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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 computationSun, 18 Dec 2011 09:06:26 -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/18/t132421729865n4u1gton4qaxz.htm/, Retrieved Sun, 05 May 2024 17:08:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156884, Retrieved Sun, 05 May 2024 17:08:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123

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] [274a40ad31da88f12aea425a159a1f93] [Current]
-    D            [Recursive Partitioning (Regression Trees)] [] [2011-12-20 09:39:54] [c505444e07acba7694d29053ca5d114e]
- RMPD            [Multiple Regression] [] [2011-12-20 10:14:41] [c505444e07acba7694d29053ca5d114e]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
272545	140824
179444	110459
222373	105079
218443	112098
167843	43929
70849	76173
33186	22807
216660	144408
213274	66485
307153	79089
237633	81625
166215	68788
364402	103297
244103	69446
384448	114948
325587	167949
323652	125081
176082	125818
266736	136588
278265	112431
180393	82317
189897	118906
234247	83515
238002	104581
267268	103129
270787	83243
155915	37110
342564	113344
282172	139165
216584	86652
318563	112302
98672	69652
391593	119442
273950	69867
227636	70168
115658	31081
349863	103925
324178	92622
178083	79011
195153	93487
177694	64520
153778	93473
455168	114360
78800	33032
208051	96125
348077	151911
175523	89256
224591	95671
24188	5950
372238	149695
65029	32551
101097	31701
279012	100087
317644	169707
340471	150491
358958	120192
252529	95893
377482	151715
304468	176225
270190	59900
264889	104767
228595	114799
216027	72128
198798	143592
238146	89626
234891	131072
175816	126817
239314	81351
73566	22618
242622	88977
187167	92059
209049	81897
360592	108146
342846	126372
207650	249771
206500	71154
182357	71571
153613	55918
456979	160141
145943	38692
280366	102812
80953	56622
150216	15986
167878	123534
369718	108535
322454	93879
179797	144551
264350	56750
262793	127654
189142	65594
275997	59938
328875	146975
189252	143372
222504	168553
287386	183500
389104	165986
397681	184923
287748	140358
294320	149959
186856	57224
43287	43750
185468	48029
235352	104978
268077	100046
305195	101047
143356	197426
154287	160902
307000	147172
298039	109432
23623	1168
195817	83248
61857	25162
163766	45724
21054	855
252805	101382
31961	14116
317367	89506
240153	135356
175083	116066
152043	144244
38214	8773
216299	102153
357602	117440
198104	104128
410803	134238
316105	134047
397297	279488
187992	79756
102424	66089
286327	102070
409878	146760
143860	154771
391854	165933
157429	64593
258751	92280
282399	67150
217665	128692
367246	124089
239072	125386
173260	37238
323545	140015
168994	150047
253330	154451
301703	156349
246435	84601
384136	68946
46660	6179
116678	52789
206501	100350

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156884&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156884&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156884&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Goodness of Fit
Correlation0.6539
R-squared0.4276
RMSE35594.12

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.6539 \tabularnewline
R-squared & 0.4276 \tabularnewline
RMSE & 35594.12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156884&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.6539[/C][/ROW]
[ROW][C]R-squared[/C][C]0.4276[/C][/ROW]
[ROW][C]RMSE[/C][C]35594.12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156884&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156884&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
Correlation0.6539
R-squared0.4276
RMSE35594.12






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
114082499187.126436781641636.8735632184
211045999187.126436781611271.8735632184
310507999187.12643678165891.8735632184
411209899187.126436781612910.8735632184
54392999187.1264367816-55258.1264367816
67617331635.157894736844537.8421052632
72280731635.1578947368-8828.15789473684
814440899187.126436781645220.8735632184
96648599187.1264367816-32702.1264367816
1079089134872.813953488-55783.8139534884
118162599187.1264367816-17562.1264367816
126878899187.1264367816-30399.1264367816
13103297134872.813953488-31575.8139534884
146944699187.1264367816-29741.1264367816
15114948134872.813953488-19924.8139534884
16167949134872.81395348833076.1860465116
17125081134872.813953488-9791.81395348837
1812581899187.126436781626630.8735632184
1913658899187.126436781637400.8735632184
2011243199187.126436781613243.8735632184
218231799187.1264367816-16870.1264367816
2211890699187.126436781619718.8735632184
238351599187.1264367816-15672.1264367816
2410458199187.12643678165393.8735632184
2510312999187.12643678163941.8735632184
268324399187.1264367816-15944.1264367816
273711099187.1264367816-62077.1264367816
28113344134872.813953488-21528.8139534884
2913916599187.126436781639977.8735632184
308665299187.1264367816-12535.1264367816
31112302134872.813953488-22570.8139534884
326965231635.157894736838016.8421052632
33119442134872.813953488-15430.8139534884
346986799187.1264367816-29320.1264367816
357016899187.1264367816-29019.1264367816
363108131635.1578947368-554.157894736843
37103925134872.813953488-30947.8139534884
3892622134872.813953488-42250.8139534884
397901199187.1264367816-20176.1264367816
409348799187.1264367816-5700.1264367816
416452099187.1264367816-34667.1264367816
429347399187.1264367816-5714.1264367816
43114360134872.813953488-20512.8139534884
443303231635.15789473681396.84210526316
459612599187.1264367816-3062.1264367816
46151911134872.81395348817038.1860465116
478925699187.1264367816-9931.1264367816
489567199187.1264367816-3516.1264367816
49595031635.1578947368-25685.1578947368
50149695134872.81395348814822.1860465116
513255131635.1578947368915.842105263157
523170131635.157894736865.8421052631566
5310008799187.1264367816899.873563218396
54169707134872.81395348834834.1860465116
55150491134872.81395348815618.1860465116
56120192134872.813953488-14680.8139534884
579589399187.1264367816-3294.1264367816
58151715134872.81395348816842.1860465116
59176225134872.81395348841352.1860465116
605990099187.1264367816-39287.1264367816
6110476799187.12643678165579.8735632184
6211479999187.126436781615611.8735632184
637212899187.1264367816-27059.1264367816
6414359299187.126436781644404.8735632184
658962699187.1264367816-9561.1264367816
6613107299187.126436781631884.8735632184
6712681799187.126436781627629.8735632184
688135199187.1264367816-17836.1264367816
692261831635.1578947368-9017.15789473684
708897799187.1264367816-10210.1264367816
719205999187.1264367816-7128.1264367816
728189799187.1264367816-17290.1264367816
73108146134872.813953488-26726.8139534884
74126372134872.813953488-8500.81395348837
7524977199187.1264367816150583.873563218
767115499187.1264367816-28033.1264367816
777157199187.1264367816-27616.1264367816
785591899187.1264367816-43269.1264367816
79160141134872.81395348825268.1860465116
803869299187.1264367816-60495.1264367816
8110281299187.12643678163624.8735632184
825662231635.157894736824986.8421052632
831598699187.1264367816-83201.1264367816
8412353499187.126436781624346.8735632184
85108535134872.813953488-26337.8139534884
8693879134872.813953488-40993.8139534884
8714455199187.126436781645363.8735632184
885675099187.1264367816-42437.1264367816
8912765499187.126436781628466.8735632184
906559499187.1264367816-33593.1264367816
915993899187.1264367816-39249.1264367816
92146975134872.81395348812102.1860465116
9314337299187.126436781644184.8735632184
9416855399187.126436781669365.8735632184
95183500134872.81395348848627.1860465116
96165986134872.81395348831113.1860465116
97184923134872.81395348850050.1860465116
98140358134872.8139534885485.18604651163
99149959134872.81395348815086.1860465116
1005722499187.1264367816-41963.1264367816
1014375031635.157894736812114.8421052632
1024802999187.1264367816-51158.1264367816
10310497899187.12643678165790.8735632184
10410004699187.1264367816858.873563218396
105101047134872.813953488-33825.8139534884
10619742699187.126436781698238.8735632184
10716090299187.126436781661714.8735632184
108147172134872.81395348812299.1860465116
109109432134872.813953488-25440.8139534884
110116831635.1578947368-30467.1578947368
1118324899187.1264367816-15939.1264367816
1122516231635.1578947368-6473.15789473684
1134572499187.1264367816-53463.1264367816
11485531635.1578947368-30780.1578947368
11510138299187.12643678162194.8735632184
1161411631635.1578947368-17519.1578947368
11789506134872.813953488-45366.8139534884
11813535699187.126436781636168.8735632184
11911606699187.126436781616878.8735632184
12014424499187.126436781645056.8735632184
121877331635.1578947368-22862.1578947368
12210215399187.12643678162965.8735632184
123117440134872.813953488-17432.8139534884
12410412899187.12643678164940.8735632184
125134238134872.813953488-634.813953488367
126134047134872.813953488-825.813953488367
127279488134872.813953488144615.186046512
1287975699187.1264367816-19431.1264367816
1296608931635.157894736834453.8421052632
13010207099187.12643678162882.8735632184
131146760134872.81395348811887.1860465116
13215477199187.126436781655583.8735632184
133165933134872.81395348831060.1860465116
1346459399187.1264367816-34594.1264367816
1359228099187.1264367816-6907.1264367816
1366715099187.1264367816-32037.1264367816
13712869299187.126436781629504.8735632184
138124089134872.813953488-10783.8139534884
13912538699187.126436781626198.8735632184
1403723899187.1264367816-61949.1264367816
141140015134872.8139534885142.18604651163
14215004799187.126436781650859.8735632184
14315445199187.126436781655263.8735632184
144156349134872.81395348821476.1860465116
1458460199187.1264367816-14586.1264367816
14668946134872.813953488-65926.8139534884
147617931635.1578947368-25456.1578947368
1485278931635.157894736821153.8421052632
14910035099187.12643678161162.8735632184

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 140824 & 99187.1264367816 & 41636.8735632184 \tabularnewline
2 & 110459 & 99187.1264367816 & 11271.8735632184 \tabularnewline
3 & 105079 & 99187.1264367816 & 5891.8735632184 \tabularnewline
4 & 112098 & 99187.1264367816 & 12910.8735632184 \tabularnewline
5 & 43929 & 99187.1264367816 & -55258.1264367816 \tabularnewline
6 & 76173 & 31635.1578947368 & 44537.8421052632 \tabularnewline
7 & 22807 & 31635.1578947368 & -8828.15789473684 \tabularnewline
8 & 144408 & 99187.1264367816 & 45220.8735632184 \tabularnewline
9 & 66485 & 99187.1264367816 & -32702.1264367816 \tabularnewline
10 & 79089 & 134872.813953488 & -55783.8139534884 \tabularnewline
11 & 81625 & 99187.1264367816 & -17562.1264367816 \tabularnewline
12 & 68788 & 99187.1264367816 & -30399.1264367816 \tabularnewline
13 & 103297 & 134872.813953488 & -31575.8139534884 \tabularnewline
14 & 69446 & 99187.1264367816 & -29741.1264367816 \tabularnewline
15 & 114948 & 134872.813953488 & -19924.8139534884 \tabularnewline
16 & 167949 & 134872.813953488 & 33076.1860465116 \tabularnewline
17 & 125081 & 134872.813953488 & -9791.81395348837 \tabularnewline
18 & 125818 & 99187.1264367816 & 26630.8735632184 \tabularnewline
19 & 136588 & 99187.1264367816 & 37400.8735632184 \tabularnewline
20 & 112431 & 99187.1264367816 & 13243.8735632184 \tabularnewline
21 & 82317 & 99187.1264367816 & -16870.1264367816 \tabularnewline
22 & 118906 & 99187.1264367816 & 19718.8735632184 \tabularnewline
23 & 83515 & 99187.1264367816 & -15672.1264367816 \tabularnewline
24 & 104581 & 99187.1264367816 & 5393.8735632184 \tabularnewline
25 & 103129 & 99187.1264367816 & 3941.8735632184 \tabularnewline
26 & 83243 & 99187.1264367816 & -15944.1264367816 \tabularnewline
27 & 37110 & 99187.1264367816 & -62077.1264367816 \tabularnewline
28 & 113344 & 134872.813953488 & -21528.8139534884 \tabularnewline
29 & 139165 & 99187.1264367816 & 39977.8735632184 \tabularnewline
30 & 86652 & 99187.1264367816 & -12535.1264367816 \tabularnewline
31 & 112302 & 134872.813953488 & -22570.8139534884 \tabularnewline
32 & 69652 & 31635.1578947368 & 38016.8421052632 \tabularnewline
33 & 119442 & 134872.813953488 & -15430.8139534884 \tabularnewline
34 & 69867 & 99187.1264367816 & -29320.1264367816 \tabularnewline
35 & 70168 & 99187.1264367816 & -29019.1264367816 \tabularnewline
36 & 31081 & 31635.1578947368 & -554.157894736843 \tabularnewline
37 & 103925 & 134872.813953488 & -30947.8139534884 \tabularnewline
38 & 92622 & 134872.813953488 & -42250.8139534884 \tabularnewline
39 & 79011 & 99187.1264367816 & -20176.1264367816 \tabularnewline
40 & 93487 & 99187.1264367816 & -5700.1264367816 \tabularnewline
41 & 64520 & 99187.1264367816 & -34667.1264367816 \tabularnewline
42 & 93473 & 99187.1264367816 & -5714.1264367816 \tabularnewline
43 & 114360 & 134872.813953488 & -20512.8139534884 \tabularnewline
44 & 33032 & 31635.1578947368 & 1396.84210526316 \tabularnewline
45 & 96125 & 99187.1264367816 & -3062.1264367816 \tabularnewline
46 & 151911 & 134872.813953488 & 17038.1860465116 \tabularnewline
47 & 89256 & 99187.1264367816 & -9931.1264367816 \tabularnewline
48 & 95671 & 99187.1264367816 & -3516.1264367816 \tabularnewline
49 & 5950 & 31635.1578947368 & -25685.1578947368 \tabularnewline
50 & 149695 & 134872.813953488 & 14822.1860465116 \tabularnewline
51 & 32551 & 31635.1578947368 & 915.842105263157 \tabularnewline
52 & 31701 & 31635.1578947368 & 65.8421052631566 \tabularnewline
53 & 100087 & 99187.1264367816 & 899.873563218396 \tabularnewline
54 & 169707 & 134872.813953488 & 34834.1860465116 \tabularnewline
55 & 150491 & 134872.813953488 & 15618.1860465116 \tabularnewline
56 & 120192 & 134872.813953488 & -14680.8139534884 \tabularnewline
57 & 95893 & 99187.1264367816 & -3294.1264367816 \tabularnewline
58 & 151715 & 134872.813953488 & 16842.1860465116 \tabularnewline
59 & 176225 & 134872.813953488 & 41352.1860465116 \tabularnewline
60 & 59900 & 99187.1264367816 & -39287.1264367816 \tabularnewline
61 & 104767 & 99187.1264367816 & 5579.8735632184 \tabularnewline
62 & 114799 & 99187.1264367816 & 15611.8735632184 \tabularnewline
63 & 72128 & 99187.1264367816 & -27059.1264367816 \tabularnewline
64 & 143592 & 99187.1264367816 & 44404.8735632184 \tabularnewline
65 & 89626 & 99187.1264367816 & -9561.1264367816 \tabularnewline
66 & 131072 & 99187.1264367816 & 31884.8735632184 \tabularnewline
67 & 126817 & 99187.1264367816 & 27629.8735632184 \tabularnewline
68 & 81351 & 99187.1264367816 & -17836.1264367816 \tabularnewline
69 & 22618 & 31635.1578947368 & -9017.15789473684 \tabularnewline
70 & 88977 & 99187.1264367816 & -10210.1264367816 \tabularnewline
71 & 92059 & 99187.1264367816 & -7128.1264367816 \tabularnewline
72 & 81897 & 99187.1264367816 & -17290.1264367816 \tabularnewline
73 & 108146 & 134872.813953488 & -26726.8139534884 \tabularnewline
74 & 126372 & 134872.813953488 & -8500.81395348837 \tabularnewline
75 & 249771 & 99187.1264367816 & 150583.873563218 \tabularnewline
76 & 71154 & 99187.1264367816 & -28033.1264367816 \tabularnewline
77 & 71571 & 99187.1264367816 & -27616.1264367816 \tabularnewline
78 & 55918 & 99187.1264367816 & -43269.1264367816 \tabularnewline
79 & 160141 & 134872.813953488 & 25268.1860465116 \tabularnewline
80 & 38692 & 99187.1264367816 & -60495.1264367816 \tabularnewline
81 & 102812 & 99187.1264367816 & 3624.8735632184 \tabularnewline
82 & 56622 & 31635.1578947368 & 24986.8421052632 \tabularnewline
83 & 15986 & 99187.1264367816 & -83201.1264367816 \tabularnewline
84 & 123534 & 99187.1264367816 & 24346.8735632184 \tabularnewline
85 & 108535 & 134872.813953488 & -26337.8139534884 \tabularnewline
86 & 93879 & 134872.813953488 & -40993.8139534884 \tabularnewline
87 & 144551 & 99187.1264367816 & 45363.8735632184 \tabularnewline
88 & 56750 & 99187.1264367816 & -42437.1264367816 \tabularnewline
89 & 127654 & 99187.1264367816 & 28466.8735632184 \tabularnewline
90 & 65594 & 99187.1264367816 & -33593.1264367816 \tabularnewline
91 & 59938 & 99187.1264367816 & -39249.1264367816 \tabularnewline
92 & 146975 & 134872.813953488 & 12102.1860465116 \tabularnewline
93 & 143372 & 99187.1264367816 & 44184.8735632184 \tabularnewline
94 & 168553 & 99187.1264367816 & 69365.8735632184 \tabularnewline
95 & 183500 & 134872.813953488 & 48627.1860465116 \tabularnewline
96 & 165986 & 134872.813953488 & 31113.1860465116 \tabularnewline
97 & 184923 & 134872.813953488 & 50050.1860465116 \tabularnewline
98 & 140358 & 134872.813953488 & 5485.18604651163 \tabularnewline
99 & 149959 & 134872.813953488 & 15086.1860465116 \tabularnewline
100 & 57224 & 99187.1264367816 & -41963.1264367816 \tabularnewline
101 & 43750 & 31635.1578947368 & 12114.8421052632 \tabularnewline
102 & 48029 & 99187.1264367816 & -51158.1264367816 \tabularnewline
103 & 104978 & 99187.1264367816 & 5790.8735632184 \tabularnewline
104 & 100046 & 99187.1264367816 & 858.873563218396 \tabularnewline
105 & 101047 & 134872.813953488 & -33825.8139534884 \tabularnewline
106 & 197426 & 99187.1264367816 & 98238.8735632184 \tabularnewline
107 & 160902 & 99187.1264367816 & 61714.8735632184 \tabularnewline
108 & 147172 & 134872.813953488 & 12299.1860465116 \tabularnewline
109 & 109432 & 134872.813953488 & -25440.8139534884 \tabularnewline
110 & 1168 & 31635.1578947368 & -30467.1578947368 \tabularnewline
111 & 83248 & 99187.1264367816 & -15939.1264367816 \tabularnewline
112 & 25162 & 31635.1578947368 & -6473.15789473684 \tabularnewline
113 & 45724 & 99187.1264367816 & -53463.1264367816 \tabularnewline
114 & 855 & 31635.1578947368 & -30780.1578947368 \tabularnewline
115 & 101382 & 99187.1264367816 & 2194.8735632184 \tabularnewline
116 & 14116 & 31635.1578947368 & -17519.1578947368 \tabularnewline
117 & 89506 & 134872.813953488 & -45366.8139534884 \tabularnewline
118 & 135356 & 99187.1264367816 & 36168.8735632184 \tabularnewline
119 & 116066 & 99187.1264367816 & 16878.8735632184 \tabularnewline
120 & 144244 & 99187.1264367816 & 45056.8735632184 \tabularnewline
121 & 8773 & 31635.1578947368 & -22862.1578947368 \tabularnewline
122 & 102153 & 99187.1264367816 & 2965.8735632184 \tabularnewline
123 & 117440 & 134872.813953488 & -17432.8139534884 \tabularnewline
124 & 104128 & 99187.1264367816 & 4940.8735632184 \tabularnewline
125 & 134238 & 134872.813953488 & -634.813953488367 \tabularnewline
126 & 134047 & 134872.813953488 & -825.813953488367 \tabularnewline
127 & 279488 & 134872.813953488 & 144615.186046512 \tabularnewline
128 & 79756 & 99187.1264367816 & -19431.1264367816 \tabularnewline
129 & 66089 & 31635.1578947368 & 34453.8421052632 \tabularnewline
130 & 102070 & 99187.1264367816 & 2882.8735632184 \tabularnewline
131 & 146760 & 134872.813953488 & 11887.1860465116 \tabularnewline
132 & 154771 & 99187.1264367816 & 55583.8735632184 \tabularnewline
133 & 165933 & 134872.813953488 & 31060.1860465116 \tabularnewline
134 & 64593 & 99187.1264367816 & -34594.1264367816 \tabularnewline
135 & 92280 & 99187.1264367816 & -6907.1264367816 \tabularnewline
136 & 67150 & 99187.1264367816 & -32037.1264367816 \tabularnewline
137 & 128692 & 99187.1264367816 & 29504.8735632184 \tabularnewline
138 & 124089 & 134872.813953488 & -10783.8139534884 \tabularnewline
139 & 125386 & 99187.1264367816 & 26198.8735632184 \tabularnewline
140 & 37238 & 99187.1264367816 & -61949.1264367816 \tabularnewline
141 & 140015 & 134872.813953488 & 5142.18604651163 \tabularnewline
142 & 150047 & 99187.1264367816 & 50859.8735632184 \tabularnewline
143 & 154451 & 99187.1264367816 & 55263.8735632184 \tabularnewline
144 & 156349 & 134872.813953488 & 21476.1860465116 \tabularnewline
145 & 84601 & 99187.1264367816 & -14586.1264367816 \tabularnewline
146 & 68946 & 134872.813953488 & -65926.8139534884 \tabularnewline
147 & 6179 & 31635.1578947368 & -25456.1578947368 \tabularnewline
148 & 52789 & 31635.1578947368 & 21153.8421052632 \tabularnewline
149 & 100350 & 99187.1264367816 & 1162.8735632184 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156884&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]99187.1264367816[/C][C]41636.8735632184[/C][/ROW]
[ROW][C]2[/C][C]110459[/C][C]99187.1264367816[/C][C]11271.8735632184[/C][/ROW]
[ROW][C]3[/C][C]105079[/C][C]99187.1264367816[/C][C]5891.8735632184[/C][/ROW]
[ROW][C]4[/C][C]112098[/C][C]99187.1264367816[/C][C]12910.8735632184[/C][/ROW]
[ROW][C]5[/C][C]43929[/C][C]99187.1264367816[/C][C]-55258.1264367816[/C][/ROW]
[ROW][C]6[/C][C]76173[/C][C]31635.1578947368[/C][C]44537.8421052632[/C][/ROW]
[ROW][C]7[/C][C]22807[/C][C]31635.1578947368[/C][C]-8828.15789473684[/C][/ROW]
[ROW][C]8[/C][C]144408[/C][C]99187.1264367816[/C][C]45220.8735632184[/C][/ROW]
[ROW][C]9[/C][C]66485[/C][C]99187.1264367816[/C][C]-32702.1264367816[/C][/ROW]
[ROW][C]10[/C][C]79089[/C][C]134872.813953488[/C][C]-55783.8139534884[/C][/ROW]
[ROW][C]11[/C][C]81625[/C][C]99187.1264367816[/C][C]-17562.1264367816[/C][/ROW]
[ROW][C]12[/C][C]68788[/C][C]99187.1264367816[/C][C]-30399.1264367816[/C][/ROW]
[ROW][C]13[/C][C]103297[/C][C]134872.813953488[/C][C]-31575.8139534884[/C][/ROW]
[ROW][C]14[/C][C]69446[/C][C]99187.1264367816[/C][C]-29741.1264367816[/C][/ROW]
[ROW][C]15[/C][C]114948[/C][C]134872.813953488[/C][C]-19924.8139534884[/C][/ROW]
[ROW][C]16[/C][C]167949[/C][C]134872.813953488[/C][C]33076.1860465116[/C][/ROW]
[ROW][C]17[/C][C]125081[/C][C]134872.813953488[/C][C]-9791.81395348837[/C][/ROW]
[ROW][C]18[/C][C]125818[/C][C]99187.1264367816[/C][C]26630.8735632184[/C][/ROW]
[ROW][C]19[/C][C]136588[/C][C]99187.1264367816[/C][C]37400.8735632184[/C][/ROW]
[ROW][C]20[/C][C]112431[/C][C]99187.1264367816[/C][C]13243.8735632184[/C][/ROW]
[ROW][C]21[/C][C]82317[/C][C]99187.1264367816[/C][C]-16870.1264367816[/C][/ROW]
[ROW][C]22[/C][C]118906[/C][C]99187.1264367816[/C][C]19718.8735632184[/C][/ROW]
[ROW][C]23[/C][C]83515[/C][C]99187.1264367816[/C][C]-15672.1264367816[/C][/ROW]
[ROW][C]24[/C][C]104581[/C][C]99187.1264367816[/C][C]5393.8735632184[/C][/ROW]
[ROW][C]25[/C][C]103129[/C][C]99187.1264367816[/C][C]3941.8735632184[/C][/ROW]
[ROW][C]26[/C][C]83243[/C][C]99187.1264367816[/C][C]-15944.1264367816[/C][/ROW]
[ROW][C]27[/C][C]37110[/C][C]99187.1264367816[/C][C]-62077.1264367816[/C][/ROW]
[ROW][C]28[/C][C]113344[/C][C]134872.813953488[/C][C]-21528.8139534884[/C][/ROW]
[ROW][C]29[/C][C]139165[/C][C]99187.1264367816[/C][C]39977.8735632184[/C][/ROW]
[ROW][C]30[/C][C]86652[/C][C]99187.1264367816[/C][C]-12535.1264367816[/C][/ROW]
[ROW][C]31[/C][C]112302[/C][C]134872.813953488[/C][C]-22570.8139534884[/C][/ROW]
[ROW][C]32[/C][C]69652[/C][C]31635.1578947368[/C][C]38016.8421052632[/C][/ROW]
[ROW][C]33[/C][C]119442[/C][C]134872.813953488[/C][C]-15430.8139534884[/C][/ROW]
[ROW][C]34[/C][C]69867[/C][C]99187.1264367816[/C][C]-29320.1264367816[/C][/ROW]
[ROW][C]35[/C][C]70168[/C][C]99187.1264367816[/C][C]-29019.1264367816[/C][/ROW]
[ROW][C]36[/C][C]31081[/C][C]31635.1578947368[/C][C]-554.157894736843[/C][/ROW]
[ROW][C]37[/C][C]103925[/C][C]134872.813953488[/C][C]-30947.8139534884[/C][/ROW]
[ROW][C]38[/C][C]92622[/C][C]134872.813953488[/C][C]-42250.8139534884[/C][/ROW]
[ROW][C]39[/C][C]79011[/C][C]99187.1264367816[/C][C]-20176.1264367816[/C][/ROW]
[ROW][C]40[/C][C]93487[/C][C]99187.1264367816[/C][C]-5700.1264367816[/C][/ROW]
[ROW][C]41[/C][C]64520[/C][C]99187.1264367816[/C][C]-34667.1264367816[/C][/ROW]
[ROW][C]42[/C][C]93473[/C][C]99187.1264367816[/C][C]-5714.1264367816[/C][/ROW]
[ROW][C]43[/C][C]114360[/C][C]134872.813953488[/C][C]-20512.8139534884[/C][/ROW]
[ROW][C]44[/C][C]33032[/C][C]31635.1578947368[/C][C]1396.84210526316[/C][/ROW]
[ROW][C]45[/C][C]96125[/C][C]99187.1264367816[/C][C]-3062.1264367816[/C][/ROW]
[ROW][C]46[/C][C]151911[/C][C]134872.813953488[/C][C]17038.1860465116[/C][/ROW]
[ROW][C]47[/C][C]89256[/C][C]99187.1264367816[/C][C]-9931.1264367816[/C][/ROW]
[ROW][C]48[/C][C]95671[/C][C]99187.1264367816[/C][C]-3516.1264367816[/C][/ROW]
[ROW][C]49[/C][C]5950[/C][C]31635.1578947368[/C][C]-25685.1578947368[/C][/ROW]
[ROW][C]50[/C][C]149695[/C][C]134872.813953488[/C][C]14822.1860465116[/C][/ROW]
[ROW][C]51[/C][C]32551[/C][C]31635.1578947368[/C][C]915.842105263157[/C][/ROW]
[ROW][C]52[/C][C]31701[/C][C]31635.1578947368[/C][C]65.8421052631566[/C][/ROW]
[ROW][C]53[/C][C]100087[/C][C]99187.1264367816[/C][C]899.873563218396[/C][/ROW]
[ROW][C]54[/C][C]169707[/C][C]134872.813953488[/C][C]34834.1860465116[/C][/ROW]
[ROW][C]55[/C][C]150491[/C][C]134872.813953488[/C][C]15618.1860465116[/C][/ROW]
[ROW][C]56[/C][C]120192[/C][C]134872.813953488[/C][C]-14680.8139534884[/C][/ROW]
[ROW][C]57[/C][C]95893[/C][C]99187.1264367816[/C][C]-3294.1264367816[/C][/ROW]
[ROW][C]58[/C][C]151715[/C][C]134872.813953488[/C][C]16842.1860465116[/C][/ROW]
[ROW][C]59[/C][C]176225[/C][C]134872.813953488[/C][C]41352.1860465116[/C][/ROW]
[ROW][C]60[/C][C]59900[/C][C]99187.1264367816[/C][C]-39287.1264367816[/C][/ROW]
[ROW][C]61[/C][C]104767[/C][C]99187.1264367816[/C][C]5579.8735632184[/C][/ROW]
[ROW][C]62[/C][C]114799[/C][C]99187.1264367816[/C][C]15611.8735632184[/C][/ROW]
[ROW][C]63[/C][C]72128[/C][C]99187.1264367816[/C][C]-27059.1264367816[/C][/ROW]
[ROW][C]64[/C][C]143592[/C][C]99187.1264367816[/C][C]44404.8735632184[/C][/ROW]
[ROW][C]65[/C][C]89626[/C][C]99187.1264367816[/C][C]-9561.1264367816[/C][/ROW]
[ROW][C]66[/C][C]131072[/C][C]99187.1264367816[/C][C]31884.8735632184[/C][/ROW]
[ROW][C]67[/C][C]126817[/C][C]99187.1264367816[/C][C]27629.8735632184[/C][/ROW]
[ROW][C]68[/C][C]81351[/C][C]99187.1264367816[/C][C]-17836.1264367816[/C][/ROW]
[ROW][C]69[/C][C]22618[/C][C]31635.1578947368[/C][C]-9017.15789473684[/C][/ROW]
[ROW][C]70[/C][C]88977[/C][C]99187.1264367816[/C][C]-10210.1264367816[/C][/ROW]
[ROW][C]71[/C][C]92059[/C][C]99187.1264367816[/C][C]-7128.1264367816[/C][/ROW]
[ROW][C]72[/C][C]81897[/C][C]99187.1264367816[/C][C]-17290.1264367816[/C][/ROW]
[ROW][C]73[/C][C]108146[/C][C]134872.813953488[/C][C]-26726.8139534884[/C][/ROW]
[ROW][C]74[/C][C]126372[/C][C]134872.813953488[/C][C]-8500.81395348837[/C][/ROW]
[ROW][C]75[/C][C]249771[/C][C]99187.1264367816[/C][C]150583.873563218[/C][/ROW]
[ROW][C]76[/C][C]71154[/C][C]99187.1264367816[/C][C]-28033.1264367816[/C][/ROW]
[ROW][C]77[/C][C]71571[/C][C]99187.1264367816[/C][C]-27616.1264367816[/C][/ROW]
[ROW][C]78[/C][C]55918[/C][C]99187.1264367816[/C][C]-43269.1264367816[/C][/ROW]
[ROW][C]79[/C][C]160141[/C][C]134872.813953488[/C][C]25268.1860465116[/C][/ROW]
[ROW][C]80[/C][C]38692[/C][C]99187.1264367816[/C][C]-60495.1264367816[/C][/ROW]
[ROW][C]81[/C][C]102812[/C][C]99187.1264367816[/C][C]3624.8735632184[/C][/ROW]
[ROW][C]82[/C][C]56622[/C][C]31635.1578947368[/C][C]24986.8421052632[/C][/ROW]
[ROW][C]83[/C][C]15986[/C][C]99187.1264367816[/C][C]-83201.1264367816[/C][/ROW]
[ROW][C]84[/C][C]123534[/C][C]99187.1264367816[/C][C]24346.8735632184[/C][/ROW]
[ROW][C]85[/C][C]108535[/C][C]134872.813953488[/C][C]-26337.8139534884[/C][/ROW]
[ROW][C]86[/C][C]93879[/C][C]134872.813953488[/C][C]-40993.8139534884[/C][/ROW]
[ROW][C]87[/C][C]144551[/C][C]99187.1264367816[/C][C]45363.8735632184[/C][/ROW]
[ROW][C]88[/C][C]56750[/C][C]99187.1264367816[/C][C]-42437.1264367816[/C][/ROW]
[ROW][C]89[/C][C]127654[/C][C]99187.1264367816[/C][C]28466.8735632184[/C][/ROW]
[ROW][C]90[/C][C]65594[/C][C]99187.1264367816[/C][C]-33593.1264367816[/C][/ROW]
[ROW][C]91[/C][C]59938[/C][C]99187.1264367816[/C][C]-39249.1264367816[/C][/ROW]
[ROW][C]92[/C][C]146975[/C][C]134872.813953488[/C][C]12102.1860465116[/C][/ROW]
[ROW][C]93[/C][C]143372[/C][C]99187.1264367816[/C][C]44184.8735632184[/C][/ROW]
[ROW][C]94[/C][C]168553[/C][C]99187.1264367816[/C][C]69365.8735632184[/C][/ROW]
[ROW][C]95[/C][C]183500[/C][C]134872.813953488[/C][C]48627.1860465116[/C][/ROW]
[ROW][C]96[/C][C]165986[/C][C]134872.813953488[/C][C]31113.1860465116[/C][/ROW]
[ROW][C]97[/C][C]184923[/C][C]134872.813953488[/C][C]50050.1860465116[/C][/ROW]
[ROW][C]98[/C][C]140358[/C][C]134872.813953488[/C][C]5485.18604651163[/C][/ROW]
[ROW][C]99[/C][C]149959[/C][C]134872.813953488[/C][C]15086.1860465116[/C][/ROW]
[ROW][C]100[/C][C]57224[/C][C]99187.1264367816[/C][C]-41963.1264367816[/C][/ROW]
[ROW][C]101[/C][C]43750[/C][C]31635.1578947368[/C][C]12114.8421052632[/C][/ROW]
[ROW][C]102[/C][C]48029[/C][C]99187.1264367816[/C][C]-51158.1264367816[/C][/ROW]
[ROW][C]103[/C][C]104978[/C][C]99187.1264367816[/C][C]5790.8735632184[/C][/ROW]
[ROW][C]104[/C][C]100046[/C][C]99187.1264367816[/C][C]858.873563218396[/C][/ROW]
[ROW][C]105[/C][C]101047[/C][C]134872.813953488[/C][C]-33825.8139534884[/C][/ROW]
[ROW][C]106[/C][C]197426[/C][C]99187.1264367816[/C][C]98238.8735632184[/C][/ROW]
[ROW][C]107[/C][C]160902[/C][C]99187.1264367816[/C][C]61714.8735632184[/C][/ROW]
[ROW][C]108[/C][C]147172[/C][C]134872.813953488[/C][C]12299.1860465116[/C][/ROW]
[ROW][C]109[/C][C]109432[/C][C]134872.813953488[/C][C]-25440.8139534884[/C][/ROW]
[ROW][C]110[/C][C]1168[/C][C]31635.1578947368[/C][C]-30467.1578947368[/C][/ROW]
[ROW][C]111[/C][C]83248[/C][C]99187.1264367816[/C][C]-15939.1264367816[/C][/ROW]
[ROW][C]112[/C][C]25162[/C][C]31635.1578947368[/C][C]-6473.15789473684[/C][/ROW]
[ROW][C]113[/C][C]45724[/C][C]99187.1264367816[/C][C]-53463.1264367816[/C][/ROW]
[ROW][C]114[/C][C]855[/C][C]31635.1578947368[/C][C]-30780.1578947368[/C][/ROW]
[ROW][C]115[/C][C]101382[/C][C]99187.1264367816[/C][C]2194.8735632184[/C][/ROW]
[ROW][C]116[/C][C]14116[/C][C]31635.1578947368[/C][C]-17519.1578947368[/C][/ROW]
[ROW][C]117[/C][C]89506[/C][C]134872.813953488[/C][C]-45366.8139534884[/C][/ROW]
[ROW][C]118[/C][C]135356[/C][C]99187.1264367816[/C][C]36168.8735632184[/C][/ROW]
[ROW][C]119[/C][C]116066[/C][C]99187.1264367816[/C][C]16878.8735632184[/C][/ROW]
[ROW][C]120[/C][C]144244[/C][C]99187.1264367816[/C][C]45056.8735632184[/C][/ROW]
[ROW][C]121[/C][C]8773[/C][C]31635.1578947368[/C][C]-22862.1578947368[/C][/ROW]
[ROW][C]122[/C][C]102153[/C][C]99187.1264367816[/C][C]2965.8735632184[/C][/ROW]
[ROW][C]123[/C][C]117440[/C][C]134872.813953488[/C][C]-17432.8139534884[/C][/ROW]
[ROW][C]124[/C][C]104128[/C][C]99187.1264367816[/C][C]4940.8735632184[/C][/ROW]
[ROW][C]125[/C][C]134238[/C][C]134872.813953488[/C][C]-634.813953488367[/C][/ROW]
[ROW][C]126[/C][C]134047[/C][C]134872.813953488[/C][C]-825.813953488367[/C][/ROW]
[ROW][C]127[/C][C]279488[/C][C]134872.813953488[/C][C]144615.186046512[/C][/ROW]
[ROW][C]128[/C][C]79756[/C][C]99187.1264367816[/C][C]-19431.1264367816[/C][/ROW]
[ROW][C]129[/C][C]66089[/C][C]31635.1578947368[/C][C]34453.8421052632[/C][/ROW]
[ROW][C]130[/C][C]102070[/C][C]99187.1264367816[/C][C]2882.8735632184[/C][/ROW]
[ROW][C]131[/C][C]146760[/C][C]134872.813953488[/C][C]11887.1860465116[/C][/ROW]
[ROW][C]132[/C][C]154771[/C][C]99187.1264367816[/C][C]55583.8735632184[/C][/ROW]
[ROW][C]133[/C][C]165933[/C][C]134872.813953488[/C][C]31060.1860465116[/C][/ROW]
[ROW][C]134[/C][C]64593[/C][C]99187.1264367816[/C][C]-34594.1264367816[/C][/ROW]
[ROW][C]135[/C][C]92280[/C][C]99187.1264367816[/C][C]-6907.1264367816[/C][/ROW]
[ROW][C]136[/C][C]67150[/C][C]99187.1264367816[/C][C]-32037.1264367816[/C][/ROW]
[ROW][C]137[/C][C]128692[/C][C]99187.1264367816[/C][C]29504.8735632184[/C][/ROW]
[ROW][C]138[/C][C]124089[/C][C]134872.813953488[/C][C]-10783.8139534884[/C][/ROW]
[ROW][C]139[/C][C]125386[/C][C]99187.1264367816[/C][C]26198.8735632184[/C][/ROW]
[ROW][C]140[/C][C]37238[/C][C]99187.1264367816[/C][C]-61949.1264367816[/C][/ROW]
[ROW][C]141[/C][C]140015[/C][C]134872.813953488[/C][C]5142.18604651163[/C][/ROW]
[ROW][C]142[/C][C]150047[/C][C]99187.1264367816[/C][C]50859.8735632184[/C][/ROW]
[ROW][C]143[/C][C]154451[/C][C]99187.1264367816[/C][C]55263.8735632184[/C][/ROW]
[ROW][C]144[/C][C]156349[/C][C]134872.813953488[/C][C]21476.1860465116[/C][/ROW]
[ROW][C]145[/C][C]84601[/C][C]99187.1264367816[/C][C]-14586.1264367816[/C][/ROW]
[ROW][C]146[/C][C]68946[/C][C]134872.813953488[/C][C]-65926.8139534884[/C][/ROW]
[ROW][C]147[/C][C]6179[/C][C]31635.1578947368[/C][C]-25456.1578947368[/C][/ROW]
[ROW][C]148[/C][C]52789[/C][C]31635.1578947368[/C][C]21153.8421052632[/C][/ROW]
[ROW][C]149[/C][C]100350[/C][C]99187.1264367816[/C][C]1162.8735632184[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156884&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156884&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
114082499187.126436781641636.8735632184
211045999187.126436781611271.8735632184
310507999187.12643678165891.8735632184
411209899187.126436781612910.8735632184
54392999187.1264367816-55258.1264367816
67617331635.157894736844537.8421052632
72280731635.1578947368-8828.15789473684
814440899187.126436781645220.8735632184
96648599187.1264367816-32702.1264367816
1079089134872.813953488-55783.8139534884
118162599187.1264367816-17562.1264367816
126878899187.1264367816-30399.1264367816
13103297134872.813953488-31575.8139534884
146944699187.1264367816-29741.1264367816
15114948134872.813953488-19924.8139534884
16167949134872.81395348833076.1860465116
17125081134872.813953488-9791.81395348837
1812581899187.126436781626630.8735632184
1913658899187.126436781637400.8735632184
2011243199187.126436781613243.8735632184
218231799187.1264367816-16870.1264367816
2211890699187.126436781619718.8735632184
238351599187.1264367816-15672.1264367816
2410458199187.12643678165393.8735632184
2510312999187.12643678163941.8735632184
268324399187.1264367816-15944.1264367816
273711099187.1264367816-62077.1264367816
28113344134872.813953488-21528.8139534884
2913916599187.126436781639977.8735632184
308665299187.1264367816-12535.1264367816
31112302134872.813953488-22570.8139534884
326965231635.157894736838016.8421052632
33119442134872.813953488-15430.8139534884
346986799187.1264367816-29320.1264367816
357016899187.1264367816-29019.1264367816
363108131635.1578947368-554.157894736843
37103925134872.813953488-30947.8139534884
3892622134872.813953488-42250.8139534884
397901199187.1264367816-20176.1264367816
409348799187.1264367816-5700.1264367816
416452099187.1264367816-34667.1264367816
429347399187.1264367816-5714.1264367816
43114360134872.813953488-20512.8139534884
443303231635.15789473681396.84210526316
459612599187.1264367816-3062.1264367816
46151911134872.81395348817038.1860465116
478925699187.1264367816-9931.1264367816
489567199187.1264367816-3516.1264367816
49595031635.1578947368-25685.1578947368
50149695134872.81395348814822.1860465116
513255131635.1578947368915.842105263157
523170131635.157894736865.8421052631566
5310008799187.1264367816899.873563218396
54169707134872.81395348834834.1860465116
55150491134872.81395348815618.1860465116
56120192134872.813953488-14680.8139534884
579589399187.1264367816-3294.1264367816
58151715134872.81395348816842.1860465116
59176225134872.81395348841352.1860465116
605990099187.1264367816-39287.1264367816
6110476799187.12643678165579.8735632184
6211479999187.126436781615611.8735632184
637212899187.1264367816-27059.1264367816
6414359299187.126436781644404.8735632184
658962699187.1264367816-9561.1264367816
6613107299187.126436781631884.8735632184
6712681799187.126436781627629.8735632184
688135199187.1264367816-17836.1264367816
692261831635.1578947368-9017.15789473684
708897799187.1264367816-10210.1264367816
719205999187.1264367816-7128.1264367816
728189799187.1264367816-17290.1264367816
73108146134872.813953488-26726.8139534884
74126372134872.813953488-8500.81395348837
7524977199187.1264367816150583.873563218
767115499187.1264367816-28033.1264367816
777157199187.1264367816-27616.1264367816
785591899187.1264367816-43269.1264367816
79160141134872.81395348825268.1860465116
803869299187.1264367816-60495.1264367816
8110281299187.12643678163624.8735632184
825662231635.157894736824986.8421052632
831598699187.1264367816-83201.1264367816
8412353499187.126436781624346.8735632184
85108535134872.813953488-26337.8139534884
8693879134872.813953488-40993.8139534884
8714455199187.126436781645363.8735632184
885675099187.1264367816-42437.1264367816
8912765499187.126436781628466.8735632184
906559499187.1264367816-33593.1264367816
915993899187.1264367816-39249.1264367816
92146975134872.81395348812102.1860465116
9314337299187.126436781644184.8735632184
9416855399187.126436781669365.8735632184
95183500134872.81395348848627.1860465116
96165986134872.81395348831113.1860465116
97184923134872.81395348850050.1860465116
98140358134872.8139534885485.18604651163
99149959134872.81395348815086.1860465116
1005722499187.1264367816-41963.1264367816
1014375031635.157894736812114.8421052632
1024802999187.1264367816-51158.1264367816
10310497899187.12643678165790.8735632184
10410004699187.1264367816858.873563218396
105101047134872.813953488-33825.8139534884
10619742699187.126436781698238.8735632184
10716090299187.126436781661714.8735632184
108147172134872.81395348812299.1860465116
109109432134872.813953488-25440.8139534884
110116831635.1578947368-30467.1578947368
1118324899187.1264367816-15939.1264367816
1122516231635.1578947368-6473.15789473684
1134572499187.1264367816-53463.1264367816
11485531635.1578947368-30780.1578947368
11510138299187.12643678162194.8735632184
1161411631635.1578947368-17519.1578947368
11789506134872.813953488-45366.8139534884
11813535699187.126436781636168.8735632184
11911606699187.126436781616878.8735632184
12014424499187.126436781645056.8735632184
121877331635.1578947368-22862.1578947368
12210215399187.12643678162965.8735632184
123117440134872.813953488-17432.8139534884
12410412899187.12643678164940.8735632184
125134238134872.813953488-634.813953488367
126134047134872.813953488-825.813953488367
127279488134872.813953488144615.186046512
1287975699187.1264367816-19431.1264367816
1296608931635.157894736834453.8421052632
13010207099187.12643678162882.8735632184
131146760134872.81395348811887.1860465116
13215477199187.126436781655583.8735632184
133165933134872.81395348831060.1860465116
1346459399187.1264367816-34594.1264367816
1359228099187.1264367816-6907.1264367816
1366715099187.1264367816-32037.1264367816
13712869299187.126436781629504.8735632184
138124089134872.813953488-10783.8139534884
13912538699187.126436781626198.8735632184
1403723899187.1264367816-61949.1264367816
141140015134872.8139534885142.18604651163
14215004799187.126436781650859.8735632184
14315445199187.126436781655263.8735632184
144156349134872.81395348821476.1860465116
1458460199187.1264367816-14586.1264367816
14668946134872.813953488-65926.8139534884
147617931635.1578947368-25456.1578947368
1485278931635.157894736821153.8421052632
14910035099187.12643678161162.8735632184


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = none ; par4 = no ;
Parameters (R input):
par1 = 2 ; 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')
}