4375[/C][C]-13022.4375[/C][/ROW]
[ROW][C]150[/C][C]84601[/C][C]111984.6[/C][C]-27383.6[/C][/ROW]
[ROW][C]151[/C][C]68946[/C][C]111984.6[/C][C]-43038.6[/C][/ROW]
[ROW][C]152[/C][C]1644[/C][C]19045.4375[/C][C]-17401.4375[/C][/ROW]
[ROW][C]153[/C][C]6179[/C][C]19045.4375[/C][C]-12866.4375[/C][/ROW]
[ROW][C]154[/C][C]3926[/C][C]19045.4375[/C][C]-15119.4375[/C][/ROW]
[ROW][C]155[/C][C]52789[/C][C]73685.4146341463[/C][C]-20896.4146341463[/C][/ROW]
[ROW][C]156[/C][C]100350[/C][C]73685.4146341463[/C][C]26664.5853658537[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157863&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157863&T=2

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


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

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1140824111984.628839.4
2110459111984.6-1525.60000000001
310507973685.414634146331393.5853658537
4112098134120.125-22022.125
54392973685.4146341463-29756.4146341463
67617319045.437557127.5625
7187326173801.513524.5
82280719045.43753761.5625
9144408134120.12510287.875
1066485111984.6-45499.6
1179089111984.6-32895.6
1281625111984.6-30359.6
136878873685.4146341463-4897.41463414633
14103297111984.6-8687.60000000001
156944673685.4146341463-4239.41463414633
16114948111984.62963.39999999999
17167949134120.12533828.875
18125081111984.613096.4
19125818111984.613833.4
20136588111984.624603.4
21112431134120.125-21689.125
22103037111984.6-8947.60000000001
238231773685.41463414638631.58536585367
24118906111984.66921.39999999999
2583515111984.6-28469.6
26104581111984.6-7403.60000000001
27103129111984.6-8855.60000000001
2883243111984.6-28741.6
293711073685.4146341463-36575.4146341463
30113344111984.61359.39999999999
31139165173801.5-34636.5
328665273685.414634146312966.5853658537
33112302134120.125-21818.125
346965273685.4146341463-4033.41463414633
35119442134120.125-14678.125
366986773685.4146341463-3818.41463414633
37101629111984.6-10355.6
3870168111984.6-41816.6
393108173685.4146341463-42604.4146341463
40103925134120.125-30195.125
4192622111984.6-19362.6
427901173685.41463414635325.58536585367
4393487111984.6-18497.6
446452073685.4146341463-9165.41463414633
459347373685.414634146319787.5853658537
4611436073685.414634146340674.5853658537
473303219045.437513986.5625
4896125111984.6-15859.6
49151911111984.639926.4
5089256111984.6-22728.6
5195676111984.6-16308.6
52595019045.4375-13095.4375
53149695111984.637710.4
543255119045.437513505.5625
553170173685.4146341463-41984.4146341463
56100087111984.6-11897.6
57169707134120.12535586.875
58150491173801.5-23310.5
59120192134120.125-13928.125
6095893111984.6-16091.6
61151715111984.639730.4
62176225173801.52423.5
635990073685.4146341463-13785.4146341463
64104767111984.6-7217.60000000001
65114799134120.125-19321.125
6672128111984.6-39856.6
67143592111984.631607.4
6889626111984.6-22358.6
69131072111984.619087.4
70126817111984.614832.4
718135173685.41463414637665.58536585367
722261819045.43753572.5625
7388977111984.6-23007.6
7492059111984.6-19925.6
7581897111984.6-30087.6
76108146111984.6-3838.60000000001
77126372111984.614387.4
78249771111984.6137786.4
797115473685.4146341463-2531.41463414633
807157173685.4146341463-2114.41463414633
815591873685.4146341463-17767.4146341463
82160141134120.12526020.875
833869273685.4146341463-34993.4146341463
84102812111984.6-9172.60000000001
855662273685.4146341463-17063.4146341463
861598673685.4146341463-57699.4146341463
87123534134120.125-10586.125
88108535111984.6-3449.60000000001
8993879134120.125-40241.125
90144551134120.12510430.875
915675073685.4146341463-16935.4146341463
92127654111984.615669.4
936559473685.4146341463-8091.41463414633
945993873685.4146341463-13747.4146341463
95146975134120.12512854.875
96161100134120.12526979.875
97168553134120.12534432.875
98183500173801.59698.5
99165986173801.5-7815.5
100184923173801.511121.5
101140358134120.1256237.875
102149959111984.637974.4
1035722473685.4146341463-16461.4146341463
1044375019045.437524704.5625
1054802973685.4146341463-25656.4146341463
106104978173801.5-68823.5
107100046134120.125-34074.125
108101047111984.6-10937.6
109197426111984.685441.4
11016090273685.414634146387216.5853658537
111147172111984.635187.4
112109432111984.6-2552.60000000001
113116819045.4375-17877.4375
11483248111984.6-28736.6
1152516219045.43756116.5625
1164572473685.4146341463-27961.4146341463
117110529111984.6-1455.60000000001
11885519045.4375-18190.4375
11910138273685.414634146327696.5853658537
1201411619045.4375-4929.4375
12189506111984.6-22478.6
122135356111984.623371.4
12311606673685.414634146342380.5853658537
12414424473685.414634146370558.5853658537
125877319045.4375-10272.4375
126102153111984.6-9831.60000000001
127117440134120.125-16680.125
128104128111984.6-7856.60000000001
129134238134120.125117.875
130134047111984.622062.4
131279488173801.5105686.5
13279756111984.6-32228.6
1336608973685.4146341463-7596.41463414633
134102070111984.6-9914.60000000001
135146760111984.634775.4
13615477173685.414634146381085.5853658537
137165933173801.5-7868.5
1386459373685.4146341463-9092.41463414633
13992280111984.6-19704.6
1406715073685.4146341463-6535.41463414633
141128692111984.616707.4
14212408973685.414634146350403.5853658537
143125386111984.613401.4
1443723873685.4146341463-36447.4146341463
145140015134120.1255894.875
146150047111984.638062.4
147154451134120.12520330.875
148156349134120.12522228.875
149602319045.4375-13022.4375
15084601111984.6-27383.6
15168946111984.6-43038.6
152164419045.4375-17401.4375
153617919045.4375-12866.4375
154392619045.4375-15119.4375
1555278973685.4146341463-20896.4146341463
15610035073685.414634146326664.5853658537


Figures (Output of Computation)

Input Parameters & R Code

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