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*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Sun, 12 Aug 2012 09:11:35 -0400

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Aug/12/t1344777112xtfjjvrjlz9c59n.htm/, Retrieved Sat, 27 Apr 2024 16:01:06 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=169236, Retrieved Sat, 27 Apr 2024 16:01:06 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

159

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] [0b94335bf72158573fe52322b9537409] [Current]
-   P             [Recursive Partitioning (Regression Trees)] [Berekening 19] [2012-08-12 13:13:25] [eb6e95800005ec22b7fd76eead8d8a59] 

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Dataseries X:

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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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169236&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=169236&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169236&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Goodness of Fit
Correlation	0.8444
R-squared	0.713
RMSE	467.2189

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8444 \tabularnewline
R-squared & 0.713 \tabularnewline
RMSE & 467.2189 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169236&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8444[/C][/ROW]
[ROW][C]R-squared[/C][C]0.713[/C][/ROW]
[ROW][C]RMSE[/C][C]467.2189[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=169236&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169236&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
Correlation	0.8444
R-squared	0.713
RMSE	467.2189

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1772	1962.56756756757	-190.567567567567
2	1703	1617.73076923077	85.2692307692307
3	192	208.222222222222	-16.2222222222222
4	2294	1962.56756756757	331.432432432433
5	3448	2582.15384615385	865.846153846154
6	6813	3086.85714285714	3726.14285714286
7	1795	1617.73076923077	177.269230769231
8	1680	1962.56756756757	-282.567567567567
9	1896	1617.73076923077	278.269230769231
10	2917	3086.85714285714	-169.857142857143
11	1946	1353.42857142857	592.571428571429
12	2148	1962.56756756757	185.432432432433
13	1832	1353.42857142857	478.571428571429
14	3059	2582.15384615385	476.846153846154
15	1469	1617.73076923077	-148.730769230769
16	1565	1962.56756756757	-397.567567567567
17	1755	1617.73076923077	137.269230769231
18	1234	1009.73333333333	224.266666666667
19	2779	1962.56756756757	816.432432432433
20	726	208.222222222222	517.777777777778
21	1048	824.428571428571	223.571428571429
22	2804	2582.15384615385	221.846153846154
23	1760	1962.56756756757	-202.567567567567
24	2261	1617.73076923077	643.269230769231
25	1848	1962.56756756757	-114.567567567567
26	1647	1617.73076923077	29.2692307692307
27	2081	1962.56756756757	118.432432432433
28	1393	1962.56756756757	-569.567567567567
29	2741	3086.85714285714	-345.857142857143
30	2112	1617.73076923077	494.269230769231
31	1684	1617.73076923077	66.2692307692307
32	1616	1617.73076923077	-1.73076923076928
33	2227	1962.56756756757	264.432432432433
34	3088	2582.15384615385	505.846153846154
35	2388	2582.15384615385	-194.153846153846
36	1	208.222222222222	-207.222222222222
37	2099	2582.15384615385	-483.153846153846
38	1669	1353.42857142857	315.571428571429
39	2094	1617.73076923077	476.269230769231
40	2153	3086.85714285714	-933.857142857143
41	2390	2582.15384615385	-192.153846153846
42	1701	1962.56756756757	-261.567567567567
43	983	1009.73333333333	-26.7333333333333
44	2161	1962.56756756757	198.432432432433
45	1276	1353.42857142857	-77.4285714285713
46	1189	1962.56756756757	-773.567567567567
47	744	1009.73333333333	-265.733333333333
48	2231	2582.15384615385	-351.153846153846
49	2242	2582.15384615385	-340.153846153846
50	2638	1962.56756756757	675.432432432433
51	658	824.428571428571	-166.428571428571
52	1859	1962.56756756757	-103.567567567567
53	2489	3086.85714285714	-597.857142857143
54	2025	2582.15384615385	-557.153846153846
55	1911	1962.56756756757	-51.5675675675675
56	1714	1617.73076923077	96.2692307692307
57	1851	1962.56756756757	-111.567567567567
58	980	1009.73333333333	-29.7333333333333
59	1177	1962.56756756757	-785.567567567567
60	2809	1962.56756756757	846.432432432433
61	1688	1962.56756756757	-274.567567567567
62	2097	1962.56756756757	134.432432432433
63	1309	1353.42857142857	-44.4285714285713
64	1243	1353.42857142857	-110.428571428571
65	1255	1617.73076923077	-362.730769230769
66	1293	1353.42857142857	-60.4285714285713
67	2259	1962.56756756757	296.432432432433
68	2897	1962.56756756757	934.432432432433
69	1103	1353.42857142857	-250.428571428571
70	340	208.222222222222	131.777777777778
71	2791	2582.15384615385	208.846153846154
72	1333	1962.56756756757	-629.567567567567
73	1441	1353.42857142857	87.5714285714287
74	1622	1353.42857142857	268.571428571429
75	2649	1962.56756756757	686.432432432433
76	1499	1353.42857142857	145.571428571429
77	2302	1962.56756756757	339.432432432433
78	2540	1962.56756756757	577.432432432433
79	1000	1009.73333333333	-9.73333333333335
80	1234	1009.73333333333	224.266666666667
81	927	1009.73333333333	-82.7333333333333
82	2176	3086.85714285714	-910.857142857143
83	956	824.428571428571	131.571428571429
84	1531	1617.73076923077	-86.7307692307693
85	1013	1009.73333333333	3.26666666666665
86	1771	1617.73076923077	153.269230769231
87	2613	2582.15384615385	30.8461538461538
88	1203	1353.42857142857	-150.428571428571
89	1303	1353.42857142857	-50.4285714285713
90	1524	1617.73076923077	-93.7307692307693
91	1829	1617.73076923077	211.269230769231
92	2227	1962.56756756757	264.432432432433
93	1233	1617.73076923077	-384.730769230769
94	1365	1962.56756756757	-597.567567567567
95	901	824.428571428571	76.5714285714286
96	2319	3086.85714285714	-767.857142857143
97	1857	1962.56756756757	-105.567567567567
98	223	208.222222222222	14.7777777777778
99	2390	2582.15384615385	-192.153846153846
100	1973	1962.56756756757	10.4324324324325
101	699	824.428571428571	-125.428571428571
102	1062	1009.73333333333	52.2666666666667
103	1252	1353.42857142857	-101.428571428571
104	1154	1353.42857142857	-199.428571428571
105	823	1009.73333333333	-186.733333333333
106	596	208.222222222222	387.777777777778
107	1471	1617.73076923077	-146.730769230769
108	1130	1353.42857142857	-223.428571428571
109	0	208.222222222222	-208.222222222222
110	1082	1009.73333333333	72.2666666666667
111	1134	1353.42857142857	-219.428571428571
112	1366	1617.73076923077	-251.730769230769
113	1452	1962.56756756757	-510.567567567567
114	869	1009.73333333333	-140.733333333333
115	78	208.222222222222	-130.222222222222
116	0	208.222222222222	-208.222222222222
117	1127	1617.73076923077	-490.730769230769
118	1578	1962.56756756757	-384.567567567567
119	2018	1962.56756756757	55.4324324324325
120	919	1353.42857142857	-434.428571428571
121	778	824.428571428571	-46.4285714285714
122	1751	1962.56756756757	-211.567567567567
123	956	1009.73333333333	-53.7333333333333
124	1875	1353.42857142857	521.571428571429
125	731	824.428571428571	-93.4285714285714
126	285	208.222222222222	76.7777777777778
127	1833	1617.73076923077	215.269230769231
128	1147	1617.73076923077	-470.730769230769
129	1646	1617.73076923077	28.2692307692307
130	256	208.222222222222	47.7777777777778
131	98	208.222222222222	-110.222222222222
132	1403	1617.73076923077	-214.730769230769
133	41	208.222222222222	-167.222222222222
134	1786	1962.56756756757	-176.567567567567
135	42	208.222222222222	-166.222222222222
136	528	208.222222222222	319.777777777778
137	0	208.222222222222	-208.222222222222
138	1072	1353.42857142857	-281.428571428571
139	1305	1009.73333333333	295.266666666667
140	81	208.222222222222	-127.222222222222
141	261	208.222222222222	52.7777777777778
142	934	1009.73333333333	-75.7333333333333
143	1179	1617.73076923077	-438.730769230769
144	1147	1353.42857142857	-206.428571428571

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1772 & 1962.56756756757 & -190.567567567567 \tabularnewline
2 & 1703 & 1617.73076923077 & 85.2692307692307 \tabularnewline
3 & 192 & 208.222222222222 & -16.2222222222222 \tabularnewline
4 & 2294 & 1962.56756756757 & 331.432432432433 \tabularnewline
5 & 3448 & 2582.15384615385 & 865.846153846154 \tabularnewline
6 & 6813 & 3086.85714285714 & 3726.14285714286 \tabularnewline
7 & 1795 & 1617.73076923077 & 177.269230769231 \tabularnewline
8 & 1680 & 1962.56756756757 & -282.567567567567 \tabularnewline
9 & 1896 & 1617.73076923077 & 278.269230769231 \tabularnewline
10 & 2917 & 3086.85714285714 & -169.857142857143 \tabularnewline
11 & 1946 & 1353.42857142857 & 592.571428571429 \tabularnewline
12 & 2148 & 1962.56756756757 & 185.432432432433 \tabularnewline
13 & 1832 & 1353.42857142857 & 478.571428571429 \tabularnewline
14 & 3059 & 2582.15384615385 & 476.846153846154 \tabularnewline
15 & 1469 & 1617.73076923077 & -148.730769230769 \tabularnewline
16 & 1565 & 1962.56756756757 & -397.567567567567 \tabularnewline
17 & 1755 & 1617.73076923077 & 137.269230769231 \tabularnewline
18 & 1234 & 1009.73333333333 & 224.266666666667 \tabularnewline
19 & 2779 & 1962.56756756757 & 816.432432432433 \tabularnewline
20 & 726 & 208.222222222222 & 517.777777777778 \tabularnewline
21 & 1048 & 824.428571428571 & 223.571428571429 \tabularnewline
22 & 2804 & 2582.15384615385 & 221.846153846154 \tabularnewline
23 & 1760 & 1962.56756756757 & -202.567567567567 \tabularnewline
24 & 2261 & 1617.73076923077 & 643.269230769231 \tabularnewline
25 & 1848 & 1962.56756756757 & -114.567567567567 \tabularnewline
26 & 1647 & 1617.73076923077 & 29.2692307692307 \tabularnewline
27 & 2081 & 1962.56756756757 & 118.432432432433 \tabularnewline
28 & 1393 & 1962.56756756757 & -569.567567567567 \tabularnewline
29 & 2741 & 3086.85714285714 & -345.857142857143 \tabularnewline
30 & 2112 & 1617.73076923077 & 494.269230769231 \tabularnewline
31 & 1684 & 1617.73076923077 & 66.2692307692307 \tabularnewline
32 & 1616 & 1617.73076923077 & -1.73076923076928 \tabularnewline
33 & 2227 & 1962.56756756757 & 264.432432432433 \tabularnewline
34 & 3088 & 2582.15384615385 & 505.846153846154 \tabularnewline
35 & 2388 & 2582.15384615385 & -194.153846153846 \tabularnewline
36 & 1 & 208.222222222222 & -207.222222222222 \tabularnewline
37 & 2099 & 2582.15384615385 & -483.153846153846 \tabularnewline
38 & 1669 & 1353.42857142857 & 315.571428571429 \tabularnewline
39 & 2094 & 1617.73076923077 & 476.269230769231 \tabularnewline
40 & 2153 & 3086.85714285714 & -933.857142857143 \tabularnewline
41 & 2390 & 2582.15384615385 & -192.153846153846 \tabularnewline
42 & 1701 & 1962.56756756757 & -261.567567567567 \tabularnewline
43 & 983 & 1009.73333333333 & -26.7333333333333 \tabularnewline
44 & 2161 & 1962.56756756757 & 198.432432432433 \tabularnewline
45 & 1276 & 1353.42857142857 & -77.4285714285713 \tabularnewline
46 & 1189 & 1962.56756756757 & -773.567567567567 \tabularnewline
47 & 744 & 1009.73333333333 & -265.733333333333 \tabularnewline
48 & 2231 & 2582.15384615385 & -351.153846153846 \tabularnewline
49 & 2242 & 2582.15384615385 & -340.153846153846 \tabularnewline
50 & 2638 & 1962.56756756757 & 675.432432432433 \tabularnewline
51 & 658 & 824.428571428571 & -166.428571428571 \tabularnewline
52 & 1859 & 1962.56756756757 & -103.567567567567 \tabularnewline
53 & 2489 & 3086.85714285714 & -597.857142857143 \tabularnewline
54 & 2025 & 2582.15384615385 & -557.153846153846 \tabularnewline
55 & 1911 & 1962.56756756757 & -51.5675675675675 \tabularnewline
56 & 1714 & 1617.73076923077 & 96.2692307692307 \tabularnewline
57 & 1851 & 1962.56756756757 & -111.567567567567 \tabularnewline
58 & 980 & 1009.73333333333 & -29.7333333333333 \tabularnewline
59 & 1177 & 1962.56756756757 & -785.567567567567 \tabularnewline
60 & 2809 & 1962.56756756757 & 846.432432432433 \tabularnewline
61 & 1688 & 1962.56756756757 & -274.567567567567 \tabularnewline
62 & 2097 & 1962.56756756757 & 134.432432432433 \tabularnewline
63 & 1309 & 1353.42857142857 & -44.4285714285713 \tabularnewline
64 & 1243 & 1353.42857142857 & -110.428571428571 \tabularnewline
65 & 1255 & 1617.73076923077 & -362.730769230769 \tabularnewline
66 & 1293 & 1353.42857142857 & -60.4285714285713 \tabularnewline
67 & 2259 & 1962.56756756757 & 296.432432432433 \tabularnewline
68 & 2897 & 1962.56756756757 & 934.432432432433 \tabularnewline
69 & 1103 & 1353.42857142857 & -250.428571428571 \tabularnewline
70 & 340 & 208.222222222222 & 131.777777777778 \tabularnewline
71 & 2791 & 2582.15384615385 & 208.846153846154 \tabularnewline
72 & 1333 & 1962.56756756757 & -629.567567567567 \tabularnewline
73 & 1441 & 1353.42857142857 & 87.5714285714287 \tabularnewline
74 & 1622 & 1353.42857142857 & 268.571428571429 \tabularnewline
75 & 2649 & 1962.56756756757 & 686.432432432433 \tabularnewline
76 & 1499 & 1353.42857142857 & 145.571428571429 \tabularnewline
77 & 2302 & 1962.56756756757 & 339.432432432433 \tabularnewline
78 & 2540 & 1962.56756756757 & 577.432432432433 \tabularnewline
79 & 1000 & 1009.73333333333 & -9.73333333333335 \tabularnewline
80 & 1234 & 1009.73333333333 & 224.266666666667 \tabularnewline
81 & 927 & 1009.73333333333 & -82.7333333333333 \tabularnewline
82 & 2176 & 3086.85714285714 & -910.857142857143 \tabularnewline
83 & 956 & 824.428571428571 & 131.571428571429 \tabularnewline
84 & 1531 & 1617.73076923077 & -86.7307692307693 \tabularnewline
85 & 1013 & 1009.73333333333 & 3.26666666666665 \tabularnewline
86 & 1771 & 1617.73076923077 & 153.269230769231 \tabularnewline
87 & 2613 & 2582.15384615385 & 30.8461538461538 \tabularnewline
88 & 1203 & 1353.42857142857 & -150.428571428571 \tabularnewline
89 & 1303 & 1353.42857142857 & -50.4285714285713 \tabularnewline
90 & 1524 & 1617.73076923077 & -93.7307692307693 \tabularnewline
91 & 1829 & 1617.73076923077 & 211.269230769231 \tabularnewline
92 & 2227 & 1962.56756756757 & 264.432432432433 \tabularnewline
93 & 1233 & 1617.73076923077 & -384.730769230769 \tabularnewline
94 & 1365 & 1962.56756756757 & -597.567567567567 \tabularnewline
95 & 901 & 824.428571428571 & 76.5714285714286 \tabularnewline
96 & 2319 & 3086.85714285714 & -767.857142857143 \tabularnewline
97 & 1857 & 1962.56756756757 & -105.567567567567 \tabularnewline
98 & 223 & 208.222222222222 & 14.7777777777778 \tabularnewline
99 & 2390 & 2582.15384615385 & -192.153846153846 \tabularnewline
100 & 1973 & 1962.56756756757 & 10.4324324324325 \tabularnewline
101 & 699 & 824.428571428571 & -125.428571428571 \tabularnewline
102 & 1062 & 1009.73333333333 & 52.2666666666667 \tabularnewline
103 & 1252 & 1353.42857142857 & -101.428571428571 \tabularnewline
104 & 1154 & 1353.42857142857 & -199.428571428571 \tabularnewline
105 & 823 & 1009.73333333333 & -186.733333333333 \tabularnewline
106 & 596 & 208.222222222222 & 387.777777777778 \tabularnewline
107 & 1471 & 1617.73076923077 & -146.730769230769 \tabularnewline
108 & 1130 & 1353.42857142857 & -223.428571428571 \tabularnewline
109 & 0 & 208.222222222222 & -208.222222222222 \tabularnewline
110 & 1082 & 1009.73333333333 & 72.2666666666667 \tabularnewline
111 & 1134 & 1353.42857142857 & -219.428571428571 \tabularnewline
112 & 1366 & 1617.73076923077 & -251.730769230769 \tabularnewline
113 & 1452 & 1962.56756756757 & -510.567567567567 \tabularnewline
114 & 869 & 1009.73333333333 & -140.733333333333 \tabularnewline
115 & 78 & 208.222222222222 & -130.222222222222 \tabularnewline
116 & 0 & 208.222222222222 & -208.222222222222 \tabularnewline
117 & 1127 & 1617.73076923077 & -490.730769230769 \tabularnewline
118 & 1578 & 1962.56756756757 & -384.567567567567 \tabularnewline
119 & 2018 & 1962.56756756757 & 55.4324324324325 \tabularnewline
120 & 919 & 1353.42857142857 & -434.428571428571 \tabularnewline
121 & 778 & 824.428571428571 & -46.4285714285714 \tabularnewline
122 & 1751 & 1962.56756756757 & -211.567567567567 \tabularnewline
123 & 956 & 1009.73333333333 & -53.7333333333333 \tabularnewline
124 & 1875 & 1353.42857142857 & 521.571428571429 \tabularnewline
125 & 731 & 824.428571428571 & -93.4285714285714 \tabularnewline
126 & 285 & 208.222222222222 & 76.7777777777778 \tabularnewline
127 & 1833 & 1617.73076923077 & 215.269230769231 \tabularnewline
128 & 1147 & 1617.73076923077 & -470.730769230769 \tabularnewline
129 & 1646 & 1617.73076923077 & 28.2692307692307 \tabularnewline
130 & 256 & 208.222222222222 & 47.7777777777778 \tabularnewline
131 & 98 & 208.222222222222 & -110.222222222222 \tabularnewline
132 & 1403 & 1617.73076923077 & -214.730769230769 \tabularnewline
133 & 41 & 208.222222222222 & -167.222222222222 \tabularnewline
134 & 1786 & 1962.56756756757 & -176.567567567567 \tabularnewline
135 & 42 & 208.222222222222 & -166.222222222222 \tabularnewline
136 & 528 & 208.222222222222 & 319.777777777778 \tabularnewline
137 & 0 & 208.222222222222 & -208.222222222222 \tabularnewline
138 & 1072 & 1353.42857142857 & -281.428571428571 \tabularnewline
139 & 1305 & 1009.73333333333 & 295.266666666667 \tabularnewline
140 & 81 & 208.222222222222 & -127.222222222222 \tabularnewline
141 & 261 & 208.222222222222 & 52.7777777777778 \tabularnewline
142 & 934 & 1009.73333333333 & -75.7333333333333 \tabularnewline
143 & 1179 & 1617.73076923077 & -438.730769230769 \tabularnewline
144 & 1147 & 1353.42857142857 & -206.428571428571 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169236&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]1772[/C][C]1962.56756756757[/C][C]-190.567567567567[/C][/ROW]
[ROW][C]2[/C][C]1703[/C][C]1617.73076923077[/C][C]85.2692307692307[/C][/ROW]
[ROW][C]3[/C][C]192[/C][C]208.222222222222[/C][C]-16.2222222222222[/C][/ROW]
[ROW][C]4[/C][C]2294[/C][C]1962.56756756757[/C][C]331.432432432433[/C][/ROW]
[ROW][C]5[/C][C]3448[/C][C]2582.15384615385[/C][C]865.846153846154[/C][/ROW]
[ROW][C]6[/C][C]6813[/C][C]3086.85714285714[/C][C]3726.14285714286[/C][/ROW]
[ROW][C]7[/C][C]1795[/C][C]1617.73076923077[/C][C]177.269230769231[/C][/ROW]
[ROW][C]8[/C][C]1680[/C][C]1962.56756756757[/C][C]-282.567567567567[/C][/ROW]
[ROW][C]9[/C][C]1896[/C][C]1617.73076923077[/C][C]278.269230769231[/C][/ROW]
[ROW][C]10[/C][C]2917[/C][C]3086.85714285714[/C][C]-169.857142857143[/C][/ROW]
[ROW][C]11[/C][C]1946[/C][C]1353.42857142857[/C][C]592.571428571429[/C][/ROW]
[ROW][C]12[/C][C]2148[/C][C]1962.56756756757[/C][C]185.432432432433[/C][/ROW]
[ROW][C]13[/C][C]1832[/C][C]1353.42857142857[/C][C]478.571428571429[/C][/ROW]
[ROW][C]14[/C][C]3059[/C][C]2582.15384615385[/C][C]476.846153846154[/C][/ROW]
[ROW][C]15[/C][C]1469[/C][C]1617.73076923077[/C][C]-148.730769230769[/C][/ROW]
[ROW][C]16[/C][C]1565[/C][C]1962.56756756757[/C][C]-397.567567567567[/C][/ROW]
[ROW][C]17[/C][C]1755[/C][C]1617.73076923077[/C][C]137.269230769231[/C][/ROW]
[ROW][C]18[/C][C]1234[/C][C]1009.73333333333[/C][C]224.266666666667[/C][/ROW]
[ROW][C]19[/C][C]2779[/C][C]1962.56756756757[/C][C]816.432432432433[/C][/ROW]
[ROW][C]20[/C][C]726[/C][C]208.222222222222[/C][C]517.777777777778[/C][/ROW]
[ROW][C]21[/C][C]1048[/C][C]824.428571428571[/C][C]223.571428571429[/C][/ROW]
[ROW][C]22[/C][C]2804[/C][C]2582.15384615385[/C][C]221.846153846154[/C][/ROW]
[ROW][C]23[/C][C]1760[/C][C]1962.56756756757[/C][C]-202.567567567567[/C][/ROW]
[ROW][C]24[/C][C]2261[/C][C]1617.73076923077[/C][C]643.269230769231[/C][/ROW]
[ROW][C]25[/C][C]1848[/C][C]1962.56756756757[/C][C]-114.567567567567[/C][/ROW]
[ROW][C]26[/C][C]1647[/C][C]1617.73076923077[/C][C]29.2692307692307[/C][/ROW]
[ROW][C]27[/C][C]2081[/C][C]1962.56756756757[/C][C]118.432432432433[/C][/ROW]
[ROW][C]28[/C][C]1393[/C][C]1962.56756756757[/C][C]-569.567567567567[/C][/ROW]
[ROW][C]29[/C][C]2741[/C][C]3086.85714285714[/C][C]-345.857142857143[/C][/ROW]
[ROW][C]30[/C][C]2112[/C][C]1617.73076923077[/C][C]494.269230769231[/C][/ROW]
[ROW][C]31[/C][C]1684[/C][C]1617.73076923077[/C][C]66.2692307692307[/C][/ROW]
[ROW][C]32[/C][C]1616[/C][C]1617.73076923077[/C][C]-1.73076923076928[/C][/ROW]
[ROW][C]33[/C][C]2227[/C][C]1962.56756756757[/C][C]264.432432432433[/C][/ROW]
[ROW][C]34[/C][C]3088[/C][C]2582.15384615385[/C][C]505.846153846154[/C][/ROW]
[ROW][C]35[/C][C]2388[/C][C]2582.15384615385[/C][C]-194.153846153846[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]208.222222222222[/C][C]-207.222222222222[/C][/ROW]
[ROW][C]37[/C][C]2099[/C][C]2582.15384615385[/C][C]-483.153846153846[/C][/ROW]
[ROW][C]38[/C][C]1669[/C][C]1353.42857142857[/C][C]315.571428571429[/C][/ROW]
[ROW][C]39[/C][C]2094[/C][C]1617.73076923077[/C][C]476.269230769231[/C][/ROW]
[ROW][C]40[/C][C]2153[/C][C]3086.85714285714[/C][C]-933.857142857143[/C][/ROW]
[ROW][C]41[/C][C]2390[/C][C]2582.15384615385[/C][C]-192.153846153846[/C][/ROW]
[ROW][C]42[/C][C]1701[/C][C]1962.56756756757[/C][C]-261.567567567567[/C][/ROW]
[ROW][C]43[/C][C]983[/C][C]1009.73333333333[/C][C]-26.7333333333333[/C][/ROW]
[ROW][C]44[/C][C]2161[/C][C]1962.56756756757[/C][C]198.432432432433[/C][/ROW]
[ROW][C]45[/C][C]1276[/C][C]1353.42857142857[/C][C]-77.4285714285713[/C][/ROW]
[ROW][C]46[/C][C]1189[/C][C]1962.56756756757[/C][C]-773.567567567567[/C][/ROW]
[ROW][C]47[/C][C]744[/C][C]1009.73333333333[/C][C]-265.733333333333[/C][/ROW]
[ROW][C]48[/C][C]2231[/C][C]2582.15384615385[/C][C]-351.153846153846[/C][/ROW]
[ROW][C]49[/C][C]2242[/C][C]2582.15384615385[/C][C]-340.153846153846[/C][/ROW]
[ROW][C]50[/C][C]2638[/C][C]1962.56756756757[/C][C]675.432432432433[/C][/ROW]
[ROW][C]51[/C][C]658[/C][C]824.428571428571[/C][C]-166.428571428571[/C][/ROW]
[ROW][C]52[/C][C]1859[/C][C]1962.56756756757[/C][C]-103.567567567567[/C][/ROW]
[ROW][C]53[/C][C]2489[/C][C]3086.85714285714[/C][C]-597.857142857143[/C][/ROW]
[ROW][C]54[/C][C]2025[/C][C]2582.15384615385[/C][C]-557.153846153846[/C][/ROW]
[ROW][C]55[/C][C]1911[/C][C]1962.56756756757[/C][C]-51.5675675675675[/C][/ROW]
[ROW][C]56[/C][C]1714[/C][C]1617.73076923077[/C][C]96.2692307692307[/C][/ROW]
[ROW][C]57[/C][C]1851[/C][C]1962.56756756757[/C][C]-111.567567567567[/C][/ROW]
[ROW][C]58[/C][C]980[/C][C]1009.73333333333[/C][C]-29.7333333333333[/C][/ROW]
[ROW][C]59[/C][C]1177[/C][C]1962.56756756757[/C][C]-785.567567567567[/C][/ROW]
[ROW][C]60[/C][C]2809[/C][C]1962.56756756757[/C][C]846.432432432433[/C][/ROW]
[ROW][C]61[/C][C]1688[/C][C]1962.56756756757[/C][C]-274.567567567567[/C][/ROW]
[ROW][C]62[/C][C]2097[/C][C]1962.56756756757[/C][C]134.432432432433[/C][/ROW]
[ROW][C]63[/C][C]1309[/C][C]1353.42857142857[/C][C]-44.4285714285713[/C][/ROW]
[ROW][C]64[/C][C]1243[/C][C]1353.42857142857[/C][C]-110.428571428571[/C][/ROW]
[ROW][C]65[/C][C]1255[/C][C]1617.73076923077[/C][C]-362.730769230769[/C][/ROW]
[ROW][C]66[/C][C]1293[/C][C]1353.42857142857[/C][C]-60.4285714285713[/C][/ROW]
[ROW][C]67[/C][C]2259[/C][C]1962.56756756757[/C][C]296.432432432433[/C][/ROW]
[ROW][C]68[/C][C]2897[/C][C]1962.56756756757[/C][C]934.432432432433[/C][/ROW]
[ROW][C]69[/C][C]1103[/C][C]1353.42857142857[/C][C]-250.428571428571[/C][/ROW]
[ROW][C]70[/C][C]340[/C][C]208.222222222222[/C][C]131.777777777778[/C][/ROW]
[ROW][C]71[/C][C]2791[/C][C]2582.15384615385[/C][C]208.846153846154[/C][/ROW]
[ROW][C]72[/C][C]1333[/C][C]1962.56756756757[/C][C]-629.567567567567[/C][/ROW]
[ROW][C]73[/C][C]1441[/C][C]1353.42857142857[/C][C]87.5714285714287[/C][/ROW]
[ROW][C]74[/C][C]1622[/C][C]1353.42857142857[/C][C]268.571428571429[/C][/ROW]
[ROW][C]75[/C][C]2649[/C][C]1962.56756756757[/C][C]686.432432432433[/C][/ROW]
[ROW][C]76[/C][C]1499[/C][C]1353.42857142857[/C][C]145.571428571429[/C][/ROW]
[ROW][C]77[/C][C]2302[/C][C]1962.56756756757[/C][C]339.432432432433[/C][/ROW]
[ROW][C]78[/C][C]2540[/C][C]1962.56756756757[/C][C]577.432432432433[/C][/ROW]
[ROW][C]79[/C][C]1000[/C][C]1009.73333333333[/C][C]-9.73333333333335[/C][/ROW]
[ROW][C]80[/C][C]1234[/C][C]1009.73333333333[/C][C]224.266666666667[/C][/ROW]
[ROW][C]81[/C][C]927[/C][C]1009.73333333333[/C][C]-82.7333333333333[/C][/ROW]
[ROW][C]82[/C][C]2176[/C][C]3086.85714285714[/C][C]-910.857142857143[/C][/ROW]
[ROW][C]83[/C][C]956[/C][C]824.428571428571[/C][C]131.571428571429[/C][/ROW]
[ROW][C]84[/C][C]1531[/C][C]1617.73076923077[/C][C]-86.7307692307693[/C][/ROW]
[ROW][C]85[/C][C]1013[/C][C]1009.73333333333[/C][C]3.26666666666665[/C][/ROW]
[ROW][C]86[/C][C]1771[/C][C]1617.73076923077[/C][C]153.269230769231[/C][/ROW]
[ROW][C]87[/C][C]2613[/C][C]2582.15384615385[/C][C]30.8461538461538[/C][/ROW]
[ROW][C]88[/C][C]1203[/C][C]1353.42857142857[/C][C]-150.428571428571[/C][/ROW]
[ROW][C]89[/C][C]1303[/C][C]1353.42857142857[/C][C]-50.4285714285713[/C][/ROW]
[ROW][C]90[/C][C]1524[/C][C]1617.73076923077[/C][C]-93.7307692307693[/C][/ROW]
[ROW][C]91[/C][C]1829[/C][C]1617.73076923077[/C][C]211.269230769231[/C][/ROW]
[ROW][C]92[/C][C]2227[/C][C]1962.56756756757[/C][C]264.432432432433[/C][/ROW]
[ROW][C]93[/C][C]1233[/C][C]1617.73076923077[/C][C]-384.730769230769[/C][/ROW]
[ROW][C]94[/C][C]1365[/C][C]1962.56756756757[/C][C]-597.567567567567[/C][/ROW]
[ROW][C]95[/C][C]901[/C][C]824.428571428571[/C][C]76.5714285714286[/C][/ROW]
[ROW][C]96[/C][C]2319[/C][C]3086.85714285714[/C][C]-767.857142857143[/C][/ROW]
[ROW][C]97[/C][C]1857[/C][C]1962.56756756757[/C][C]-105.567567567567[/C][/ROW]
[ROW][C]98[/C][C]223[/C][C]208.222222222222[/C][C]14.7777777777778[/C][/ROW]
[ROW][C]99[/C][C]2390[/C][C]2582.15384615385[/C][C]-192.153846153846[/C][/ROW]
[ROW][C]100[/C][C]1973[/C][C]1962.56756756757[/C][C]10.4324324324325[/C][/ROW]
[ROW][C]101[/C][C]699[/C][C]824.428571428571[/C][C]-125.428571428571[/C][/ROW]
[ROW][C]102[/C][C]1062[/C][C]1009.73333333333[/C][C]52.2666666666667[/C][/ROW]
[ROW][C]103[/C][C]1252[/C][C]1353.42857142857[/C][C]-101.428571428571[/C][/ROW]
[ROW][C]104[/C][C]1154[/C][C]1353.42857142857[/C][C]-199.428571428571[/C][/ROW]
[ROW][C]105[/C][C]823[/C][C]1009.73333333333[/C][C]-186.733333333333[/C][/ROW]
[ROW][C]106[/C][C]596[/C][C]208.222222222222[/C][C]387.777777777778[/C][/ROW]
[ROW][C]107[/C][C]1471[/C][C]1617.73076923077[/C][C]-146.730769230769[/C][/ROW]
[ROW][C]108[/C][C]1130[/C][C]1353.42857142857[/C][C]-223.428571428571[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]208.222222222222[/C][C]-208.222222222222[/C][/ROW]
[ROW][C]110[/C][C]1082[/C][C]1009.73333333333[/C][C]72.2666666666667[/C][/ROW]
[ROW][C]111[/C][C]1134[/C][C]1353.42857142857[/C][C]-219.428571428571[/C][/ROW]
[ROW][C]112[/C][C]1366[/C][C]1617.73076923077[/C][C]-251.730769230769[/C][/ROW]
[ROW][C]113[/C][C]1452[/C][C]1962.56756756757[/C][C]-510.567567567567[/C][/ROW]
[ROW][C]114[/C][C]869[/C][C]1009.73333333333[/C][C]-140.733333333333[/C][/ROW]
[ROW][C]115[/C][C]78[/C][C]208.222222222222[/C][C]-130.222222222222[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]208.222222222222[/C][C]-208.222222222222[/C][/ROW]
[ROW][C]117[/C][C]1127[/C][C]1617.73076923077[/C][C]-490.730769230769[/C][/ROW]
[ROW][C]118[/C][C]1578[/C][C]1962.56756756757[/C][C]-384.567567567567[/C][/ROW]
[ROW][C]119[/C][C]2018[/C][C]1962.56756756757[/C][C]55.4324324324325[/C][/ROW]
[ROW][C]120[/C][C]919[/C][C]1353.42857142857[/C][C]-434.428571428571[/C][/ROW]
[ROW][C]121[/C][C]778[/C][C]824.428571428571[/C][C]-46.4285714285714[/C][/ROW]
[ROW][C]122[/C][C]1751[/C][C]1962.56756756757[/C][C]-211.567567567567[/C][/ROW]
[ROW][C]123[/C][C]956[/C][C]1009.73333333333[/C][C]-53.7333333333333[/C][/ROW]
[ROW][C]124[/C][C]1875[/C][C]1353.42857142857[/C][C]521.571428571429[/C][/ROW]
[ROW][C]125[/C][C]731[/C][C]824.428571428571[/C][C]-93.4285714285714[/C][/ROW]
[ROW][C]126[/C][C]285[/C][C]208.222222222222[/C][C]76.7777777777778[/C][/ROW]
[ROW][C]127[/C][C]1833[/C][C]1617.73076923077[/C][C]215.269230769231[/C][/ROW]
[ROW][C]128[/C][C]1147[/C][C]1617.73076923077[/C][C]-470.730769230769[/C][/ROW]
[ROW][C]129[/C][C]1646[/C][C]1617.73076923077[/C][C]28.2692307692307[/C][/ROW]
[ROW][C]130[/C][C]256[/C][C]208.222222222222[/C][C]47.7777777777778[/C][/ROW]
[ROW][C]131[/C][C]98[/C][C]208.222222222222[/C][C]-110.222222222222[/C][/ROW]
[ROW][C]132[/C][C]1403[/C][C]1617.73076923077[/C][C]-214.730769230769[/C][/ROW]
[ROW][C]133[/C][C]41[/C][C]208.222222222222[/C][C]-167.222222222222[/C][/ROW]
[ROW][C]134[/C][C]1786[/C][C]1962.56756756757[/C][C]-176.567567567567[/C][/ROW]
[ROW][C]135[/C][C]42[/C][C]208.222222222222[/C][C]-166.222222222222[/C][/ROW]
[ROW][C]136[/C][C]528[/C][C]208.222222222222[/C][C]319.777777777778[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]208.222222222222[/C][C]-208.222222222222[/C][/ROW]
[ROW][C]138[/C][C]1072[/C][C]1353.42857142857[/C][C]-281.428571428571[/C][/ROW]
[ROW][C]139[/C][C]1305[/C][C]1009.73333333333[/C][C]295.266666666667[/C][/ROW]
[ROW][C]140[/C][C]81[/C][C]208.222222222222[/C][C]-127.222222222222[/C][/ROW]
[ROW][C]141[/C][C]261[/C][C]208.222222222222[/C][C]52.7777777777778[/C][/ROW]
[ROW][C]142[/C][C]934[/C][C]1009.73333333333[/C][C]-75.7333333333333[/C][/ROW]
[ROW][C]143[/C][C]1179[/C][C]1617.73076923077[/C][C]-438.730769230769[/C][/ROW]
[ROW][C]144[/C][C]1147[/C][C]1353.42857142857[/C][C]-206.428571428571[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=169236&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169236&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
#	Actuals	Forecasts	Residuals
1	1772	1962.56756756757	-190.567567567567
2	1703	1617.73076923077	85.2692307692307
3	192	208.222222222222	-16.2222222222222
4	2294	1962.56756756757	331.432432432433
5	3448	2582.15384615385	865.846153846154
6	6813	3086.85714285714	3726.14285714286
7	1795	1617.73076923077	177.269230769231
8	1680	1962.56756756757	-282.567567567567
9	1896	1617.73076923077	278.269230769231
10	2917	3086.85714285714	-169.857142857143
11	1946	1353.42857142857	592.571428571429
12	2148	1962.56756756757	185.432432432433
13	1832	1353.42857142857	478.571428571429
14	3059	2582.15384615385	476.846153846154
15	1469	1617.73076923077	-148.730769230769
16	1565	1962.56756756757	-397.567567567567
17	1755	1617.73076923077	137.269230769231
18	1234	1009.73333333333	224.266666666667
19	2779	1962.56756756757	816.432432432433
20	726	208.222222222222	517.777777777778
21	1048	824.428571428571	223.571428571429
22	2804	2582.15384615385	221.846153846154
23	1760	1962.56756756757	-202.567567567567
24	2261	1617.73076923077	643.269230769231
25	1848	1962.56756756757	-114.567567567567
26	1647	1617.73076923077	29.2692307692307
27	2081	1962.56756756757	118.432432432433
28	1393	1962.56756756757	-569.567567567567
29	2741	3086.85714285714	-345.857142857143
30	2112	1617.73076923077	494.269230769231
31	1684	1617.73076923077	66.2692307692307
32	1616	1617.73076923077	-1.73076923076928
33	2227	1962.56756756757	264.432432432433
34	3088	2582.15384615385	505.846153846154
35	2388	2582.15384615385	-194.153846153846
36	1	208.222222222222	-207.222222222222
37	2099	2582.15384615385	-483.153846153846
38	1669	1353.42857142857	315.571428571429
39	2094	1617.73076923077	476.269230769231
40	2153	3086.85714285714	-933.857142857143
41	2390	2582.15384615385	-192.153846153846
42	1701	1962.56756756757	-261.567567567567
43	983	1009.73333333333	-26.7333333333333
44	2161	1962.56756756757	198.432432432433
45	1276	1353.42857142857	-77.4285714285713
46	1189	1962.56756756757	-773.567567567567
47	744	1009.73333333333	-265.733333333333
48	2231	2582.15384615385	-351.153846153846
49	2242	2582.15384615385	-340.153846153846
50	2638	1962.56756756757	675.432432432433
51	658	824.428571428571	-166.428571428571
52	1859	1962.56756756757	-103.567567567567
53	2489	3086.85714285714	-597.857142857143
54	2025	2582.15384615385	-557.153846153846
55	1911	1962.56756756757	-51.5675675675675
56	1714	1617.73076923077	96.2692307692307
57	1851	1962.56756756757	-111.567567567567
58	980	1009.73333333333	-29.7333333333333
59	1177	1962.56756756757	-785.567567567567
60	2809	1962.56756756757	846.432432432433
61	1688	1962.56756756757	-274.567567567567
62	2097	1962.56756756757	134.432432432433
63	1309	1353.42857142857	-44.4285714285713
64	1243	1353.42857142857	-110.428571428571
65	1255	1617.73076923077	-362.730769230769
66	1293	1353.42857142857	-60.4285714285713
67	2259	1962.56756756757	296.432432432433
68	2897	1962.56756756757	934.432432432433
69	1103	1353.42857142857	-250.428571428571
70	340	208.222222222222	131.777777777778
71	2791	2582.15384615385	208.846153846154
72	1333	1962.56756756757	-629.567567567567
73	1441	1353.42857142857	87.5714285714287
74	1622	1353.42857142857	268.571428571429
75	2649	1962.56756756757	686.432432432433
76	1499	1353.42857142857	145.571428571429
77	2302	1962.56756756757	339.432432432433
78	2540	1962.56756756757	577.432432432433
79	1000	1009.73333333333	-9.73333333333335
80	1234	1009.73333333333	224.266666666667
81	927	1009.73333333333	-82.7333333333333
82	2176	3086.85714285714	-910.857142857143
83	956	824.428571428571	131.571428571429
84	1531	1617.73076923077	-86.7307692307693
85	1013	1009.73333333333	3.26666666666665
86	1771	1617.73076923077	153.269230769231
87	2613	2582.15384615385	30.8461538461538
88	1203	1353.42857142857	-150.428571428571
89	1303	1353.42857142857	-50.4285714285713
90	1524	1617.73076923077	-93.7307692307693
91	1829	1617.73076923077	211.269230769231
92	2227	1962.56756756757	264.432432432433
93	1233	1617.73076923077	-384.730769230769
94	1365	1962.56756756757	-597.567567567567
95	901	824.428571428571	76.5714285714286
96	2319	3086.85714285714	-767.857142857143
97	1857	1962.56756756757	-105.567567567567
98	223	208.222222222222	14.7777777777778
99	2390	2582.15384615385	-192.153846153846
100	1973	1962.56756756757	10.4324324324325
101	699	824.428571428571	-125.428571428571
102	1062	1009.73333333333	52.2666666666667
103	1252	1353.42857142857	-101.428571428571
104	1154	1353.42857142857	-199.428571428571
105	823	1009.73333333333	-186.733333333333
106	596	208.222222222222	387.777777777778
107	1471	1617.73076923077	-146.730769230769
108	1130	1353.42857142857	-223.428571428571
109	0	208.222222222222	-208.222222222222
110	1082	1009.73333333333	72.2666666666667
111	1134	1353.42857142857	-219.428571428571
112	1366	1617.73076923077	-251.730769230769
113	1452	1962.56756756757	-510.567567567567
114	869	1009.73333333333	-140.733333333333
115	78	208.222222222222	-130.222222222222
116	0	208.222222222222	-208.222222222222
117	1127	1617.73076923077	-490.730769230769
118	1578	1962.56756756757	-384.567567567567
119	2018	1962.56756756757	55.4324324324325
120	919	1353.42857142857	-434.428571428571
121	778	824.428571428571	-46.4285714285714
122	1751	1962.56756756757	-211.567567567567
123	956	1009.73333333333	-53.7333333333333
124	1875	1353.42857142857	521.571428571429
125	731	824.428571428571	-93.4285714285714
126	285	208.222222222222	76.7777777777778
127	1833	1617.73076923077	215.269230769231
128	1147	1617.73076923077	-470.730769230769
129	1646	1617.73076923077	28.2692307692307
130	256	208.222222222222	47.7777777777778
131	98	208.222222222222	-110.222222222222
132	1403	1617.73076923077	-214.730769230769
133	41	208.222222222222	-167.222222222222
134	1786	1962.56756756757	-176.567567567567
135	42	208.222222222222	-166.222222222222
136	528	208.222222222222	319.777777777778
137	0	208.222222222222	-208.222222222222
138	1072	1353.42857142857	-281.428571428571
139	1305	1009.73333333333	295.266666666667
140	81	208.222222222222	-127.222222222222
141	261	208.222222222222	52.7777777777778
142	934	1009.73333333333	-75.7333333333333
143	1179	1617.73076923077	-438.730769230769
144	1147	1353.42857142857	-206.428571428571

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code