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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationFri, 23 Dec 2011 08:30:13 -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/23/t1324647061s9v73jhnfkkx5to.htm/, Retrieved Thu, 31 Oct 2024 23:21:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160388, Retrieved Thu, 31 Oct 2024 23:21:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142
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)] [Recursive Partiti...] [2011-12-13 17:44:25] [570fce4db58fd7864ac807c4286d6e49]
- R  D      [Recursive Partitioning (Regression Trees)] [] [2011-12-23 13:30:13] [204816f6f70a8d342ddc2b9d4f4a80d3] [Current]
- R           [Recursive Partitioning (Regression Trees)] [] [2011-12-23 17:24:19] [19d77e37efa419fdc040c74a96874aff]
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Dataseries X:
279055	73	3	96	130
212408	75	4	75	143
233939	83	16	70	118
222117	106	2	134	146
179751	55	1	72	73
70849	28	3	8	89
605767	135	0	173	146
33186	19	0	1	22
227332	62	7	88	132
258874	48	0	98	92
359064	120	0	112	147
264989	131	7	125	203
212638	87	10	57	113
368577	85	4	139	171
269455	88	10	87	87
397992	190	0	176	208
335567	76	8	114	153
428322	172	4	121	97
182016	58	3	103	95
267365	89	8	135	197
279428	73	0	123	160
508849	111	1	99	148
206722	47	5	74	84
200004	58	9	103	227
257139	133	1	158	154
270941	138	0	116	151
324969	134	5	114	142
329962	92	0	150	148
190867	60	0	64	110
393860	79	0	150	149
327660	89	3	143	179
269239	83	6	50	149
391045	105	1	145	187
130446	49	4	56	153
430118	104	4	141	163
273950	56	0	83	127
428077	128	0	112	151
254312	93	2	79	100
120351	35	1	33	46
395643	211	2	152	156
345875	86	10	126	128
216827	82	10	97	111
224524	83	5	84	119
182485	69	6	68	148
157164	85	1	50	65
459455	157	2	101	134
78800	42	2	20	66
217932	84	0	101	201
368086	123	10	150	177
230299	70	3	129	156
244782	81	0	99	158
24188	24	0	8	7
400109	334	8	88	175
65029	17	5	21	61
101097	64	3	30	41
309810	67	1	102	133
369627	90	5	163	228
367127	204	6	132	140
377704	154	0	161	155
280106	90	12	90	141
400971	153	10	160	181
315924	122	12	139	75
291391	124	11	104	97
295075	93	8	103	142
280018	81	3	66	136
267432	71	0	163	87
217181	141	6	93	140
258166	159	10	85	169
260919	87	2	150	129
182961	73	5	143	92
256967	74	13	107	160
73566	32	6	22	67
272362	93	7	85	179
229056	62	2	101	90
229851	70	5	131	144
371391	91	4	140	144
398210	104	3	156	144
220419	111	6	81	134
231884	72	2	137	146
217714	72	0	102	121
200046	53	1	72	112
483074	131	1	161	145
146100	72	5	30	99
295224	109	2	120	96
80953	25	0	49	27
217384	63	0	121	77
179344	62	6	76	137
415550	221	1	85	151
389059	129	4	151	126
180679	106	1	165	159
299505	104	1	89	101
292260	84	3	168	144
199481	68	10	48	102
282361	78	1	149	135
329281	89	4	75	147
234577	48	5	107	155
297995	67	7	116	138
329583	89	0	173	113
416463	163	12	155	248
415683	119	13	165	116
297080	142	9	121	176
318283	70	0	156	140
224033	199	0	86	59
43287	14	4	13	64
238089	87	4	120	40
263322	160	0	117	98
299566	60	0	133	139
321797	95	0	169	135
193926	95	0	39	97
175138	105	0	125	142
354041	78	5	82	155
303273	91	1	148	115
23668	13	0	12	0
196743	79	0	146	103
61857	25	4	23	30
217543	54	0	87	130
440711	128	1	164	102
21054	16	0	4	0
252805	52	5	81	77
31961	22	0	18	9
360436	125	3	118	150
251948	77	7	76	163
187003	96	14	55	148
180842	58	3	62	94
38214	34	0	16	21
280392	56	3	98	151
358276	84	0	137	187
211775	67	0	50	171
447335	90	4	152	170
348017	99	0	163	145
441946	133	3	142	198
215177	43	0	80	152
130177	47	0	59	112
316128	363	4	94	173
466139	198	5	128	177
162279	62	16	63	153
416643	140	6	127	161
178322	86	5	60	115
292443	54	2	118	147
283913	100	1	110	124
244802	126	1	45	57
387072	125	9	96	144
246963	92	1	128	126
173260	63	3	41	78
346748	108	11	146	153
176654	59	5	147	196
268189	95	2	121	130
314070	112	1	185	159
1	0	9	0	0
14688	10	0	4	0
98	1	0	0	0
455	2	0	0	0
0	0	1	0	0
0	0	0	0	0
291650	94	2	85	94
415421	168	3	164	129
0	0	0	0	0
203	4	0	0	0
7199	5	0	7	0
46660	20	0	12	13
17547	5	0	0	4
121550	46	0	37	89
969	2	0	0	0
242774	75	2	62	71




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160388&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160388&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160388&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 time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Goodness of Fit
Correlation0.8887
R-squared0.7898
RMSE57452.0215

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8887 \tabularnewline
R-squared & 0.7898 \tabularnewline
RMSE & 57452.0215 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160388&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8887[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7898[/C][/ROW]
[ROW][C]RMSE[/C][C]57452.0215[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160388&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160388&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
Correlation0.8887
R-squared0.7898
RMSE57452.0215







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1279055247658.831396.2
2212408231212.866666667-18804.8666666667
3233939231212.8666666672726.13333333333
4222117302217.619047619-80100.6190476191
5179751179775.625-24.625
67084937007.444444444433841.5555555556
7605767372649.894736842233117.105263158
83318637007.4444444444-3821.44444444445
9227332247658.8-20326.8
10258874247658.811215.2
11359064372649.894736842-13585.8947368421
12264989372649.894736842-107660.894736842
13212638231212.866666667-18574.8666666667
14368577302217.61904761966359.3809523809
15269455302217.619047619-32762.6190476191
16397992372649.89473684225342.1052631579
17335567302217.61904761933349.3809523809
18428322372649.89473684255672.1052631579
19182016247658.8-65642.8
20267365302217.619047619-34852.6190476191
21279428247658.831769.2
22508849372649.894736842136199.105263158
23206722179775.62526946.375
24200004247658.8-47654.8
25257139372649.894736842-115510.894736842
26270941372649.894736842-101708.894736842
27324969372649.894736842-47680.8947368421
28329962302217.61904761927744.3809523809
29190867179775.62511091.375
30393860302217.61904761991642.3809523809
31327660302217.61904761925442.3809523809
32269239231212.86666666738026.1333333333
33391045302217.61904761988827.3809523809
34130446179775.625-49329.625
35430118302217.619047619127900.380952381
36273950247658.826291.2
37428077372649.89473684255427.1052631579
38254312231212.86666666723099.1333333333
3912035196043.7524307.25
40395643372649.89473684222993.1052631579
41345875302217.61904761943657.3809523809
42216827302217.619047619-85390.6190476191
43224524302217.619047619-77693.6190476191
44182485179775.6252709.375
45157164231212.866666667-74048.8666666667
46459455372649.89473684286805.1052631579
477880096043.75-17243.75
48217932302217.619047619-84285.6190476191
49368086372649.894736842-4563.89473684208
50230299247658.8-17359.8
51244782302217.619047619-57435.6190476191
522418837007.4444444444-12819.4444444444
53400109372649.89473684227459.1052631579
546502996043.75-31014.75
5510109796043.755053.25
56309810247658.862151.2
57369627302217.61904761967409.3809523809
58367127372649.894736842-5522.89473684208
59377704372649.8947368425054.10526315792
60280106302217.619047619-22111.6190476191
61400971372649.89473684228321.1052631579
62315924372649.894736842-56725.8947368421
63291391372649.894736842-81258.8947368421
64295075302217.619047619-7142.61904761905
65280018231212.86666666748805.1333333333
66267432247658.819773.2
67217181372649.894736842-155468.894736842
68258166372649.894736842-114483.894736842
69260919302217.619047619-41298.6190476191
70182961247658.8-64697.8
71256967247658.89308.20000000001
727356696043.75-22477.75
73272362302217.619047619-29855.6190476191
74229056247658.8-18602.8
75229851247658.8-17807.8
76371391302217.61904761969173.3809523809
77398210302217.61904761995992.3809523809
78220419231212.866666667-10793.8666666667
79231884247658.8-15774.8
80217714247658.8-29944.8
81200046179775.62520270.375
82483074372649.894736842110424.105263158
8314610096043.7550056.25
84295224302217.619047619-6993.61904761905
8580953179775.625-98822.625
86217384247658.8-30274.8
87179344179775.625-431.625
88415550372649.89473684242900.1052631579
89389059372649.89473684216409.1052631579
90180679302217.619047619-121538.619047619
91299505302217.619047619-2712.61904761905
92292260302217.619047619-9957.61904761905
93199481179775.62519705.375
94282361302217.619047619-19856.6190476191
95329281231212.86666666798068.1333333333
96234577247658.8-13081.8
97297995247658.850336.2
98329583302217.61904761927365.3809523809
99416463372649.89473684243813.1052631579
100415683372649.89473684243033.1052631579
101297080372649.894736842-75569.8947368421
102318283247658.870624.2
103224033372649.894736842-148616.894736842
1044328737007.44444444446279.55555555555
105238089302217.619047619-64128.6190476191
106263322372649.894736842-109327.894736842
107299566247658.851907.2
108321797302217.61904761919579.3809523809
109193926231212.866666667-37286.8666666667
110175138302217.619047619-127079.619047619
111354041302217.61904761951823.3809523809
112303273302217.6190476191055.38095238095
1132366837007.4444444444-13339.4444444444
114196743302217.619047619-105474.619047619
1156185796043.75-34186.75
116217543247658.8-30115.8
117440711372649.89473684268061.1052631579
1182105437007.4444444444-15953.4444444444
119252805179775.62573029.375
1203196137007.4444444444-5046.44444444445
121360436372649.894736842-12213.8947368421
122251948231212.86666666720735.1333333333
123187003231212.866666667-44209.8666666667
124180842179775.6251066.375
1253821437007.44444444441206.55555555555
126280392247658.832733.2
127358276302217.61904761956058.3809523809
128211775179775.62531999.375
129447335302217.619047619145117.380952381
130348017302217.61904761945799.3809523809
131441946372649.89473684269296.1052631579
132215177179775.62535401.375
133130177179775.625-49598.625
134316128372649.894736842-56521.8947368421
135466139372649.89473684293489.1052631579
136162279179775.625-17496.625
137416643372649.89473684243993.1052631579
138178322231212.866666667-52890.8666666667
139292443247658.844784.2
140283913302217.619047619-18304.6190476191
141244802231212.86666666713589.1333333333
142387072372649.89473684214422.1052631579
143246963302217.619047619-55254.6190476191
144173260179775.625-6515.625
145346748302217.61904761944530.3809523809
146176654247658.8-71004.8
147268189302217.619047619-34028.6190476191
148314070372649.894736842-58579.8947368421
14913741.81818181818-3740.81818181818
150146883741.8181818181810946.1818181818
151983741.81818181818-3643.81818181818
1524553741.81818181818-3286.81818181818
15303741.81818181818-3741.81818181818
15403741.81818181818-3741.81818181818
155291650302217.619047619-10567.6190476191
156415421372649.89473684242771.1052631579
15703741.81818181818-3741.81818181818
1582033741.81818181818-3538.81818181818
15971993741.818181818183457.18181818182
1604666037007.44444444449652.55555555555
161175473741.8181818181813805.1818181818
16212155096043.7525506.25
1639693741.81818181818-2772.81818181818
164242774231212.86666666711561.1333333333

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 279055 & 247658.8 & 31396.2 \tabularnewline
2 & 212408 & 231212.866666667 & -18804.8666666667 \tabularnewline
3 & 233939 & 231212.866666667 & 2726.13333333333 \tabularnewline
4 & 222117 & 302217.619047619 & -80100.6190476191 \tabularnewline
5 & 179751 & 179775.625 & -24.625 \tabularnewline
6 & 70849 & 37007.4444444444 & 33841.5555555556 \tabularnewline
7 & 605767 & 372649.894736842 & 233117.105263158 \tabularnewline
8 & 33186 & 37007.4444444444 & -3821.44444444445 \tabularnewline
9 & 227332 & 247658.8 & -20326.8 \tabularnewline
10 & 258874 & 247658.8 & 11215.2 \tabularnewline
11 & 359064 & 372649.894736842 & -13585.8947368421 \tabularnewline
12 & 264989 & 372649.894736842 & -107660.894736842 \tabularnewline
13 & 212638 & 231212.866666667 & -18574.8666666667 \tabularnewline
14 & 368577 & 302217.619047619 & 66359.3809523809 \tabularnewline
15 & 269455 & 302217.619047619 & -32762.6190476191 \tabularnewline
16 & 397992 & 372649.894736842 & 25342.1052631579 \tabularnewline
17 & 335567 & 302217.619047619 & 33349.3809523809 \tabularnewline
18 & 428322 & 372649.894736842 & 55672.1052631579 \tabularnewline
19 & 182016 & 247658.8 & -65642.8 \tabularnewline
20 & 267365 & 302217.619047619 & -34852.6190476191 \tabularnewline
21 & 279428 & 247658.8 & 31769.2 \tabularnewline
22 & 508849 & 372649.894736842 & 136199.105263158 \tabularnewline
23 & 206722 & 179775.625 & 26946.375 \tabularnewline
24 & 200004 & 247658.8 & -47654.8 \tabularnewline
25 & 257139 & 372649.894736842 & -115510.894736842 \tabularnewline
26 & 270941 & 372649.894736842 & -101708.894736842 \tabularnewline
27 & 324969 & 372649.894736842 & -47680.8947368421 \tabularnewline
28 & 329962 & 302217.619047619 & 27744.3809523809 \tabularnewline
29 & 190867 & 179775.625 & 11091.375 \tabularnewline
30 & 393860 & 302217.619047619 & 91642.3809523809 \tabularnewline
31 & 327660 & 302217.619047619 & 25442.3809523809 \tabularnewline
32 & 269239 & 231212.866666667 & 38026.1333333333 \tabularnewline
33 & 391045 & 302217.619047619 & 88827.3809523809 \tabularnewline
34 & 130446 & 179775.625 & -49329.625 \tabularnewline
35 & 430118 & 302217.619047619 & 127900.380952381 \tabularnewline
36 & 273950 & 247658.8 & 26291.2 \tabularnewline
37 & 428077 & 372649.894736842 & 55427.1052631579 \tabularnewline
38 & 254312 & 231212.866666667 & 23099.1333333333 \tabularnewline
39 & 120351 & 96043.75 & 24307.25 \tabularnewline
40 & 395643 & 372649.894736842 & 22993.1052631579 \tabularnewline
41 & 345875 & 302217.619047619 & 43657.3809523809 \tabularnewline
42 & 216827 & 302217.619047619 & -85390.6190476191 \tabularnewline
43 & 224524 & 302217.619047619 & -77693.6190476191 \tabularnewline
44 & 182485 & 179775.625 & 2709.375 \tabularnewline
45 & 157164 & 231212.866666667 & -74048.8666666667 \tabularnewline
46 & 459455 & 372649.894736842 & 86805.1052631579 \tabularnewline
47 & 78800 & 96043.75 & -17243.75 \tabularnewline
48 & 217932 & 302217.619047619 & -84285.6190476191 \tabularnewline
49 & 368086 & 372649.894736842 & -4563.89473684208 \tabularnewline
50 & 230299 & 247658.8 & -17359.8 \tabularnewline
51 & 244782 & 302217.619047619 & -57435.6190476191 \tabularnewline
52 & 24188 & 37007.4444444444 & -12819.4444444444 \tabularnewline
53 & 400109 & 372649.894736842 & 27459.1052631579 \tabularnewline
54 & 65029 & 96043.75 & -31014.75 \tabularnewline
55 & 101097 & 96043.75 & 5053.25 \tabularnewline
56 & 309810 & 247658.8 & 62151.2 \tabularnewline
57 & 369627 & 302217.619047619 & 67409.3809523809 \tabularnewline
58 & 367127 & 372649.894736842 & -5522.89473684208 \tabularnewline
59 & 377704 & 372649.894736842 & 5054.10526315792 \tabularnewline
60 & 280106 & 302217.619047619 & -22111.6190476191 \tabularnewline
61 & 400971 & 372649.894736842 & 28321.1052631579 \tabularnewline
62 & 315924 & 372649.894736842 & -56725.8947368421 \tabularnewline
63 & 291391 & 372649.894736842 & -81258.8947368421 \tabularnewline
64 & 295075 & 302217.619047619 & -7142.61904761905 \tabularnewline
65 & 280018 & 231212.866666667 & 48805.1333333333 \tabularnewline
66 & 267432 & 247658.8 & 19773.2 \tabularnewline
67 & 217181 & 372649.894736842 & -155468.894736842 \tabularnewline
68 & 258166 & 372649.894736842 & -114483.894736842 \tabularnewline
69 & 260919 & 302217.619047619 & -41298.6190476191 \tabularnewline
70 & 182961 & 247658.8 & -64697.8 \tabularnewline
71 & 256967 & 247658.8 & 9308.20000000001 \tabularnewline
72 & 73566 & 96043.75 & -22477.75 \tabularnewline
73 & 272362 & 302217.619047619 & -29855.6190476191 \tabularnewline
74 & 229056 & 247658.8 & -18602.8 \tabularnewline
75 & 229851 & 247658.8 & -17807.8 \tabularnewline
76 & 371391 & 302217.619047619 & 69173.3809523809 \tabularnewline
77 & 398210 & 302217.619047619 & 95992.3809523809 \tabularnewline
78 & 220419 & 231212.866666667 & -10793.8666666667 \tabularnewline
79 & 231884 & 247658.8 & -15774.8 \tabularnewline
80 & 217714 & 247658.8 & -29944.8 \tabularnewline
81 & 200046 & 179775.625 & 20270.375 \tabularnewline
82 & 483074 & 372649.894736842 & 110424.105263158 \tabularnewline
83 & 146100 & 96043.75 & 50056.25 \tabularnewline
84 & 295224 & 302217.619047619 & -6993.61904761905 \tabularnewline
85 & 80953 & 179775.625 & -98822.625 \tabularnewline
86 & 217384 & 247658.8 & -30274.8 \tabularnewline
87 & 179344 & 179775.625 & -431.625 \tabularnewline
88 & 415550 & 372649.894736842 & 42900.1052631579 \tabularnewline
89 & 389059 & 372649.894736842 & 16409.1052631579 \tabularnewline
90 & 180679 & 302217.619047619 & -121538.619047619 \tabularnewline
91 & 299505 & 302217.619047619 & -2712.61904761905 \tabularnewline
92 & 292260 & 302217.619047619 & -9957.61904761905 \tabularnewline
93 & 199481 & 179775.625 & 19705.375 \tabularnewline
94 & 282361 & 302217.619047619 & -19856.6190476191 \tabularnewline
95 & 329281 & 231212.866666667 & 98068.1333333333 \tabularnewline
96 & 234577 & 247658.8 & -13081.8 \tabularnewline
97 & 297995 & 247658.8 & 50336.2 \tabularnewline
98 & 329583 & 302217.619047619 & 27365.3809523809 \tabularnewline
99 & 416463 & 372649.894736842 & 43813.1052631579 \tabularnewline
100 & 415683 & 372649.894736842 & 43033.1052631579 \tabularnewline
101 & 297080 & 372649.894736842 & -75569.8947368421 \tabularnewline
102 & 318283 & 247658.8 & 70624.2 \tabularnewline
103 & 224033 & 372649.894736842 & -148616.894736842 \tabularnewline
104 & 43287 & 37007.4444444444 & 6279.55555555555 \tabularnewline
105 & 238089 & 302217.619047619 & -64128.6190476191 \tabularnewline
106 & 263322 & 372649.894736842 & -109327.894736842 \tabularnewline
107 & 299566 & 247658.8 & 51907.2 \tabularnewline
108 & 321797 & 302217.619047619 & 19579.3809523809 \tabularnewline
109 & 193926 & 231212.866666667 & -37286.8666666667 \tabularnewline
110 & 175138 & 302217.619047619 & -127079.619047619 \tabularnewline
111 & 354041 & 302217.619047619 & 51823.3809523809 \tabularnewline
112 & 303273 & 302217.619047619 & 1055.38095238095 \tabularnewline
113 & 23668 & 37007.4444444444 & -13339.4444444444 \tabularnewline
114 & 196743 & 302217.619047619 & -105474.619047619 \tabularnewline
115 & 61857 & 96043.75 & -34186.75 \tabularnewline
116 & 217543 & 247658.8 & -30115.8 \tabularnewline
117 & 440711 & 372649.894736842 & 68061.1052631579 \tabularnewline
118 & 21054 & 37007.4444444444 & -15953.4444444444 \tabularnewline
119 & 252805 & 179775.625 & 73029.375 \tabularnewline
120 & 31961 & 37007.4444444444 & -5046.44444444445 \tabularnewline
121 & 360436 & 372649.894736842 & -12213.8947368421 \tabularnewline
122 & 251948 & 231212.866666667 & 20735.1333333333 \tabularnewline
123 & 187003 & 231212.866666667 & -44209.8666666667 \tabularnewline
124 & 180842 & 179775.625 & 1066.375 \tabularnewline
125 & 38214 & 37007.4444444444 & 1206.55555555555 \tabularnewline
126 & 280392 & 247658.8 & 32733.2 \tabularnewline
127 & 358276 & 302217.619047619 & 56058.3809523809 \tabularnewline
128 & 211775 & 179775.625 & 31999.375 \tabularnewline
129 & 447335 & 302217.619047619 & 145117.380952381 \tabularnewline
130 & 348017 & 302217.619047619 & 45799.3809523809 \tabularnewline
131 & 441946 & 372649.894736842 & 69296.1052631579 \tabularnewline
132 & 215177 & 179775.625 & 35401.375 \tabularnewline
133 & 130177 & 179775.625 & -49598.625 \tabularnewline
134 & 316128 & 372649.894736842 & -56521.8947368421 \tabularnewline
135 & 466139 & 372649.894736842 & 93489.1052631579 \tabularnewline
136 & 162279 & 179775.625 & -17496.625 \tabularnewline
137 & 416643 & 372649.894736842 & 43993.1052631579 \tabularnewline
138 & 178322 & 231212.866666667 & -52890.8666666667 \tabularnewline
139 & 292443 & 247658.8 & 44784.2 \tabularnewline
140 & 283913 & 302217.619047619 & -18304.6190476191 \tabularnewline
141 & 244802 & 231212.866666667 & 13589.1333333333 \tabularnewline
142 & 387072 & 372649.894736842 & 14422.1052631579 \tabularnewline
143 & 246963 & 302217.619047619 & -55254.6190476191 \tabularnewline
144 & 173260 & 179775.625 & -6515.625 \tabularnewline
145 & 346748 & 302217.619047619 & 44530.3809523809 \tabularnewline
146 & 176654 & 247658.8 & -71004.8 \tabularnewline
147 & 268189 & 302217.619047619 & -34028.6190476191 \tabularnewline
148 & 314070 & 372649.894736842 & -58579.8947368421 \tabularnewline
149 & 1 & 3741.81818181818 & -3740.81818181818 \tabularnewline
150 & 14688 & 3741.81818181818 & 10946.1818181818 \tabularnewline
151 & 98 & 3741.81818181818 & -3643.81818181818 \tabularnewline
152 & 455 & 3741.81818181818 & -3286.81818181818 \tabularnewline
153 & 0 & 3741.81818181818 & -3741.81818181818 \tabularnewline
154 & 0 & 3741.81818181818 & -3741.81818181818 \tabularnewline
155 & 291650 & 302217.619047619 & -10567.6190476191 \tabularnewline
156 & 415421 & 372649.894736842 & 42771.1052631579 \tabularnewline
157 & 0 & 3741.81818181818 & -3741.81818181818 \tabularnewline
158 & 203 & 3741.81818181818 & -3538.81818181818 \tabularnewline
159 & 7199 & 3741.81818181818 & 3457.18181818182 \tabularnewline
160 & 46660 & 37007.4444444444 & 9652.55555555555 \tabularnewline
161 & 17547 & 3741.81818181818 & 13805.1818181818 \tabularnewline
162 & 121550 & 96043.75 & 25506.25 \tabularnewline
163 & 969 & 3741.81818181818 & -2772.81818181818 \tabularnewline
164 & 242774 & 231212.866666667 & 11561.1333333333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160388&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]279055[/C][C]247658.8[/C][C]31396.2[/C][/ROW]
[ROW][C]2[/C][C]212408[/C][C]231212.866666667[/C][C]-18804.8666666667[/C][/ROW]
[ROW][C]3[/C][C]233939[/C][C]231212.866666667[/C][C]2726.13333333333[/C][/ROW]
[ROW][C]4[/C][C]222117[/C][C]302217.619047619[/C][C]-80100.6190476191[/C][/ROW]
[ROW][C]5[/C][C]179751[/C][C]179775.625[/C][C]-24.625[/C][/ROW]
[ROW][C]6[/C][C]70849[/C][C]37007.4444444444[/C][C]33841.5555555556[/C][/ROW]
[ROW][C]7[/C][C]605767[/C][C]372649.894736842[/C][C]233117.105263158[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]37007.4444444444[/C][C]-3821.44444444445[/C][/ROW]
[ROW][C]9[/C][C]227332[/C][C]247658.8[/C][C]-20326.8[/C][/ROW]
[ROW][C]10[/C][C]258874[/C][C]247658.8[/C][C]11215.2[/C][/ROW]
[ROW][C]11[/C][C]359064[/C][C]372649.894736842[/C][C]-13585.8947368421[/C][/ROW]
[ROW][C]12[/C][C]264989[/C][C]372649.894736842[/C][C]-107660.894736842[/C][/ROW]
[ROW][C]13[/C][C]212638[/C][C]231212.866666667[/C][C]-18574.8666666667[/C][/ROW]
[ROW][C]14[/C][C]368577[/C][C]302217.619047619[/C][C]66359.3809523809[/C][/ROW]
[ROW][C]15[/C][C]269455[/C][C]302217.619047619[/C][C]-32762.6190476191[/C][/ROW]
[ROW][C]16[/C][C]397992[/C][C]372649.894736842[/C][C]25342.1052631579[/C][/ROW]
[ROW][C]17[/C][C]335567[/C][C]302217.619047619[/C][C]33349.3809523809[/C][/ROW]
[ROW][C]18[/C][C]428322[/C][C]372649.894736842[/C][C]55672.1052631579[/C][/ROW]
[ROW][C]19[/C][C]182016[/C][C]247658.8[/C][C]-65642.8[/C][/ROW]
[ROW][C]20[/C][C]267365[/C][C]302217.619047619[/C][C]-34852.6190476191[/C][/ROW]
[ROW][C]21[/C][C]279428[/C][C]247658.8[/C][C]31769.2[/C][/ROW]
[ROW][C]22[/C][C]508849[/C][C]372649.894736842[/C][C]136199.105263158[/C][/ROW]
[ROW][C]23[/C][C]206722[/C][C]179775.625[/C][C]26946.375[/C][/ROW]
[ROW][C]24[/C][C]200004[/C][C]247658.8[/C][C]-47654.8[/C][/ROW]
[ROW][C]25[/C][C]257139[/C][C]372649.894736842[/C][C]-115510.894736842[/C][/ROW]
[ROW][C]26[/C][C]270941[/C][C]372649.894736842[/C][C]-101708.894736842[/C][/ROW]
[ROW][C]27[/C][C]324969[/C][C]372649.894736842[/C][C]-47680.8947368421[/C][/ROW]
[ROW][C]28[/C][C]329962[/C][C]302217.619047619[/C][C]27744.3809523809[/C][/ROW]
[ROW][C]29[/C][C]190867[/C][C]179775.625[/C][C]11091.375[/C][/ROW]
[ROW][C]30[/C][C]393860[/C][C]302217.619047619[/C][C]91642.3809523809[/C][/ROW]
[ROW][C]31[/C][C]327660[/C][C]302217.619047619[/C][C]25442.3809523809[/C][/ROW]
[ROW][C]32[/C][C]269239[/C][C]231212.866666667[/C][C]38026.1333333333[/C][/ROW]
[ROW][C]33[/C][C]391045[/C][C]302217.619047619[/C][C]88827.3809523809[/C][/ROW]
[ROW][C]34[/C][C]130446[/C][C]179775.625[/C][C]-49329.625[/C][/ROW]
[ROW][C]35[/C][C]430118[/C][C]302217.619047619[/C][C]127900.380952381[/C][/ROW]
[ROW][C]36[/C][C]273950[/C][C]247658.8[/C][C]26291.2[/C][/ROW]
[ROW][C]37[/C][C]428077[/C][C]372649.894736842[/C][C]55427.1052631579[/C][/ROW]
[ROW][C]38[/C][C]254312[/C][C]231212.866666667[/C][C]23099.1333333333[/C][/ROW]
[ROW][C]39[/C][C]120351[/C][C]96043.75[/C][C]24307.25[/C][/ROW]
[ROW][C]40[/C][C]395643[/C][C]372649.894736842[/C][C]22993.1052631579[/C][/ROW]
[ROW][C]41[/C][C]345875[/C][C]302217.619047619[/C][C]43657.3809523809[/C][/ROW]
[ROW][C]42[/C][C]216827[/C][C]302217.619047619[/C][C]-85390.6190476191[/C][/ROW]
[ROW][C]43[/C][C]224524[/C][C]302217.619047619[/C][C]-77693.6190476191[/C][/ROW]
[ROW][C]44[/C][C]182485[/C][C]179775.625[/C][C]2709.375[/C][/ROW]
[ROW][C]45[/C][C]157164[/C][C]231212.866666667[/C][C]-74048.8666666667[/C][/ROW]
[ROW][C]46[/C][C]459455[/C][C]372649.894736842[/C][C]86805.1052631579[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]96043.75[/C][C]-17243.75[/C][/ROW]
[ROW][C]48[/C][C]217932[/C][C]302217.619047619[/C][C]-84285.6190476191[/C][/ROW]
[ROW][C]49[/C][C]368086[/C][C]372649.894736842[/C][C]-4563.89473684208[/C][/ROW]
[ROW][C]50[/C][C]230299[/C][C]247658.8[/C][C]-17359.8[/C][/ROW]
[ROW][C]51[/C][C]244782[/C][C]302217.619047619[/C][C]-57435.6190476191[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]37007.4444444444[/C][C]-12819.4444444444[/C][/ROW]
[ROW][C]53[/C][C]400109[/C][C]372649.894736842[/C][C]27459.1052631579[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]96043.75[/C][C]-31014.75[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]96043.75[/C][C]5053.25[/C][/ROW]
[ROW][C]56[/C][C]309810[/C][C]247658.8[/C][C]62151.2[/C][/ROW]
[ROW][C]57[/C][C]369627[/C][C]302217.619047619[/C][C]67409.3809523809[/C][/ROW]
[ROW][C]58[/C][C]367127[/C][C]372649.894736842[/C][C]-5522.89473684208[/C][/ROW]
[ROW][C]59[/C][C]377704[/C][C]372649.894736842[/C][C]5054.10526315792[/C][/ROW]
[ROW][C]60[/C][C]280106[/C][C]302217.619047619[/C][C]-22111.6190476191[/C][/ROW]
[ROW][C]61[/C][C]400971[/C][C]372649.894736842[/C][C]28321.1052631579[/C][/ROW]
[ROW][C]62[/C][C]315924[/C][C]372649.894736842[/C][C]-56725.8947368421[/C][/ROW]
[ROW][C]63[/C][C]291391[/C][C]372649.894736842[/C][C]-81258.8947368421[/C][/ROW]
[ROW][C]64[/C][C]295075[/C][C]302217.619047619[/C][C]-7142.61904761905[/C][/ROW]
[ROW][C]65[/C][C]280018[/C][C]231212.866666667[/C][C]48805.1333333333[/C][/ROW]
[ROW][C]66[/C][C]267432[/C][C]247658.8[/C][C]19773.2[/C][/ROW]
[ROW][C]67[/C][C]217181[/C][C]372649.894736842[/C][C]-155468.894736842[/C][/ROW]
[ROW][C]68[/C][C]258166[/C][C]372649.894736842[/C][C]-114483.894736842[/C][/ROW]
[ROW][C]69[/C][C]260919[/C][C]302217.619047619[/C][C]-41298.6190476191[/C][/ROW]
[ROW][C]70[/C][C]182961[/C][C]247658.8[/C][C]-64697.8[/C][/ROW]
[ROW][C]71[/C][C]256967[/C][C]247658.8[/C][C]9308.20000000001[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]96043.75[/C][C]-22477.75[/C][/ROW]
[ROW][C]73[/C][C]272362[/C][C]302217.619047619[/C][C]-29855.6190476191[/C][/ROW]
[ROW][C]74[/C][C]229056[/C][C]247658.8[/C][C]-18602.8[/C][/ROW]
[ROW][C]75[/C][C]229851[/C][C]247658.8[/C][C]-17807.8[/C][/ROW]
[ROW][C]76[/C][C]371391[/C][C]302217.619047619[/C][C]69173.3809523809[/C][/ROW]
[ROW][C]77[/C][C]398210[/C][C]302217.619047619[/C][C]95992.3809523809[/C][/ROW]
[ROW][C]78[/C][C]220419[/C][C]231212.866666667[/C][C]-10793.8666666667[/C][/ROW]
[ROW][C]79[/C][C]231884[/C][C]247658.8[/C][C]-15774.8[/C][/ROW]
[ROW][C]80[/C][C]217714[/C][C]247658.8[/C][C]-29944.8[/C][/ROW]
[ROW][C]81[/C][C]200046[/C][C]179775.625[/C][C]20270.375[/C][/ROW]
[ROW][C]82[/C][C]483074[/C][C]372649.894736842[/C][C]110424.105263158[/C][/ROW]
[ROW][C]83[/C][C]146100[/C][C]96043.75[/C][C]50056.25[/C][/ROW]
[ROW][C]84[/C][C]295224[/C][C]302217.619047619[/C][C]-6993.61904761905[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]179775.625[/C][C]-98822.625[/C][/ROW]
[ROW][C]86[/C][C]217384[/C][C]247658.8[/C][C]-30274.8[/C][/ROW]
[ROW][C]87[/C][C]179344[/C][C]179775.625[/C][C]-431.625[/C][/ROW]
[ROW][C]88[/C][C]415550[/C][C]372649.894736842[/C][C]42900.1052631579[/C][/ROW]
[ROW][C]89[/C][C]389059[/C][C]372649.894736842[/C][C]16409.1052631579[/C][/ROW]
[ROW][C]90[/C][C]180679[/C][C]302217.619047619[/C][C]-121538.619047619[/C][/ROW]
[ROW][C]91[/C][C]299505[/C][C]302217.619047619[/C][C]-2712.61904761905[/C][/ROW]
[ROW][C]92[/C][C]292260[/C][C]302217.619047619[/C][C]-9957.61904761905[/C][/ROW]
[ROW][C]93[/C][C]199481[/C][C]179775.625[/C][C]19705.375[/C][/ROW]
[ROW][C]94[/C][C]282361[/C][C]302217.619047619[/C][C]-19856.6190476191[/C][/ROW]
[ROW][C]95[/C][C]329281[/C][C]231212.866666667[/C][C]98068.1333333333[/C][/ROW]
[ROW][C]96[/C][C]234577[/C][C]247658.8[/C][C]-13081.8[/C][/ROW]
[ROW][C]97[/C][C]297995[/C][C]247658.8[/C][C]50336.2[/C][/ROW]
[ROW][C]98[/C][C]329583[/C][C]302217.619047619[/C][C]27365.3809523809[/C][/ROW]
[ROW][C]99[/C][C]416463[/C][C]372649.894736842[/C][C]43813.1052631579[/C][/ROW]
[ROW][C]100[/C][C]415683[/C][C]372649.894736842[/C][C]43033.1052631579[/C][/ROW]
[ROW][C]101[/C][C]297080[/C][C]372649.894736842[/C][C]-75569.8947368421[/C][/ROW]
[ROW][C]102[/C][C]318283[/C][C]247658.8[/C][C]70624.2[/C][/ROW]
[ROW][C]103[/C][C]224033[/C][C]372649.894736842[/C][C]-148616.894736842[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]37007.4444444444[/C][C]6279.55555555555[/C][/ROW]
[ROW][C]105[/C][C]238089[/C][C]302217.619047619[/C][C]-64128.6190476191[/C][/ROW]
[ROW][C]106[/C][C]263322[/C][C]372649.894736842[/C][C]-109327.894736842[/C][/ROW]
[ROW][C]107[/C][C]299566[/C][C]247658.8[/C][C]51907.2[/C][/ROW]
[ROW][C]108[/C][C]321797[/C][C]302217.619047619[/C][C]19579.3809523809[/C][/ROW]
[ROW][C]109[/C][C]193926[/C][C]231212.866666667[/C][C]-37286.8666666667[/C][/ROW]
[ROW][C]110[/C][C]175138[/C][C]302217.619047619[/C][C]-127079.619047619[/C][/ROW]
[ROW][C]111[/C][C]354041[/C][C]302217.619047619[/C][C]51823.3809523809[/C][/ROW]
[ROW][C]112[/C][C]303273[/C][C]302217.619047619[/C][C]1055.38095238095[/C][/ROW]
[ROW][C]113[/C][C]23668[/C][C]37007.4444444444[/C][C]-13339.4444444444[/C][/ROW]
[ROW][C]114[/C][C]196743[/C][C]302217.619047619[/C][C]-105474.619047619[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]96043.75[/C][C]-34186.75[/C][/ROW]
[ROW][C]116[/C][C]217543[/C][C]247658.8[/C][C]-30115.8[/C][/ROW]
[ROW][C]117[/C][C]440711[/C][C]372649.894736842[/C][C]68061.1052631579[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]37007.4444444444[/C][C]-15953.4444444444[/C][/ROW]
[ROW][C]119[/C][C]252805[/C][C]179775.625[/C][C]73029.375[/C][/ROW]
[ROW][C]120[/C][C]31961[/C][C]37007.4444444444[/C][C]-5046.44444444445[/C][/ROW]
[ROW][C]121[/C][C]360436[/C][C]372649.894736842[/C][C]-12213.8947368421[/C][/ROW]
[ROW][C]122[/C][C]251948[/C][C]231212.866666667[/C][C]20735.1333333333[/C][/ROW]
[ROW][C]123[/C][C]187003[/C][C]231212.866666667[/C][C]-44209.8666666667[/C][/ROW]
[ROW][C]124[/C][C]180842[/C][C]179775.625[/C][C]1066.375[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]37007.4444444444[/C][C]1206.55555555555[/C][/ROW]
[ROW][C]126[/C][C]280392[/C][C]247658.8[/C][C]32733.2[/C][/ROW]
[ROW][C]127[/C][C]358276[/C][C]302217.619047619[/C][C]56058.3809523809[/C][/ROW]
[ROW][C]128[/C][C]211775[/C][C]179775.625[/C][C]31999.375[/C][/ROW]
[ROW][C]129[/C][C]447335[/C][C]302217.619047619[/C][C]145117.380952381[/C][/ROW]
[ROW][C]130[/C][C]348017[/C][C]302217.619047619[/C][C]45799.3809523809[/C][/ROW]
[ROW][C]131[/C][C]441946[/C][C]372649.894736842[/C][C]69296.1052631579[/C][/ROW]
[ROW][C]132[/C][C]215177[/C][C]179775.625[/C][C]35401.375[/C][/ROW]
[ROW][C]133[/C][C]130177[/C][C]179775.625[/C][C]-49598.625[/C][/ROW]
[ROW][C]134[/C][C]316128[/C][C]372649.894736842[/C][C]-56521.8947368421[/C][/ROW]
[ROW][C]135[/C][C]466139[/C][C]372649.894736842[/C][C]93489.1052631579[/C][/ROW]
[ROW][C]136[/C][C]162279[/C][C]179775.625[/C][C]-17496.625[/C][/ROW]
[ROW][C]137[/C][C]416643[/C][C]372649.894736842[/C][C]43993.1052631579[/C][/ROW]
[ROW][C]138[/C][C]178322[/C][C]231212.866666667[/C][C]-52890.8666666667[/C][/ROW]
[ROW][C]139[/C][C]292443[/C][C]247658.8[/C][C]44784.2[/C][/ROW]
[ROW][C]140[/C][C]283913[/C][C]302217.619047619[/C][C]-18304.6190476191[/C][/ROW]
[ROW][C]141[/C][C]244802[/C][C]231212.866666667[/C][C]13589.1333333333[/C][/ROW]
[ROW][C]142[/C][C]387072[/C][C]372649.894736842[/C][C]14422.1052631579[/C][/ROW]
[ROW][C]143[/C][C]246963[/C][C]302217.619047619[/C][C]-55254.6190476191[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]179775.625[/C][C]-6515.625[/C][/ROW]
[ROW][C]145[/C][C]346748[/C][C]302217.619047619[/C][C]44530.3809523809[/C][/ROW]
[ROW][C]146[/C][C]176654[/C][C]247658.8[/C][C]-71004.8[/C][/ROW]
[ROW][C]147[/C][C]268189[/C][C]302217.619047619[/C][C]-34028.6190476191[/C][/ROW]
[ROW][C]148[/C][C]314070[/C][C]372649.894736842[/C][C]-58579.8947368421[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]3741.81818181818[/C][C]-3740.81818181818[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]3741.81818181818[/C][C]10946.1818181818[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]3741.81818181818[/C][C]-3643.81818181818[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]3741.81818181818[/C][C]-3286.81818181818[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]3741.81818181818[/C][C]-3741.81818181818[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]3741.81818181818[/C][C]-3741.81818181818[/C][/ROW]
[ROW][C]155[/C][C]291650[/C][C]302217.619047619[/C][C]-10567.6190476191[/C][/ROW]
[ROW][C]156[/C][C]415421[/C][C]372649.894736842[/C][C]42771.1052631579[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]3741.81818181818[/C][C]-3741.81818181818[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]3741.81818181818[/C][C]-3538.81818181818[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]3741.81818181818[/C][C]3457.18181818182[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]37007.4444444444[/C][C]9652.55555555555[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]3741.81818181818[/C][C]13805.1818181818[/C][/ROW]
[ROW][C]162[/C][C]121550[/C][C]96043.75[/C][C]25506.25[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]3741.81818181818[/C][C]-2772.81818181818[/C][/ROW]
[ROW][C]164[/C][C]242774[/C][C]231212.866666667[/C][C]11561.1333333333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160388&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160388&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
1279055247658.831396.2
2212408231212.866666667-18804.8666666667
3233939231212.8666666672726.13333333333
4222117302217.619047619-80100.6190476191
5179751179775.625-24.625
67084937007.444444444433841.5555555556
7605767372649.894736842233117.105263158
83318637007.4444444444-3821.44444444445
9227332247658.8-20326.8
10258874247658.811215.2
11359064372649.894736842-13585.8947368421
12264989372649.894736842-107660.894736842
13212638231212.866666667-18574.8666666667
14368577302217.61904761966359.3809523809
15269455302217.619047619-32762.6190476191
16397992372649.89473684225342.1052631579
17335567302217.61904761933349.3809523809
18428322372649.89473684255672.1052631579
19182016247658.8-65642.8
20267365302217.619047619-34852.6190476191
21279428247658.831769.2
22508849372649.894736842136199.105263158
23206722179775.62526946.375
24200004247658.8-47654.8
25257139372649.894736842-115510.894736842
26270941372649.894736842-101708.894736842
27324969372649.894736842-47680.8947368421
28329962302217.61904761927744.3809523809
29190867179775.62511091.375
30393860302217.61904761991642.3809523809
31327660302217.61904761925442.3809523809
32269239231212.86666666738026.1333333333
33391045302217.61904761988827.3809523809
34130446179775.625-49329.625
35430118302217.619047619127900.380952381
36273950247658.826291.2
37428077372649.89473684255427.1052631579
38254312231212.86666666723099.1333333333
3912035196043.7524307.25
40395643372649.89473684222993.1052631579
41345875302217.61904761943657.3809523809
42216827302217.619047619-85390.6190476191
43224524302217.619047619-77693.6190476191
44182485179775.6252709.375
45157164231212.866666667-74048.8666666667
46459455372649.89473684286805.1052631579
477880096043.75-17243.75
48217932302217.619047619-84285.6190476191
49368086372649.894736842-4563.89473684208
50230299247658.8-17359.8
51244782302217.619047619-57435.6190476191
522418837007.4444444444-12819.4444444444
53400109372649.89473684227459.1052631579
546502996043.75-31014.75
5510109796043.755053.25
56309810247658.862151.2
57369627302217.61904761967409.3809523809
58367127372649.894736842-5522.89473684208
59377704372649.8947368425054.10526315792
60280106302217.619047619-22111.6190476191
61400971372649.89473684228321.1052631579
62315924372649.894736842-56725.8947368421
63291391372649.894736842-81258.8947368421
64295075302217.619047619-7142.61904761905
65280018231212.86666666748805.1333333333
66267432247658.819773.2
67217181372649.894736842-155468.894736842
68258166372649.894736842-114483.894736842
69260919302217.619047619-41298.6190476191
70182961247658.8-64697.8
71256967247658.89308.20000000001
727356696043.75-22477.75
73272362302217.619047619-29855.6190476191
74229056247658.8-18602.8
75229851247658.8-17807.8
76371391302217.61904761969173.3809523809
77398210302217.61904761995992.3809523809
78220419231212.866666667-10793.8666666667
79231884247658.8-15774.8
80217714247658.8-29944.8
81200046179775.62520270.375
82483074372649.894736842110424.105263158
8314610096043.7550056.25
84295224302217.619047619-6993.61904761905
8580953179775.625-98822.625
86217384247658.8-30274.8
87179344179775.625-431.625
88415550372649.89473684242900.1052631579
89389059372649.89473684216409.1052631579
90180679302217.619047619-121538.619047619
91299505302217.619047619-2712.61904761905
92292260302217.619047619-9957.61904761905
93199481179775.62519705.375
94282361302217.619047619-19856.6190476191
95329281231212.86666666798068.1333333333
96234577247658.8-13081.8
97297995247658.850336.2
98329583302217.61904761927365.3809523809
99416463372649.89473684243813.1052631579
100415683372649.89473684243033.1052631579
101297080372649.894736842-75569.8947368421
102318283247658.870624.2
103224033372649.894736842-148616.894736842
1044328737007.44444444446279.55555555555
105238089302217.619047619-64128.6190476191
106263322372649.894736842-109327.894736842
107299566247658.851907.2
108321797302217.61904761919579.3809523809
109193926231212.866666667-37286.8666666667
110175138302217.619047619-127079.619047619
111354041302217.61904761951823.3809523809
112303273302217.6190476191055.38095238095
1132366837007.4444444444-13339.4444444444
114196743302217.619047619-105474.619047619
1156185796043.75-34186.75
116217543247658.8-30115.8
117440711372649.89473684268061.1052631579
1182105437007.4444444444-15953.4444444444
119252805179775.62573029.375
1203196137007.4444444444-5046.44444444445
121360436372649.894736842-12213.8947368421
122251948231212.86666666720735.1333333333
123187003231212.866666667-44209.8666666667
124180842179775.6251066.375
1253821437007.44444444441206.55555555555
126280392247658.832733.2
127358276302217.61904761956058.3809523809
128211775179775.62531999.375
129447335302217.619047619145117.380952381
130348017302217.61904761945799.3809523809
131441946372649.89473684269296.1052631579
132215177179775.62535401.375
133130177179775.625-49598.625
134316128372649.894736842-56521.8947368421
135466139372649.89473684293489.1052631579
136162279179775.625-17496.625
137416643372649.89473684243993.1052631579
138178322231212.866666667-52890.8666666667
139292443247658.844784.2
140283913302217.619047619-18304.6190476191
141244802231212.86666666713589.1333333333
142387072372649.89473684214422.1052631579
143246963302217.619047619-55254.6190476191
144173260179775.625-6515.625
145346748302217.61904761944530.3809523809
146176654247658.8-71004.8
147268189302217.619047619-34028.6190476191
148314070372649.894736842-58579.8947368421
14913741.81818181818-3740.81818181818
150146883741.8181818181810946.1818181818
151983741.81818181818-3643.81818181818
1524553741.81818181818-3286.81818181818
15303741.81818181818-3741.81818181818
15403741.81818181818-3741.81818181818
155291650302217.619047619-10567.6190476191
156415421372649.89473684242771.1052631579
15703741.81818181818-3741.81818181818
1582033741.81818181818-3538.81818181818
15971993741.818181818183457.18181818182
1604666037007.44444444449652.55555555555
161175473741.8181818181813805.1818181818
16212155096043.7525506.25
1639693741.81818181818-2772.81818181818
164242774231212.86666666711561.1333333333



Parameters (Session):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 1 ; 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')
}