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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, 12 Aug 2012 09:13:25 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Aug/12/t1344777221vq001p1px7h0rhw.htm/, Retrieved Sat, 27 Apr 2024 20:44:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=169238, Retrieved Sat, 27 Apr 2024 20:44:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162

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)] [tree] [2011-12-09 10:01:58] [22f8bc702946f784836540059d0d9516]
-   P   [Recursive Partitioning (Regression Trees)] [Deel 3 Tree] [2011-12-18 15:47:46] [845a0512200c8372fa3331a361537afe]
-    D    [Recursive Partitioning (Regression Trees)] [Deel 3 Tree] [2011-12-18 16:36:53] [845a0512200c8372fa3331a361537afe]
- R P       [Recursive Partitioning (Regression Trees)] [Berekening 19] [2012-08-12 13:07:26] [eb6e95800005ec22b7fd76eead8d8a59]
-    D        [Recursive Partitioning (Regression Trees)] [Berekening 19] [2012-08-12 13:11:35] [eb6e95800005ec22b7fd76eead8d8a59]
-   P             [Recursive Partitioning (Regression Trees)] [Berekening 19] [2012-08-12 13:13:25] [0b94335bf72158573fe52322b9537409] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1772	158258	89	20465
1703	186930	57	33629
192	7215	18	1423
2294	129098	94	25629
3448	230587	134	54002
6813	508313	261	151036
1795	180745	56	33287
1680	185559	58	31172
1896	154581	43	28113
2917	290658	95	57803
1946	121844	75	49830
2148	184039	69	52143
1832	100324	98	21055
3059	209427	114	47007
1469	167592	57	28735
1565	154593	86	59147
1755	142018	56	78950
1234	77855	59	13497
2779	167047	87	46154
726	27997	24	53249
1048	73019	59	10726
2804	241082	99	83700
1760	195820	72	40400
2261	141899	53	33797
1848	145433	86	36205
1647	180241	31	30165
2081	202232	160	58534
1393	190230	91	44663
2741	354924	118	92556
2112	192399	44	40078
1684	182286	44	34711
1616	181590	45	31076
2227	133801	105	74608
3088	233686	123	58092
2388	219428	52	42009
1	0	1	0
2099	223044	63	36022
1669	100129	51	23333
2094	136733	47	53349
2153	249965	64	92596
2390	242379	71	49598
1701	145794	59	44093
983	96404	32	84205
2161	195891	78	63369
1276	117156	50	60132
1189	157787	94	37403
744	81293	31	24460
2231	224049	100	46456
2242	223789	87	66616
2638	160344	58	41554
658	48188	28	22346
1859	152206	68	30874
2489	294283	73	68701
2025	235223	78	35728
1911	195583	59	29010
1714	145942	54	23110
1851	208834	66	38844
980	93764	23	27084
1177	151985	66	35139
2809	190545	95	57476
1688	148922	60	33277
2097	132856	80	31141
1309	126107	60	61281
1243	112718	36	25820
1255	160930	34	23284
1293	99184	40	35378
2259	182022	69	74990
2897	138708	65	29653
1103	114408	38	64622
340	31970	15	4157
2791	225558	112	29245
1333	137011	71	50008
1441	113612	68	52338
1622	108641	70	13310
2649	162203	66	92901
1499	100098	44	10956
2302	174768	60	34241
2540	158459	97	75043
1000	80934	30	21152
1234	84971	71	42249
927	80545	68	42005
2176	287191	64	41152
956	62974	27	14399
1531	130982	38	28263
1013	75555	45	17215
1771	162154	54	48140
2613	226638	227	62897
1203	115019	110	22883
1303	105038	60	41622
1524	155537	52	40715
1829	153133	41	65897
2227	165577	76	76542
1233	151517	57	37477
1365	133686	58	53216
901	58128	38	40911
2319	245196	117	57021
1857	195576	70	73116
223	19349	12	3895
2390	225371	105	46609
1973	152796	76	29351
699	59117	28	2325
1062	91762	24	31747
1252	127987	52	32665
1154	113552	58	19249
823	85338	40	15292
596	27676	22	5842
1471	147984	47	33994
1130	122417	37	13018
0	0	0	0
1082	91529	32	98177
1134	107205	66	37941
1366	144664	44	31032
1452	136540	62	32683
869	76656	59	34545
78	3616	5	0
0	0	0	0
1127	183065	43	27525
1578	144636	83	66856
2018	156889	97	28549
919	113273	38	38610
778	43410	19	2781
1751	175774	72	41211
956	95401	41	22698
1875	118893	54	41194
731	60493	40	32689
285	19764	12	5752
1833	164062	55	26757
1147	132696	32	22527
1646	155367	54	44810
256	11796	9	0
98	10674	9	0
1403	142261	56	100674
41	6836	3	0
1786	154206	61	57786
42	5118	3	0
528	40248	16	5444
0	0	0	0
1072	122641	46	28470
1305	88837	38	61849
81	7131	4	0
261	9056	14	2179
934	76611	24	8019
1179	132697	50	39644
1147	100681	19	23494

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169238&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]5 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=169238&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169238&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 time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Goodness of Fit
Correlation0.8614
R-squared0.742
RMSE38661.0078

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8614 \tabularnewline
R-squared & 0.742 \tabularnewline
RMSE & 38661.0078 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169238&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8614[/C][/ROW]
[ROW][C]R-squared[/C][C]0.742[/C][/ROW]
[ROW][C]RMSE[/C][C]38661.0078[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=169238&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169238&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
Correlation0.8614
R-squared0.742
RMSE38661.0078






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1158258156304.3191489361953.68085106384
2186930156304.31914893630625.6808510638
372156214.692307692311000.30769230769
4129098201818.826086957-72720.8260869565
5230587253685-23098
6508313253685254628
7180745156304.31914893624440.6808510638
8185559156304.31914893629254.6808510638
9154581156304.319148936-1723.31914893616
1029065825368536973
11121844156304.319148936-34460.3191489362
12184039201818.826086957-17779.8260869565
13100324156304.319148936-55980.3191489362
14209427253685-44258
15167592156304.31914893611287.6808510638
16154593156304.319148936-1711.31914893616
17142018156304.319148936-14286.3191489362
1877855123104.541666667-45249.5416666667
19167047253685-86638
202799736422.8571428571-8425.85714285714
217301982091.0526315789-9072.05263157895
22241082253685-12603
23195820156304.31914893639515.6808510638
24141899201818.826086957-59919.8260869565
25145433156304.319148936-10871.3191489362
26180241156304.31914893623936.6808510638
27202232156304.31914893645927.6808510638
28190230156304.31914893633925.6808510638
29354924253685101239
30192399201818.826086957-9419.82608695651
31182286156304.31914893625981.6808510638
32181590156304.31914893625285.6808510638
33133801201818.826086957-68017.8260869565
34233686253685-19999
35219428201818.82608695717609.1739130435
3606214.69230769231-6214.69230769231
37223044201818.82608695721225.1739130435
38100129156304.319148936-56175.3191489362
39136733156304.319148936-19571.3191489362
40249965201818.82608695748146.1739130435
41242379201818.82608695740560.1739130435
42145794156304.319148936-10510.3191489362
439640482091.052631578914312.9473684211
44195891201818.826086957-5927.82608695651
45117156123104.541666667-5948.54166666667
46157787123104.54166666734682.4583333333
478129382091.0526315789-798.052631578947
48224049201818.82608695722230.1739130435
49223789201818.82608695721970.1739130435
50160344201818.826086957-41474.8260869565
514818836422.857142857111765.1428571429
52152206156304.319148936-4098.31914893616
53294283201818.82608695792464.1739130435
54235223156304.31914893678918.6808510638
55195583156304.31914893639278.6808510638
56145942156304.319148936-10362.3191489362
57208834156304.31914893652529.6808510638
589376482091.052631578911672.9473684211
59151985123104.54166666728880.4583333333
60190545253685-63140
61148922156304.319148936-7382.31914893616
62132856156304.319148936-23448.3191489362
63126107123104.5416666673002.45833333333
64112718123104.541666667-10386.5416666667
65160930123104.54166666737825.4583333333
6699184123104.541666667-23920.5416666667
67182022201818.826086957-19796.8260869565
68138708253685-114977
69114408123104.541666667-8696.54166666667
703197036422.8571428571-4452.85714285714
71225558253685-28127
72137011123104.54166666713906.4583333333
73113612156304.319148936-42692.3191489362
74108641156304.319148936-47663.3191489362
75162203201818.826086957-39615.8260869565
76100098156304.319148936-56206.3191489362
77174768201818.826086957-27050.8260869565
78158459201818.826086957-43359.8260869565
798093482091.0526315789-1157.05263157895
8084971123104.541666667-38133.5416666667
818054582091.0526315789-1546.05263157895
82287191201818.82608695785372.1739130435
836297482091.0526315789-19117.0526315789
84130982156304.319148936-25322.3191489362
857555582091.0526315789-6536.05263157895
86162154156304.3191489365849.68085106384
87226638201818.82608695724819.1739130435
88115019123104.541666667-8085.54166666667
89105038123104.541666667-18066.5416666667
90155537156304.319148936-767.319148936163
91153133156304.319148936-3171.31914893616
92165577201818.826086957-36241.8260869565
93151517123104.54166666728412.4583333333
94133686123104.54166666710581.4583333333
955812882091.0526315789-23963.0526315789
96245196201818.82608695743377.1739130435
97195576156304.31914893639271.6808510638
98193496214.6923076923113134.3076923077
99225371201818.82608695723552.1739130435
100152796156304.319148936-3508.31914893616
1015911736422.857142857122694.1428571429
1029176282091.05263157899670.94736842105
103127987123104.5416666674882.45833333333
104113552123104.541666667-9552.54166666667
1058533882091.05263157893246.94736842105
1062767636422.8571428571-8746.85714285714
107147984156304.319148936-8320.31914893616
108122417123104.541666667-687.541666666672
10906214.69230769231-6214.69230769231
1109152982091.05263157899437.94736842105
111107205123104.541666667-15899.5416666667
112144664156304.319148936-11640.3191489362
113136540156304.319148936-19764.3191489362
1147665682091.0526315789-5435.05263157895
11536166214.69230769231-2598.69230769231
11606214.69230769231-6214.69230769231
117183065123104.54166666759960.4583333333
118144636156304.319148936-11668.3191489362
119156889156304.319148936584.680851063837
12011327382091.052631578931181.9473684211
1214341082091.0526315789-38681.0526315789
122175774156304.31914893619469.6808510638
1239540182091.052631578913309.9473684211
124118893156304.319148936-37411.3191489362
1256049382091.0526315789-21598.0526315789
1261976436422.8571428571-16658.8571428571
127164062156304.3191489367757.68085106384
128132696123104.5416666679591.45833333333
129155367156304.319148936-937.319148936163
130117966214.692307692315581.30769230769
131106746214.692307692314459.30769230769
132142261156304.319148936-14043.3191489362
13368366214.69230769231621.307692307692
134154206156304.319148936-2098.31914893616
13551186214.69230769231-1096.69230769231
1364024836422.85714285713825.14285714286
13706214.69230769231-6214.69230769231
13812264182091.052631578940549.9473684211
13988837123104.541666667-34267.5416666667
14071316214.69230769231916.307692307692
14190566214.692307692312841.30769230769
1427661182091.0526315789-5480.05263157895
143132697123104.5416666679592.45833333333
144100681123104.541666667-22423.5416666667

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 158258 & 156304.319148936 & 1953.68085106384 \tabularnewline
2 & 186930 & 156304.319148936 & 30625.6808510638 \tabularnewline
3 & 7215 & 6214.69230769231 & 1000.30769230769 \tabularnewline
4 & 129098 & 201818.826086957 & -72720.8260869565 \tabularnewline
5 & 230587 & 253685 & -23098 \tabularnewline
6 & 508313 & 253685 & 254628 \tabularnewline
7 & 180745 & 156304.319148936 & 24440.6808510638 \tabularnewline
8 & 185559 & 156304.319148936 & 29254.6808510638 \tabularnewline
9 & 154581 & 156304.319148936 & -1723.31914893616 \tabularnewline
10 & 290658 & 253685 & 36973 \tabularnewline
11 & 121844 & 156304.319148936 & -34460.3191489362 \tabularnewline
12 & 184039 & 201818.826086957 & -17779.8260869565 \tabularnewline
13 & 100324 & 156304.319148936 & -55980.3191489362 \tabularnewline
14 & 209427 & 253685 & -44258 \tabularnewline
15 & 167592 & 156304.319148936 & 11287.6808510638 \tabularnewline
16 & 154593 & 156304.319148936 & -1711.31914893616 \tabularnewline
17 & 142018 & 156304.319148936 & -14286.3191489362 \tabularnewline
18 & 77855 & 123104.541666667 & -45249.5416666667 \tabularnewline
19 & 167047 & 253685 & -86638 \tabularnewline
20 & 27997 & 36422.8571428571 & -8425.85714285714 \tabularnewline
21 & 73019 & 82091.0526315789 & -9072.05263157895 \tabularnewline
22 & 241082 & 253685 & -12603 \tabularnewline
23 & 195820 & 156304.319148936 & 39515.6808510638 \tabularnewline
24 & 141899 & 201818.826086957 & -59919.8260869565 \tabularnewline
25 & 145433 & 156304.319148936 & -10871.3191489362 \tabularnewline
26 & 180241 & 156304.319148936 & 23936.6808510638 \tabularnewline
27 & 202232 & 156304.319148936 & 45927.6808510638 \tabularnewline
28 & 190230 & 156304.319148936 & 33925.6808510638 \tabularnewline
29 & 354924 & 253685 & 101239 \tabularnewline
30 & 192399 & 201818.826086957 & -9419.82608695651 \tabularnewline
31 & 182286 & 156304.319148936 & 25981.6808510638 \tabularnewline
32 & 181590 & 156304.319148936 & 25285.6808510638 \tabularnewline
33 & 133801 & 201818.826086957 & -68017.8260869565 \tabularnewline
34 & 233686 & 253685 & -19999 \tabularnewline
35 & 219428 & 201818.826086957 & 17609.1739130435 \tabularnewline
36 & 0 & 6214.69230769231 & -6214.69230769231 \tabularnewline
37 & 223044 & 201818.826086957 & 21225.1739130435 \tabularnewline
38 & 100129 & 156304.319148936 & -56175.3191489362 \tabularnewline
39 & 136733 & 156304.319148936 & -19571.3191489362 \tabularnewline
40 & 249965 & 201818.826086957 & 48146.1739130435 \tabularnewline
41 & 242379 & 201818.826086957 & 40560.1739130435 \tabularnewline
42 & 145794 & 156304.319148936 & -10510.3191489362 \tabularnewline
43 & 96404 & 82091.0526315789 & 14312.9473684211 \tabularnewline
44 & 195891 & 201818.826086957 & -5927.82608695651 \tabularnewline
45 & 117156 & 123104.541666667 & -5948.54166666667 \tabularnewline
46 & 157787 & 123104.541666667 & 34682.4583333333 \tabularnewline
47 & 81293 & 82091.0526315789 & -798.052631578947 \tabularnewline
48 & 224049 & 201818.826086957 & 22230.1739130435 \tabularnewline
49 & 223789 & 201818.826086957 & 21970.1739130435 \tabularnewline
50 & 160344 & 201818.826086957 & -41474.8260869565 \tabularnewline
51 & 48188 & 36422.8571428571 & 11765.1428571429 \tabularnewline
52 & 152206 & 156304.319148936 & -4098.31914893616 \tabularnewline
53 & 294283 & 201818.826086957 & 92464.1739130435 \tabularnewline
54 & 235223 & 156304.319148936 & 78918.6808510638 \tabularnewline
55 & 195583 & 156304.319148936 & 39278.6808510638 \tabularnewline
56 & 145942 & 156304.319148936 & -10362.3191489362 \tabularnewline
57 & 208834 & 156304.319148936 & 52529.6808510638 \tabularnewline
58 & 93764 & 82091.0526315789 & 11672.9473684211 \tabularnewline
59 & 151985 & 123104.541666667 & 28880.4583333333 \tabularnewline
60 & 190545 & 253685 & -63140 \tabularnewline
61 & 148922 & 156304.319148936 & -7382.31914893616 \tabularnewline
62 & 132856 & 156304.319148936 & -23448.3191489362 \tabularnewline
63 & 126107 & 123104.541666667 & 3002.45833333333 \tabularnewline
64 & 112718 & 123104.541666667 & -10386.5416666667 \tabularnewline
65 & 160930 & 123104.541666667 & 37825.4583333333 \tabularnewline
66 & 99184 & 123104.541666667 & -23920.5416666667 \tabularnewline
67 & 182022 & 201818.826086957 & -19796.8260869565 \tabularnewline
68 & 138708 & 253685 & -114977 \tabularnewline
69 & 114408 & 123104.541666667 & -8696.54166666667 \tabularnewline
70 & 31970 & 36422.8571428571 & -4452.85714285714 \tabularnewline
71 & 225558 & 253685 & -28127 \tabularnewline
72 & 137011 & 123104.541666667 & 13906.4583333333 \tabularnewline
73 & 113612 & 156304.319148936 & -42692.3191489362 \tabularnewline
74 & 108641 & 156304.319148936 & -47663.3191489362 \tabularnewline
75 & 162203 & 201818.826086957 & -39615.8260869565 \tabularnewline
76 & 100098 & 156304.319148936 & -56206.3191489362 \tabularnewline
77 & 174768 & 201818.826086957 & -27050.8260869565 \tabularnewline
78 & 158459 & 201818.826086957 & -43359.8260869565 \tabularnewline
79 & 80934 & 82091.0526315789 & -1157.05263157895 \tabularnewline
80 & 84971 & 123104.541666667 & -38133.5416666667 \tabularnewline
81 & 80545 & 82091.0526315789 & -1546.05263157895 \tabularnewline
82 & 287191 & 201818.826086957 & 85372.1739130435 \tabularnewline
83 & 62974 & 82091.0526315789 & -19117.0526315789 \tabularnewline
84 & 130982 & 156304.319148936 & -25322.3191489362 \tabularnewline
85 & 75555 & 82091.0526315789 & -6536.05263157895 \tabularnewline
86 & 162154 & 156304.319148936 & 5849.68085106384 \tabularnewline
87 & 226638 & 201818.826086957 & 24819.1739130435 \tabularnewline
88 & 115019 & 123104.541666667 & -8085.54166666667 \tabularnewline
89 & 105038 & 123104.541666667 & -18066.5416666667 \tabularnewline
90 & 155537 & 156304.319148936 & -767.319148936163 \tabularnewline
91 & 153133 & 156304.319148936 & -3171.31914893616 \tabularnewline
92 & 165577 & 201818.826086957 & -36241.8260869565 \tabularnewline
93 & 151517 & 123104.541666667 & 28412.4583333333 \tabularnewline
94 & 133686 & 123104.541666667 & 10581.4583333333 \tabularnewline
95 & 58128 & 82091.0526315789 & -23963.0526315789 \tabularnewline
96 & 245196 & 201818.826086957 & 43377.1739130435 \tabularnewline
97 & 195576 & 156304.319148936 & 39271.6808510638 \tabularnewline
98 & 19349 & 6214.69230769231 & 13134.3076923077 \tabularnewline
99 & 225371 & 201818.826086957 & 23552.1739130435 \tabularnewline
100 & 152796 & 156304.319148936 & -3508.31914893616 \tabularnewline
101 & 59117 & 36422.8571428571 & 22694.1428571429 \tabularnewline
102 & 91762 & 82091.0526315789 & 9670.94736842105 \tabularnewline
103 & 127987 & 123104.541666667 & 4882.45833333333 \tabularnewline
104 & 113552 & 123104.541666667 & -9552.54166666667 \tabularnewline
105 & 85338 & 82091.0526315789 & 3246.94736842105 \tabularnewline
106 & 27676 & 36422.8571428571 & -8746.85714285714 \tabularnewline
107 & 147984 & 156304.319148936 & -8320.31914893616 \tabularnewline
108 & 122417 & 123104.541666667 & -687.541666666672 \tabularnewline
109 & 0 & 6214.69230769231 & -6214.69230769231 \tabularnewline
110 & 91529 & 82091.0526315789 & 9437.94736842105 \tabularnewline
111 & 107205 & 123104.541666667 & -15899.5416666667 \tabularnewline
112 & 144664 & 156304.319148936 & -11640.3191489362 \tabularnewline
113 & 136540 & 156304.319148936 & -19764.3191489362 \tabularnewline
114 & 76656 & 82091.0526315789 & -5435.05263157895 \tabularnewline
115 & 3616 & 6214.69230769231 & -2598.69230769231 \tabularnewline
116 & 0 & 6214.69230769231 & -6214.69230769231 \tabularnewline
117 & 183065 & 123104.541666667 & 59960.4583333333 \tabularnewline
118 & 144636 & 156304.319148936 & -11668.3191489362 \tabularnewline
119 & 156889 & 156304.319148936 & 584.680851063837 \tabularnewline
120 & 113273 & 82091.0526315789 & 31181.9473684211 \tabularnewline
121 & 43410 & 82091.0526315789 & -38681.0526315789 \tabularnewline
122 & 175774 & 156304.319148936 & 19469.6808510638 \tabularnewline
123 & 95401 & 82091.0526315789 & 13309.9473684211 \tabularnewline
124 & 118893 & 156304.319148936 & -37411.3191489362 \tabularnewline
125 & 60493 & 82091.0526315789 & -21598.0526315789 \tabularnewline
126 & 19764 & 36422.8571428571 & -16658.8571428571 \tabularnewline
127 & 164062 & 156304.319148936 & 7757.68085106384 \tabularnewline
128 & 132696 & 123104.541666667 & 9591.45833333333 \tabularnewline
129 & 155367 & 156304.319148936 & -937.319148936163 \tabularnewline
130 & 11796 & 6214.69230769231 & 5581.30769230769 \tabularnewline
131 & 10674 & 6214.69230769231 & 4459.30769230769 \tabularnewline
132 & 142261 & 156304.319148936 & -14043.3191489362 \tabularnewline
133 & 6836 & 6214.69230769231 & 621.307692307692 \tabularnewline
134 & 154206 & 156304.319148936 & -2098.31914893616 \tabularnewline
135 & 5118 & 6214.69230769231 & -1096.69230769231 \tabularnewline
136 & 40248 & 36422.8571428571 & 3825.14285714286 \tabularnewline
137 & 0 & 6214.69230769231 & -6214.69230769231 \tabularnewline
138 & 122641 & 82091.0526315789 & 40549.9473684211 \tabularnewline
139 & 88837 & 123104.541666667 & -34267.5416666667 \tabularnewline
140 & 7131 & 6214.69230769231 & 916.307692307692 \tabularnewline
141 & 9056 & 6214.69230769231 & 2841.30769230769 \tabularnewline
142 & 76611 & 82091.0526315789 & -5480.05263157895 \tabularnewline
143 & 132697 & 123104.541666667 & 9592.45833333333 \tabularnewline
144 & 100681 & 123104.541666667 & -22423.5416666667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169238&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]158258[/C][C]156304.319148936[/C][C]1953.68085106384[/C][/ROW]
[ROW][C]2[/C][C]186930[/C][C]156304.319148936[/C][C]30625.6808510638[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]6214.69230769231[/C][C]1000.30769230769[/C][/ROW]
[ROW][C]4[/C][C]129098[/C][C]201818.826086957[/C][C]-72720.8260869565[/C][/ROW]
[ROW][C]5[/C][C]230587[/C][C]253685[/C][C]-23098[/C][/ROW]
[ROW][C]6[/C][C]508313[/C][C]253685[/C][C]254628[/C][/ROW]
[ROW][C]7[/C][C]180745[/C][C]156304.319148936[/C][C]24440.6808510638[/C][/ROW]
[ROW][C]8[/C][C]185559[/C][C]156304.319148936[/C][C]29254.6808510638[/C][/ROW]
[ROW][C]9[/C][C]154581[/C][C]156304.319148936[/C][C]-1723.31914893616[/C][/ROW]
[ROW][C]10[/C][C]290658[/C][C]253685[/C][C]36973[/C][/ROW]
[ROW][C]11[/C][C]121844[/C][C]156304.319148936[/C][C]-34460.3191489362[/C][/ROW]
[ROW][C]12[/C][C]184039[/C][C]201818.826086957[/C][C]-17779.8260869565[/C][/ROW]
[ROW][C]13[/C][C]100324[/C][C]156304.319148936[/C][C]-55980.3191489362[/C][/ROW]
[ROW][C]14[/C][C]209427[/C][C]253685[/C][C]-44258[/C][/ROW]
[ROW][C]15[/C][C]167592[/C][C]156304.319148936[/C][C]11287.6808510638[/C][/ROW]
[ROW][C]16[/C][C]154593[/C][C]156304.319148936[/C][C]-1711.31914893616[/C][/ROW]
[ROW][C]17[/C][C]142018[/C][C]156304.319148936[/C][C]-14286.3191489362[/C][/ROW]
[ROW][C]18[/C][C]77855[/C][C]123104.541666667[/C][C]-45249.5416666667[/C][/ROW]
[ROW][C]19[/C][C]167047[/C][C]253685[/C][C]-86638[/C][/ROW]
[ROW][C]20[/C][C]27997[/C][C]36422.8571428571[/C][C]-8425.85714285714[/C][/ROW]
[ROW][C]21[/C][C]73019[/C][C]82091.0526315789[/C][C]-9072.05263157895[/C][/ROW]
[ROW][C]22[/C][C]241082[/C][C]253685[/C][C]-12603[/C][/ROW]
[ROW][C]23[/C][C]195820[/C][C]156304.319148936[/C][C]39515.6808510638[/C][/ROW]
[ROW][C]24[/C][C]141899[/C][C]201818.826086957[/C][C]-59919.8260869565[/C][/ROW]
[ROW][C]25[/C][C]145433[/C][C]156304.319148936[/C][C]-10871.3191489362[/C][/ROW]
[ROW][C]26[/C][C]180241[/C][C]156304.319148936[/C][C]23936.6808510638[/C][/ROW]
[ROW][C]27[/C][C]202232[/C][C]156304.319148936[/C][C]45927.6808510638[/C][/ROW]
[ROW][C]28[/C][C]190230[/C][C]156304.319148936[/C][C]33925.6808510638[/C][/ROW]
[ROW][C]29[/C][C]354924[/C][C]253685[/C][C]101239[/C][/ROW]
[ROW][C]30[/C][C]192399[/C][C]201818.826086957[/C][C]-9419.82608695651[/C][/ROW]
[ROW][C]31[/C][C]182286[/C][C]156304.319148936[/C][C]25981.6808510638[/C][/ROW]
[ROW][C]32[/C][C]181590[/C][C]156304.319148936[/C][C]25285.6808510638[/C][/ROW]
[ROW][C]33[/C][C]133801[/C][C]201818.826086957[/C][C]-68017.8260869565[/C][/ROW]
[ROW][C]34[/C][C]233686[/C][C]253685[/C][C]-19999[/C][/ROW]
[ROW][C]35[/C][C]219428[/C][C]201818.826086957[/C][C]17609.1739130435[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]6214.69230769231[/C][C]-6214.69230769231[/C][/ROW]
[ROW][C]37[/C][C]223044[/C][C]201818.826086957[/C][C]21225.1739130435[/C][/ROW]
[ROW][C]38[/C][C]100129[/C][C]156304.319148936[/C][C]-56175.3191489362[/C][/ROW]
[ROW][C]39[/C][C]136733[/C][C]156304.319148936[/C][C]-19571.3191489362[/C][/ROW]
[ROW][C]40[/C][C]249965[/C][C]201818.826086957[/C][C]48146.1739130435[/C][/ROW]
[ROW][C]41[/C][C]242379[/C][C]201818.826086957[/C][C]40560.1739130435[/C][/ROW]
[ROW][C]42[/C][C]145794[/C][C]156304.319148936[/C][C]-10510.3191489362[/C][/ROW]
[ROW][C]43[/C][C]96404[/C][C]82091.0526315789[/C][C]14312.9473684211[/C][/ROW]
[ROW][C]44[/C][C]195891[/C][C]201818.826086957[/C][C]-5927.82608695651[/C][/ROW]
[ROW][C]45[/C][C]117156[/C][C]123104.541666667[/C][C]-5948.54166666667[/C][/ROW]
[ROW][C]46[/C][C]157787[/C][C]123104.541666667[/C][C]34682.4583333333[/C][/ROW]
[ROW][C]47[/C][C]81293[/C][C]82091.0526315789[/C][C]-798.052631578947[/C][/ROW]
[ROW][C]48[/C][C]224049[/C][C]201818.826086957[/C][C]22230.1739130435[/C][/ROW]
[ROW][C]49[/C][C]223789[/C][C]201818.826086957[/C][C]21970.1739130435[/C][/ROW]
[ROW][C]50[/C][C]160344[/C][C]201818.826086957[/C][C]-41474.8260869565[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]36422.8571428571[/C][C]11765.1428571429[/C][/ROW]
[ROW][C]52[/C][C]152206[/C][C]156304.319148936[/C][C]-4098.31914893616[/C][/ROW]
[ROW][C]53[/C][C]294283[/C][C]201818.826086957[/C][C]92464.1739130435[/C][/ROW]
[ROW][C]54[/C][C]235223[/C][C]156304.319148936[/C][C]78918.6808510638[/C][/ROW]
[ROW][C]55[/C][C]195583[/C][C]156304.319148936[/C][C]39278.6808510638[/C][/ROW]
[ROW][C]56[/C][C]145942[/C][C]156304.319148936[/C][C]-10362.3191489362[/C][/ROW]
[ROW][C]57[/C][C]208834[/C][C]156304.319148936[/C][C]52529.6808510638[/C][/ROW]
[ROW][C]58[/C][C]93764[/C][C]82091.0526315789[/C][C]11672.9473684211[/C][/ROW]
[ROW][C]59[/C][C]151985[/C][C]123104.541666667[/C][C]28880.4583333333[/C][/ROW]
[ROW][C]60[/C][C]190545[/C][C]253685[/C][C]-63140[/C][/ROW]
[ROW][C]61[/C][C]148922[/C][C]156304.319148936[/C][C]-7382.31914893616[/C][/ROW]
[ROW][C]62[/C][C]132856[/C][C]156304.319148936[/C][C]-23448.3191489362[/C][/ROW]
[ROW][C]63[/C][C]126107[/C][C]123104.541666667[/C][C]3002.45833333333[/C][/ROW]
[ROW][C]64[/C][C]112718[/C][C]123104.541666667[/C][C]-10386.5416666667[/C][/ROW]
[ROW][C]65[/C][C]160930[/C][C]123104.541666667[/C][C]37825.4583333333[/C][/ROW]
[ROW][C]66[/C][C]99184[/C][C]123104.541666667[/C][C]-23920.5416666667[/C][/ROW]
[ROW][C]67[/C][C]182022[/C][C]201818.826086957[/C][C]-19796.8260869565[/C][/ROW]
[ROW][C]68[/C][C]138708[/C][C]253685[/C][C]-114977[/C][/ROW]
[ROW][C]69[/C][C]114408[/C][C]123104.541666667[/C][C]-8696.54166666667[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]36422.8571428571[/C][C]-4452.85714285714[/C][/ROW]
[ROW][C]71[/C][C]225558[/C][C]253685[/C][C]-28127[/C][/ROW]
[ROW][C]72[/C][C]137011[/C][C]123104.541666667[/C][C]13906.4583333333[/C][/ROW]
[ROW][C]73[/C][C]113612[/C][C]156304.319148936[/C][C]-42692.3191489362[/C][/ROW]
[ROW][C]74[/C][C]108641[/C][C]156304.319148936[/C][C]-47663.3191489362[/C][/ROW]
[ROW][C]75[/C][C]162203[/C][C]201818.826086957[/C][C]-39615.8260869565[/C][/ROW]
[ROW][C]76[/C][C]100098[/C][C]156304.319148936[/C][C]-56206.3191489362[/C][/ROW]
[ROW][C]77[/C][C]174768[/C][C]201818.826086957[/C][C]-27050.8260869565[/C][/ROW]
[ROW][C]78[/C][C]158459[/C][C]201818.826086957[/C][C]-43359.8260869565[/C][/ROW]
[ROW][C]79[/C][C]80934[/C][C]82091.0526315789[/C][C]-1157.05263157895[/C][/ROW]
[ROW][C]80[/C][C]84971[/C][C]123104.541666667[/C][C]-38133.5416666667[/C][/ROW]
[ROW][C]81[/C][C]80545[/C][C]82091.0526315789[/C][C]-1546.05263157895[/C][/ROW]
[ROW][C]82[/C][C]287191[/C][C]201818.826086957[/C][C]85372.1739130435[/C][/ROW]
[ROW][C]83[/C][C]62974[/C][C]82091.0526315789[/C][C]-19117.0526315789[/C][/ROW]
[ROW][C]84[/C][C]130982[/C][C]156304.319148936[/C][C]-25322.3191489362[/C][/ROW]
[ROW][C]85[/C][C]75555[/C][C]82091.0526315789[/C][C]-6536.05263157895[/C][/ROW]
[ROW][C]86[/C][C]162154[/C][C]156304.319148936[/C][C]5849.68085106384[/C][/ROW]
[ROW][C]87[/C][C]226638[/C][C]201818.826086957[/C][C]24819.1739130435[/C][/ROW]
[ROW][C]88[/C][C]115019[/C][C]123104.541666667[/C][C]-8085.54166666667[/C][/ROW]
[ROW][C]89[/C][C]105038[/C][C]123104.541666667[/C][C]-18066.5416666667[/C][/ROW]
[ROW][C]90[/C][C]155537[/C][C]156304.319148936[/C][C]-767.319148936163[/C][/ROW]
[ROW][C]91[/C][C]153133[/C][C]156304.319148936[/C][C]-3171.31914893616[/C][/ROW]
[ROW][C]92[/C][C]165577[/C][C]201818.826086957[/C][C]-36241.8260869565[/C][/ROW]
[ROW][C]93[/C][C]151517[/C][C]123104.541666667[/C][C]28412.4583333333[/C][/ROW]
[ROW][C]94[/C][C]133686[/C][C]123104.541666667[/C][C]10581.4583333333[/C][/ROW]
[ROW][C]95[/C][C]58128[/C][C]82091.0526315789[/C][C]-23963.0526315789[/C][/ROW]
[ROW][C]96[/C][C]245196[/C][C]201818.826086957[/C][C]43377.1739130435[/C][/ROW]
[ROW][C]97[/C][C]195576[/C][C]156304.319148936[/C][C]39271.6808510638[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]6214.69230769231[/C][C]13134.3076923077[/C][/ROW]
[ROW][C]99[/C][C]225371[/C][C]201818.826086957[/C][C]23552.1739130435[/C][/ROW]
[ROW][C]100[/C][C]152796[/C][C]156304.319148936[/C][C]-3508.31914893616[/C][/ROW]
[ROW][C]101[/C][C]59117[/C][C]36422.8571428571[/C][C]22694.1428571429[/C][/ROW]
[ROW][C]102[/C][C]91762[/C][C]82091.0526315789[/C][C]9670.94736842105[/C][/ROW]
[ROW][C]103[/C][C]127987[/C][C]123104.541666667[/C][C]4882.45833333333[/C][/ROW]
[ROW][C]104[/C][C]113552[/C][C]123104.541666667[/C][C]-9552.54166666667[/C][/ROW]
[ROW][C]105[/C][C]85338[/C][C]82091.0526315789[/C][C]3246.94736842105[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]36422.8571428571[/C][C]-8746.85714285714[/C][/ROW]
[ROW][C]107[/C][C]147984[/C][C]156304.319148936[/C][C]-8320.31914893616[/C][/ROW]
[ROW][C]108[/C][C]122417[/C][C]123104.541666667[/C][C]-687.541666666672[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]6214.69230769231[/C][C]-6214.69230769231[/C][/ROW]
[ROW][C]110[/C][C]91529[/C][C]82091.0526315789[/C][C]9437.94736842105[/C][/ROW]
[ROW][C]111[/C][C]107205[/C][C]123104.541666667[/C][C]-15899.5416666667[/C][/ROW]
[ROW][C]112[/C][C]144664[/C][C]156304.319148936[/C][C]-11640.3191489362[/C][/ROW]
[ROW][C]113[/C][C]136540[/C][C]156304.319148936[/C][C]-19764.3191489362[/C][/ROW]
[ROW][C]114[/C][C]76656[/C][C]82091.0526315789[/C][C]-5435.05263157895[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]6214.69230769231[/C][C]-2598.69230769231[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]6214.69230769231[/C][C]-6214.69230769231[/C][/ROW]
[ROW][C]117[/C][C]183065[/C][C]123104.541666667[/C][C]59960.4583333333[/C][/ROW]
[ROW][C]118[/C][C]144636[/C][C]156304.319148936[/C][C]-11668.3191489362[/C][/ROW]
[ROW][C]119[/C][C]156889[/C][C]156304.319148936[/C][C]584.680851063837[/C][/ROW]
[ROW][C]120[/C][C]113273[/C][C]82091.0526315789[/C][C]31181.9473684211[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]82091.0526315789[/C][C]-38681.0526315789[/C][/ROW]
[ROW][C]122[/C][C]175774[/C][C]156304.319148936[/C][C]19469.6808510638[/C][/ROW]
[ROW][C]123[/C][C]95401[/C][C]82091.0526315789[/C][C]13309.9473684211[/C][/ROW]
[ROW][C]124[/C][C]118893[/C][C]156304.319148936[/C][C]-37411.3191489362[/C][/ROW]
[ROW][C]125[/C][C]60493[/C][C]82091.0526315789[/C][C]-21598.0526315789[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]36422.8571428571[/C][C]-16658.8571428571[/C][/ROW]
[ROW][C]127[/C][C]164062[/C][C]156304.319148936[/C][C]7757.68085106384[/C][/ROW]
[ROW][C]128[/C][C]132696[/C][C]123104.541666667[/C][C]9591.45833333333[/C][/ROW]
[ROW][C]129[/C][C]155367[/C][C]156304.319148936[/C][C]-937.319148936163[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]6214.69230769231[/C][C]5581.30769230769[/C][/ROW]
[ROW][C]131[/C][C]10674[/C][C]6214.69230769231[/C][C]4459.30769230769[/C][/ROW]
[ROW][C]132[/C][C]142261[/C][C]156304.319148936[/C][C]-14043.3191489362[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]6214.69230769231[/C][C]621.307692307692[/C][/ROW]
[ROW][C]134[/C][C]154206[/C][C]156304.319148936[/C][C]-2098.31914893616[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]6214.69230769231[/C][C]-1096.69230769231[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]36422.8571428571[/C][C]3825.14285714286[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]6214.69230769231[/C][C]-6214.69230769231[/C][/ROW]
[ROW][C]138[/C][C]122641[/C][C]82091.0526315789[/C][C]40549.9473684211[/C][/ROW]
[ROW][C]139[/C][C]88837[/C][C]123104.541666667[/C][C]-34267.5416666667[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]6214.69230769231[/C][C]916.307692307692[/C][/ROW]
[ROW][C]141[/C][C]9056[/C][C]6214.69230769231[/C][C]2841.30769230769[/C][/ROW]
[ROW][C]142[/C][C]76611[/C][C]82091.0526315789[/C][C]-5480.05263157895[/C][/ROW]
[ROW][C]143[/C][C]132697[/C][C]123104.541666667[/C][C]9592.45833333333[/C][/ROW]
[ROW][C]144[/C][C]100681[/C][C]123104.541666667[/C][C]-22423.5416666667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=169238&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169238&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
1158258156304.3191489361953.68085106384
2186930156304.31914893630625.6808510638
372156214.692307692311000.30769230769
4129098201818.826086957-72720.8260869565
5230587253685-23098
6508313253685254628
7180745156304.31914893624440.6808510638
8185559156304.31914893629254.6808510638
9154581156304.319148936-1723.31914893616
1029065825368536973
11121844156304.319148936-34460.3191489362
12184039201818.826086957-17779.8260869565
13100324156304.319148936-55980.3191489362
14209427253685-44258
15167592156304.31914893611287.6808510638
16154593156304.319148936-1711.31914893616
17142018156304.319148936-14286.3191489362
1877855123104.541666667-45249.5416666667
19167047253685-86638
202799736422.8571428571-8425.85714285714
217301982091.0526315789-9072.05263157895
22241082253685-12603
23195820156304.31914893639515.6808510638
24141899201818.826086957-59919.8260869565
25145433156304.319148936-10871.3191489362
26180241156304.31914893623936.6808510638
27202232156304.31914893645927.6808510638
28190230156304.31914893633925.6808510638
29354924253685101239
30192399201818.826086957-9419.82608695651
31182286156304.31914893625981.6808510638
32181590156304.31914893625285.6808510638
33133801201818.826086957-68017.8260869565
34233686253685-19999
35219428201818.82608695717609.1739130435
3606214.69230769231-6214.69230769231
37223044201818.82608695721225.1739130435
38100129156304.319148936-56175.3191489362
39136733156304.319148936-19571.3191489362
40249965201818.82608695748146.1739130435
41242379201818.82608695740560.1739130435
42145794156304.319148936-10510.3191489362
439640482091.052631578914312.9473684211
44195891201818.826086957-5927.82608695651
45117156123104.541666667-5948.54166666667
46157787123104.54166666734682.4583333333
478129382091.0526315789-798.052631578947
48224049201818.82608695722230.1739130435
49223789201818.82608695721970.1739130435
50160344201818.826086957-41474.8260869565
514818836422.857142857111765.1428571429
52152206156304.319148936-4098.31914893616
53294283201818.82608695792464.1739130435
54235223156304.31914893678918.6808510638
55195583156304.31914893639278.6808510638
56145942156304.319148936-10362.3191489362
57208834156304.31914893652529.6808510638
589376482091.052631578911672.9473684211
59151985123104.54166666728880.4583333333
60190545253685-63140
61148922156304.319148936-7382.31914893616
62132856156304.319148936-23448.3191489362
63126107123104.5416666673002.45833333333
64112718123104.541666667-10386.5416666667
65160930123104.54166666737825.4583333333
6699184123104.541666667-23920.5416666667
67182022201818.826086957-19796.8260869565
68138708253685-114977
69114408123104.541666667-8696.54166666667
703197036422.8571428571-4452.85714285714
71225558253685-28127
72137011123104.54166666713906.4583333333
73113612156304.319148936-42692.3191489362
74108641156304.319148936-47663.3191489362
75162203201818.826086957-39615.8260869565
76100098156304.319148936-56206.3191489362
77174768201818.826086957-27050.8260869565
78158459201818.826086957-43359.8260869565
798093482091.0526315789-1157.05263157895
8084971123104.541666667-38133.5416666667
818054582091.0526315789-1546.05263157895
82287191201818.82608695785372.1739130435
836297482091.0526315789-19117.0526315789
84130982156304.319148936-25322.3191489362
857555582091.0526315789-6536.05263157895
86162154156304.3191489365849.68085106384
87226638201818.82608695724819.1739130435
88115019123104.541666667-8085.54166666667
89105038123104.541666667-18066.5416666667
90155537156304.319148936-767.319148936163
91153133156304.319148936-3171.31914893616
92165577201818.826086957-36241.8260869565
93151517123104.54166666728412.4583333333
94133686123104.54166666710581.4583333333
955812882091.0526315789-23963.0526315789
96245196201818.82608695743377.1739130435
97195576156304.31914893639271.6808510638
98193496214.6923076923113134.3076923077
99225371201818.82608695723552.1739130435
100152796156304.319148936-3508.31914893616
1015911736422.857142857122694.1428571429
1029176282091.05263157899670.94736842105
103127987123104.5416666674882.45833333333
104113552123104.541666667-9552.54166666667
1058533882091.05263157893246.94736842105
1062767636422.8571428571-8746.85714285714
107147984156304.319148936-8320.31914893616
108122417123104.541666667-687.541666666672
10906214.69230769231-6214.69230769231
1109152982091.05263157899437.94736842105
111107205123104.541666667-15899.5416666667
112144664156304.319148936-11640.3191489362
113136540156304.319148936-19764.3191489362
1147665682091.0526315789-5435.05263157895
11536166214.69230769231-2598.69230769231
11606214.69230769231-6214.69230769231
117183065123104.54166666759960.4583333333
118144636156304.319148936-11668.3191489362
119156889156304.319148936584.680851063837
12011327382091.052631578931181.9473684211
1214341082091.0526315789-38681.0526315789
122175774156304.31914893619469.6808510638
1239540182091.052631578913309.9473684211
124118893156304.319148936-37411.3191489362
1256049382091.0526315789-21598.0526315789
1261976436422.8571428571-16658.8571428571
127164062156304.3191489367757.68085106384
128132696123104.5416666679591.45833333333
129155367156304.319148936-937.319148936163
130117966214.692307692315581.30769230769
131106746214.692307692314459.30769230769
132142261156304.319148936-14043.3191489362
13368366214.69230769231621.307692307692
134154206156304.319148936-2098.31914893616
13551186214.69230769231-1096.69230769231
1364024836422.85714285713825.14285714286
13706214.69230769231-6214.69230769231
13812264182091.052631578940549.9473684211
13988837123104.541666667-34267.5416666667
14071316214.69230769231916.307692307692
14190566214.692307692312841.30769230769
1427661182091.0526315789-5480.05263157895
143132697123104.5416666679592.45833333333
144100681123104.541666667-22423.5416666667


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 2 ; 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')
}