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Author

*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, 16 Dec 2012 12:59:14 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/16/t13556810167guer0a2a2cdhjr.htm/, Retrieved Thu, 18 Apr 2024 22:51:49 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200529, Retrieved Thu, 18 Apr 2024 22:51:49 +0000

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No (this computation is public)

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Recursive Partitioning (Regression Trees)] [Regressieboom Lon...] [2012-12-16 17:59:14] [acfd67cb214b61d0a5e0fb4c8c6ef02a] [Current]

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Dataseries X:

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317	310232,86	41,761	0,939	0,923	0,869
267	1330141,29	6,2	0,623	0,843	0,618
204	139390,2	13,611	0,784	0,77	0,713
198	62348,45	32,147	0,815	0,949	0,832
107	81644,45	32,255	0,928	0,953	0,838
89	64768,39	29,578	0,87	0,971	0,819
88	48636,07	25,493	0,934	0,956	0,808
80	126804,43	29,692	0,883	1.000	0,827
79	21515,75	34,259	0,981	0,976	0,837
69	60748,96	26,578	0,856	0,976	0,799
53	9992,34	16,896	0,866	0,858	0,732
50	16783,09	36,358	0,931	0,958	0,845
49	45415,6	5,737	0,858	0,765	0,591
42	15460,48	10,452	0,834	0,742	0,668
39	4252,28	24,706	1.000	0,957	0,783
39	46505,96	27,066	0,874	0,969	0,799
34	201103,33	9,414	0,663	0,844	0,662
33	76923,3	10,496	0,64	0,836	0,662
32	2847,23	6,931	0,768	0,838	0,598
29	10201,71	22,098	0,924	0,91	0,769
27	33759,74	34,567	0,927	0,962	0,84
25	9612,63	11,841	0,776	0,794	0,702
23	40046,57	1,428	0,582	0,586	0,387
22	21959,28	10,794	0,831	0,851	0,674
21	5515,57	32,252	0,924	0,928	0,836
20	8303,51	8,752	0,671	0,8	0,639
20	88013,49	848	0,237	0,619	0,326
20	38463,69	16,705	0,822	0,885	0,739
20	49109,11	9,333	0,705	0,517	0,652
19	4486,88	16,338	0,778	0,893	0,724
16	9074,06	32,314	0,904	0,969	0,842
15	44205,29	8,136	0,667	0,847	0,633
15	77804,12	11,209	0,583	0,851	0,689
14	4600,82	4,335	0,839	0,848	0,554
14	3545,32	15,011	0,883	0,824	0,729
14	112468,85	12,429	0,726	0,898	0,7
14	7623,44	36,954	0,872	0,983	0,858
13	4676,31	47,676	0,985	0,964	0,883
10	4622,92	36,278	0,963	0,955	0,814
9	41343,2	13,202	0,806	0,882	0,713
9	7344,85	9,967	0,79	0,86	0,663
9	2003,14	24,806	0,933	0,936	0,79
8	1173108,02	2,993	0,45	0,717	0,508
8	1228,69	23,221	0,712	0,791	0,782
8	10525,04	7,512	0,645	0,86	0,614
8	27865,74	2,611	0,711	0,762	0,486
7	9823,82	7,658	0,616	0,842	0,629
7	3086,92	3,198	0,722	0,765	0,505
6	2217,97	12,847	0,873	0,841	0,711
5	34586,18	7,421	0,652	0,838	0,621
5	107,82	7,593	0,779	0,883	0,608
5	5470,31	19,202	0,875	0,875	0,759
5	66336,26	7,26	0,597	0,854	0,622
5	33398,68	1,105	0,475	0,538	0,347
5	27223,23	11,19	0,692	0,858	0,669
4	2966,8	4,794	0,76	0,856	0,566
4	10423,49	32,395	0,882	0,947	0,832
4	80471,87	5,151	0,56	0,84	0,568
4	5255,07	30,784	0,877	0,946	0,828
3	7148,78	11,456	0,802	0,842	0,678
3	1291,17	16,132	0,916	0,865	0,734
3	242968,34	3,813	0,584	0,779	0,518
3	28274,73	12,724	0,73	0,855	0,704
2	2029,31	12,154	0,693	0,523	0,698
2	1102,68	25,759	0,798	0,94	0,79
2	1545,26	13,094	0,66	0,674	0,689
2	10749,94	26,482	0,861	0,945	0,783
2	13550,44	4,286	0,438	0,807	0,534
2	666,73	10,022	0,802	0,861	0,665
2	10735,76	21,37	0,739	0,938	0,763
2	840,93	82,978	0,623	0,921	1.000
2	4317,48	2,592	0,716	0,778	0,49
2	4701,07	45,978	0,751	0,964	0,897
1	29121,29	1,20	0,367	0,452	0,38
1	7089,7	39,255	0,837	0,99	0,874
1	31627,43	4,081	0,447	0,823	0,535
1	25731,78	21,321	0,689	0,85	0,781
1	7487,49	1,791	0,704	0,75	0,425
0	2986,95	7,449	0,721	0,898	0,624
0	13068,16	5,278	0,422	0,49	0,557
0	86,75	17,052	0,744	0,83	0,723
0	8214,16	34,673	0,858	0,96	0,842
0	156118,46	1,286	0,415	0,772	0,391
0	314,52	6,019	0,663	0,884	0,582
0	9056,01	1,369	0,365	0,569	0,374
0	699,85	4,643	0,336	0,744	0,568
0	9947,42	4,013	0,749	0,735	0,53
0	4621,6	7,266	0,723	0,878	0,621
0	16241,81	1,078	0,187	0,559	0,349
0	9863,12	356	0,353	0,48	0,186
0	14453,68	1,739	0,502	0,68	0,418
0	19294,15	2,002	0,52	0,499	0,431
0	508,66	3,309	0,425	0,854	0,505
0	4844,93	688	0,321	0,448	0,28
0	10543,46	1,181	0,219	0,466	0,344
0	16746,49	13,057	0,797	0,932	0,701
0	773,41	1,074	0,368	0,648	0,341
0	4125,92	3,848	0,523	0,59	0,49
0	4516,22	10,085	0,659	0,936	0,667
0	21058,8	1,545	0,304	0,558	0,377
0	740,53	2,106	0,294	0,598	0,451
0	72,81	8,066	0,67	0,907	0,626
0	69851,29	290	0,356	0,448	0,147
0	14790,61	7,508	0,686	0,877	0,62
0	6052,06	6,02	0,637	0,823	0,585
0	650,7	28,857	0,427	0,49	0,741
0	5792,98	527	0,271	0,656	0,24
0	875,98	4,11	0,786	0,777	0,533
0	1755,46	1,285	0,334	0,607	0,365
0	24339,84	1,41	0,574	0,698	0,396
0	10324,02	951	0,246	0,538	0,309
0	1565,13	973	0,302	0,444	0,329
0	748,49	2,942	0,65	0,787	0,496
0	9648,92	1,045	0,406	0,664	0,346
0	7989,41	3,488	0,574	0,838	0,507
0	308,91	33,98	0,912	0,975	0,814
0	29671,6	3,222	0,491	0,774	0,495
0	7353,98	25,474	0,907	0,972	0,796
0	6407,09	5,082	0,71	0,842	0,569
0	99,48	2,209	0,647	0,759	0,494
0	5508,63	2,073	0,716	0,753	0,432
0	6368,16	2,048	0,432	0,749	0,445
0	4125,25	11,868	0,695	0,83	0,698
0	1919,55	1,333	0,507	0,445	0,403
0	3685,08	360	0,439	0,58	0,14
0	6461,45	14,985	0,731	0,864	0,693
0	497,54	68,853	0,771	0,946	0,892
0	21281,84	912	0,497	0,737	0,302
0	15447,5	721	0,41	0,54	0,289
0	395,65	4,972	0,568	0,897	0,568
0	13796,35	1,077	0,27	0,496	0,346
0	406,77	21,987	0,797	0,941	0,769
0	3205,06	1,751	0,366	0,609	0,419
0	1294,1	11,658	0,659	0,842	0,696
0	107,15	2,804	0,689	0,773	0,484
0	22417,45	804	0,222	0,477	0,314
0	2128,47	5,821	0,617	0,67	0,591
0	28951,85	1,049	0,356	0,77	0,351
0	5604,45	2,398	0,525	0,852	0,457
0	15878,27	626	0,177	0,547	0,266
0	152217,34	2,001	0,442	0,503	0,434
0	184404,79	2,369	0,386	0,717	0,464
0	3410,68	11,857	0,743	0,885	0,69
0	6064,52	2,072	0,335	0,675	0,447
0	6375,83	4,107	0,643	0,828	0,552
0	28947,97	7,836	0,704	0,852	0,634
0	99900,18	3,216	0,684	0,769	0,508
0	11055,98	1,032	0,407	0,559	0,348
0	175,81	1,653	0,452	0,705	0,413
0	160,92	8,722	0,693	0,862	0,632
0	192	4	0,75	0,827	0,526
0	12323,25	1,65	0,385	0,62	0,406
0	88,34	17,786	0,747	0,845	0,733
0	5245,69	734	0,304	0,438	0,286
0	559,2	2,312	0,427	0,755	0,413
0	21083,83	4,333	0,68	0,867	0,559
0	49,9	13,191	0,693	0,838	0,684
0	104,22	8,312	0,712	0,825	0,628
0	43939,6	2,007	0,247	0,654	0,421
0	1354,05	4,539	0,578	0,453	0,545
0	22198,11	4,295	0,534	0,881	0,537
0	41892,89	1,237	0,454	0,603	0,37
0	1154,62	731	0,371	0,67	0,487
0	6587,24	772	0,473	0,585	0,297
0	105,63	4,055	0,79	0,825	0,535
0	4940,92	6,576	0,739	0,71	0,615
0	4975,59	52,435	0,741	0,892	0,916
0	3301,08	11,977	0,763	0,899	0,7
0	221,55	4,03	0,554	0,805	0,527
0	89571,13	2,682	0,503	0,87	0,478
0	23495,36	2,243	0,31	0,718	0,444
0	13460,31	1,299	0,48	0,458	0,362

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200529&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200529&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200529&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	6 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Goodness of Fit
Correlation	0.7225
R-squared	0.5219
RMSE	28.2755

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7225 \tabularnewline
R-squared & 0.5219 \tabularnewline
RMSE & 28.2755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200529&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7225[/C][/ROW]
[ROW][C]R-squared[/C][C]0.5219[/C][/ROW]
[ROW][C]RMSE[/C][C]28.2755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200529&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200529&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	0.7225
R-squared	0.5219
RMSE	28.2755

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	317	132.333333333333	184.666666666667
2	267	26.2222222222222	240.777777777778
3	204	132.333333333333	71.6666666666667
4	198	132.333333333333	65.6666666666667
5	107	132.333333333333	-25.3333333333333
6	89	132.333333333333	-43.3333333333333
7	88	132.333333333333	-44.3333333333333
8	80	132.333333333333	-52.3333333333333
9	79	30.7777777777778	48.2222222222222
10	69	132.333333333333	-63.3333333333333
11	53	19.1818181818182	33.8181818181818
12	50	30.7777777777778	19.2222222222222
13	49	26.2222222222222	22.7777777777778
14	42	19.1818181818182	22.8181818181818
15	39	30.7777777777778	8.22222222222222
16	39	132.333333333333	-93.3333333333333
17	34	26.2222222222222	7.77777777777778
18	33	26.2222222222222	6.77777777777778
19	32	19.1818181818182	12.8181818181818
20	29	30.7777777777778	-1.77777777777778
21	27	30.7777777777778	-3.77777777777778
22	25	19.1818181818182	5.81818181818182
23	23	7	16
24	22	19.1818181818182	2.81818181818182
25	21	30.7777777777778	-9.77777777777778
26	20	1.78723404255319	18.2127659574468
27	20	26.2222222222222	-6.22222222222222
28	20	5.47619047619048	14.5238095238095
29	20	26.2222222222222	-6.22222222222222
30	19	5.47619047619048	13.5238095238095
31	16	5.47619047619048	10.5238095238095
32	15	7	8
33	15	26.2222222222222	-11.2222222222222
34	14	19.1818181818182	-5.18181818181818
35	14	19.1818181818182	-5.18181818181818
36	14	26.2222222222222	-12.2222222222222
37	14	5.47619047619048	8.52380952380952
38	13	30.7777777777778	-17.7777777777778
39	10	30.7777777777778	-20.7777777777778
40	9	5.47619047619048	3.52380952380952
41	9	5.47619047619048	3.52380952380952
42	9	30.7777777777778	-21.7777777777778
43	8	26.2222222222222	-18.2222222222222
44	8	1.78723404255319	6.21276595744681
45	8	1.78723404255319	6.21276595744681
46	8	1.78723404255319	6.21276595744681
47	7	1.78723404255319	5.21276595744681
48	7	1.78723404255319	5.21276595744681
49	6	19.1818181818182	-13.1818181818182
50	5	7	-2
51	5	5.47619047619048	-0.476190476190476
52	5	5.47619047619048	-0.476190476190476
53	5	26.2222222222222	-21.2222222222222
54	5	7	-2
55	5	1.78723404255319	3.21276595744681
56	4	1.78723404255319	2.21276595744681
57	4	5.47619047619048	-1.47619047619048
58	4	26.2222222222222	-22.2222222222222
59	4	5.47619047619048	-1.47619047619048
60	3	19.1818181818182	-16.1818181818182
61	3	5.47619047619048	-2.47619047619048
62	3	26.2222222222222	-23.2222222222222
63	3	1.78723404255319	1.21276595744681
64	2	1.78723404255319	0.212765957446809
65	2	5.47619047619048	-3.47619047619048
66	2	1.78723404255319	0.212765957446809
67	2	5.47619047619048	-3.47619047619048
68	2	0.06	1.94
69	2	5.47619047619048	-3.47619047619048
70	2	1.78723404255319	0.212765957446809
71	2	1.78723404255319	0.212765957446809
72	2	1.78723404255319	0.212765957446809
73	2	1.78723404255319	0.212765957446809
74	1	0.06	0.94
75	1	5.47619047619048	-4.47619047619048
76	1	7	-6
77	1	1.78723404255319	-0.787234042553191
78	1	1.78723404255319	-0.787234042553191
79	0	1.78723404255319	-1.78723404255319
80	0	0.06	-0.06
81	0	1.78723404255319	-1.78723404255319
82	0	5.47619047619048	-5.47619047619048
83	0	26.2222222222222	-26.2222222222222
84	0	1.78723404255319	-1.78723404255319
85	0	0.06	-0.06
86	0	0.06	-0.06
87	0	1.78723404255319	-1.78723404255319
88	0	1.78723404255319	-1.78723404255319
89	0	0.06	-0.06
90	0	0.06	-0.06
91	0	0.06	-0.06
92	0	0.06	-0.06
93	0	0.06	-0.06
94	0	0.06	-0.06
95	0	0.06	-0.06
96	0	5.47619047619048	-5.47619047619048
97	0	0.06	-0.06
98	0	0.06	-0.06
99	0	1.78723404255319	-1.78723404255319
100	0	0.06	-0.06
101	0	0.06	-0.06
102	0	1.78723404255319	-1.78723404255319
103	0	26.2222222222222	-26.2222222222222
104	0	1.78723404255319	-1.78723404255319
105	0	1.78723404255319	-1.78723404255319
106	0	0.06	-0.06
107	0	0.06	-0.06
108	0	19.1818181818182	-19.1818181818182
109	0	0.06	-0.06
110	0	0.06	-0.06
111	0	0.06	-0.06
112	0	0.06	-0.06
113	0	1.78723404255319	-1.78723404255319
114	0	0.06	-0.06
115	0	0.06	-0.06
116	0	5.47619047619048	-5.47619047619048
117	0	0.06	-0.06
118	0	5.47619047619048	-5.47619047619048
119	0	1.78723404255319	-1.78723404255319
120	0	1.78723404255319	-1.78723404255319
121	0	1.78723404255319	-1.78723404255319
122	0	0.06	-0.06
123	0	1.78723404255319	-1.78723404255319
124	0	0.06	-0.06
125	0	0.06	-0.06
126	0	1.78723404255319	-1.78723404255319
127	0	5.47619047619048	-5.47619047619048
128	0	0.06	-0.06
129	0	0.06	-0.06
130	0	0.06	-0.06
131	0	0.06	-0.06
132	0	5.47619047619048	-5.47619047619048
133	0	0.06	-0.06
134	0	1.78723404255319	-1.78723404255319
135	0	1.78723404255319	-1.78723404255319
136	0	0.06	-0.06
137	0	1.78723404255319	-1.78723404255319
138	0	0.06	-0.06
139	0	0.06	-0.06
140	0	0.06	-0.06
141	0	26.2222222222222	-26.2222222222222
142	0	26.2222222222222	-26.2222222222222
143	0	1.78723404255319	-1.78723404255319
144	0	0.06	-0.06
145	0	1.78723404255319	-1.78723404255319
146	0	1.78723404255319	-1.78723404255319
147	0	26.2222222222222	-26.2222222222222
148	0	0.06	-0.06
149	0	0.06	-0.06
150	0	1.78723404255319	-1.78723404255319
151	0	1.78723404255319	-1.78723404255319
152	0	0.06	-0.06
153	0	1.78723404255319	-1.78723404255319
154	0	0.06	-0.06
155	0	0.06	-0.06
156	0	1.78723404255319	-1.78723404255319
157	0	1.78723404255319	-1.78723404255319
158	0	1.78723404255319	-1.78723404255319
159	0	7	-7
160	0	0.06	-0.06
161	0	0.06	-0.06
162	0	7	-7
163	0	0.06	-0.06
164	0	0.06	-0.06
165	0	19.1818181818182	-19.1818181818182
166	0	1.78723404255319	-1.78723404255319
167	0	1.78723404255319	-1.78723404255319
168	0	1.78723404255319	-1.78723404255319
169	0	0.06	-0.06
170	0	26.2222222222222	-26.2222222222222
171	0	0.06	-0.06
172	0	0.06	-0.06

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 317 & 132.333333333333 & 184.666666666667 \tabularnewline
2 & 267 & 26.2222222222222 & 240.777777777778 \tabularnewline
3 & 204 & 132.333333333333 & 71.6666666666667 \tabularnewline
4 & 198 & 132.333333333333 & 65.6666666666667 \tabularnewline
5 & 107 & 132.333333333333 & -25.3333333333333 \tabularnewline
6 & 89 & 132.333333333333 & -43.3333333333333 \tabularnewline
7 & 88 & 132.333333333333 & -44.3333333333333 \tabularnewline
8 & 80 & 132.333333333333 & -52.3333333333333 \tabularnewline
9 & 79 & 30.7777777777778 & 48.2222222222222 \tabularnewline
10 & 69 & 132.333333333333 & -63.3333333333333 \tabularnewline
11 & 53 & 19.1818181818182 & 33.8181818181818 \tabularnewline
12 & 50 & 30.7777777777778 & 19.2222222222222 \tabularnewline
13 & 49 & 26.2222222222222 & 22.7777777777778 \tabularnewline
14 & 42 & 19.1818181818182 & 22.8181818181818 \tabularnewline
15 & 39 & 30.7777777777778 & 8.22222222222222 \tabularnewline
16 & 39 & 132.333333333333 & -93.3333333333333 \tabularnewline
17 & 34 & 26.2222222222222 & 7.77777777777778 \tabularnewline
18 & 33 & 26.2222222222222 & 6.77777777777778 \tabularnewline
19 & 32 & 19.1818181818182 & 12.8181818181818 \tabularnewline
20 & 29 & 30.7777777777778 & -1.77777777777778 \tabularnewline
21 & 27 & 30.7777777777778 & -3.77777777777778 \tabularnewline
22 & 25 & 19.1818181818182 & 5.81818181818182 \tabularnewline
23 & 23 & 7 & 16 \tabularnewline
24 & 22 & 19.1818181818182 & 2.81818181818182 \tabularnewline
25 & 21 & 30.7777777777778 & -9.77777777777778 \tabularnewline
26 & 20 & 1.78723404255319 & 18.2127659574468 \tabularnewline
27 & 20 & 26.2222222222222 & -6.22222222222222 \tabularnewline
28 & 20 & 5.47619047619048 & 14.5238095238095 \tabularnewline
29 & 20 & 26.2222222222222 & -6.22222222222222 \tabularnewline
30 & 19 & 5.47619047619048 & 13.5238095238095 \tabularnewline
31 & 16 & 5.47619047619048 & 10.5238095238095 \tabularnewline
32 & 15 & 7 & 8 \tabularnewline
33 & 15 & 26.2222222222222 & -11.2222222222222 \tabularnewline
34 & 14 & 19.1818181818182 & -5.18181818181818 \tabularnewline
35 & 14 & 19.1818181818182 & -5.18181818181818 \tabularnewline
36 & 14 & 26.2222222222222 & -12.2222222222222 \tabularnewline
37 & 14 & 5.47619047619048 & 8.52380952380952 \tabularnewline
38 & 13 & 30.7777777777778 & -17.7777777777778 \tabularnewline
39 & 10 & 30.7777777777778 & -20.7777777777778 \tabularnewline
40 & 9 & 5.47619047619048 & 3.52380952380952 \tabularnewline
41 & 9 & 5.47619047619048 & 3.52380952380952 \tabularnewline
42 & 9 & 30.7777777777778 & -21.7777777777778 \tabularnewline
43 & 8 & 26.2222222222222 & -18.2222222222222 \tabularnewline
44 & 8 & 1.78723404255319 & 6.21276595744681 \tabularnewline
45 & 8 & 1.78723404255319 & 6.21276595744681 \tabularnewline
46 & 8 & 1.78723404255319 & 6.21276595744681 \tabularnewline
47 & 7 & 1.78723404255319 & 5.21276595744681 \tabularnewline
48 & 7 & 1.78723404255319 & 5.21276595744681 \tabularnewline
49 & 6 & 19.1818181818182 & -13.1818181818182 \tabularnewline
50 & 5 & 7 & -2 \tabularnewline
51 & 5 & 5.47619047619048 & -0.476190476190476 \tabularnewline
52 & 5 & 5.47619047619048 & -0.476190476190476 \tabularnewline
53 & 5 & 26.2222222222222 & -21.2222222222222 \tabularnewline
54 & 5 & 7 & -2 \tabularnewline
55 & 5 & 1.78723404255319 & 3.21276595744681 \tabularnewline
56 & 4 & 1.78723404255319 & 2.21276595744681 \tabularnewline
57 & 4 & 5.47619047619048 & -1.47619047619048 \tabularnewline
58 & 4 & 26.2222222222222 & -22.2222222222222 \tabularnewline
59 & 4 & 5.47619047619048 & -1.47619047619048 \tabularnewline
60 & 3 & 19.1818181818182 & -16.1818181818182 \tabularnewline
61 & 3 & 5.47619047619048 & -2.47619047619048 \tabularnewline
62 & 3 & 26.2222222222222 & -23.2222222222222 \tabularnewline
63 & 3 & 1.78723404255319 & 1.21276595744681 \tabularnewline
64 & 2 & 1.78723404255319 & 0.212765957446809 \tabularnewline
65 & 2 & 5.47619047619048 & -3.47619047619048 \tabularnewline
66 & 2 & 1.78723404255319 & 0.212765957446809 \tabularnewline
67 & 2 & 5.47619047619048 & -3.47619047619048 \tabularnewline
68 & 2 & 0.06 & 1.94 \tabularnewline
69 & 2 & 5.47619047619048 & -3.47619047619048 \tabularnewline
70 & 2 & 1.78723404255319 & 0.212765957446809 \tabularnewline
71 & 2 & 1.78723404255319 & 0.212765957446809 \tabularnewline
72 & 2 & 1.78723404255319 & 0.212765957446809 \tabularnewline
73 & 2 & 1.78723404255319 & 0.212765957446809 \tabularnewline
74 & 1 & 0.06 & 0.94 \tabularnewline
75 & 1 & 5.47619047619048 & -4.47619047619048 \tabularnewline
76 & 1 & 7 & -6 \tabularnewline
77 & 1 & 1.78723404255319 & -0.787234042553191 \tabularnewline
78 & 1 & 1.78723404255319 & -0.787234042553191 \tabularnewline
79 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
80 & 0 & 0.06 & -0.06 \tabularnewline
81 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
82 & 0 & 5.47619047619048 & -5.47619047619048 \tabularnewline
83 & 0 & 26.2222222222222 & -26.2222222222222 \tabularnewline
84 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
85 & 0 & 0.06 & -0.06 \tabularnewline
86 & 0 & 0.06 & -0.06 \tabularnewline
87 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
88 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
89 & 0 & 0.06 & -0.06 \tabularnewline
90 & 0 & 0.06 & -0.06 \tabularnewline
91 & 0 & 0.06 & -0.06 \tabularnewline
92 & 0 & 0.06 & -0.06 \tabularnewline
93 & 0 & 0.06 & -0.06 \tabularnewline
94 & 0 & 0.06 & -0.06 \tabularnewline
95 & 0 & 0.06 & -0.06 \tabularnewline
96 & 0 & 5.47619047619048 & -5.47619047619048 \tabularnewline
97 & 0 & 0.06 & -0.06 \tabularnewline
98 & 0 & 0.06 & -0.06 \tabularnewline
99 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
100 & 0 & 0.06 & -0.06 \tabularnewline
101 & 0 & 0.06 & -0.06 \tabularnewline
102 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
103 & 0 & 26.2222222222222 & -26.2222222222222 \tabularnewline
104 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
105 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
106 & 0 & 0.06 & -0.06 \tabularnewline
107 & 0 & 0.06 & -0.06 \tabularnewline
108 & 0 & 19.1818181818182 & -19.1818181818182 \tabularnewline
109 & 0 & 0.06 & -0.06 \tabularnewline
110 & 0 & 0.06 & -0.06 \tabularnewline
111 & 0 & 0.06 & -0.06 \tabularnewline
112 & 0 & 0.06 & -0.06 \tabularnewline
113 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
114 & 0 & 0.06 & -0.06 \tabularnewline
115 & 0 & 0.06 & -0.06 \tabularnewline
116 & 0 & 5.47619047619048 & -5.47619047619048 \tabularnewline
117 & 0 & 0.06 & -0.06 \tabularnewline
118 & 0 & 5.47619047619048 & -5.47619047619048 \tabularnewline
119 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
120 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
121 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
122 & 0 & 0.06 & -0.06 \tabularnewline
123 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
124 & 0 & 0.06 & -0.06 \tabularnewline
125 & 0 & 0.06 & -0.06 \tabularnewline
126 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
127 & 0 & 5.47619047619048 & -5.47619047619048 \tabularnewline
128 & 0 & 0.06 & -0.06 \tabularnewline
129 & 0 & 0.06 & -0.06 \tabularnewline
130 & 0 & 0.06 & -0.06 \tabularnewline
131 & 0 & 0.06 & -0.06 \tabularnewline
132 & 0 & 5.47619047619048 & -5.47619047619048 \tabularnewline
133 & 0 & 0.06 & -0.06 \tabularnewline
134 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
135 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
136 & 0 & 0.06 & -0.06 \tabularnewline
137 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
138 & 0 & 0.06 & -0.06 \tabularnewline
139 & 0 & 0.06 & -0.06 \tabularnewline
140 & 0 & 0.06 & -0.06 \tabularnewline
141 & 0 & 26.2222222222222 & -26.2222222222222 \tabularnewline
142 & 0 & 26.2222222222222 & -26.2222222222222 \tabularnewline
143 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
144 & 0 & 0.06 & -0.06 \tabularnewline
145 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
146 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
147 & 0 & 26.2222222222222 & -26.2222222222222 \tabularnewline
148 & 0 & 0.06 & -0.06 \tabularnewline
149 & 0 & 0.06 & -0.06 \tabularnewline
150 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
151 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
152 & 0 & 0.06 & -0.06 \tabularnewline
153 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
154 & 0 & 0.06 & -0.06 \tabularnewline
155 & 0 & 0.06 & -0.06 \tabularnewline
156 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
157 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
158 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
159 & 0 & 7 & -7 \tabularnewline
160 & 0 & 0.06 & -0.06 \tabularnewline
161 & 0 & 0.06 & -0.06 \tabularnewline
162 & 0 & 7 & -7 \tabularnewline
163 & 0 & 0.06 & -0.06 \tabularnewline
164 & 0 & 0.06 & -0.06 \tabularnewline
165 & 0 & 19.1818181818182 & -19.1818181818182 \tabularnewline
166 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
167 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
168 & 0 & 1.78723404255319 & -1.78723404255319 \tabularnewline
169 & 0 & 0.06 & -0.06 \tabularnewline
170 & 0 & 26.2222222222222 & -26.2222222222222 \tabularnewline
171 & 0 & 0.06 & -0.06 \tabularnewline
172 & 0 & 0.06 & -0.06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200529&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]317[/C][C]132.333333333333[/C][C]184.666666666667[/C][/ROW]
[ROW][C]2[/C][C]267[/C][C]26.2222222222222[/C][C]240.777777777778[/C][/ROW]
[ROW][C]3[/C][C]204[/C][C]132.333333333333[/C][C]71.6666666666667[/C][/ROW]
[ROW][C]4[/C][C]198[/C][C]132.333333333333[/C][C]65.6666666666667[/C][/ROW]
[ROW][C]5[/C][C]107[/C][C]132.333333333333[/C][C]-25.3333333333333[/C][/ROW]
[ROW][C]6[/C][C]89[/C][C]132.333333333333[/C][C]-43.3333333333333[/C][/ROW]
[ROW][C]7[/C][C]88[/C][C]132.333333333333[/C][C]-44.3333333333333[/C][/ROW]
[ROW][C]8[/C][C]80[/C][C]132.333333333333[/C][C]-52.3333333333333[/C][/ROW]
[ROW][C]9[/C][C]79[/C][C]30.7777777777778[/C][C]48.2222222222222[/C][/ROW]
[ROW][C]10[/C][C]69[/C][C]132.333333333333[/C][C]-63.3333333333333[/C][/ROW]
[ROW][C]11[/C][C]53[/C][C]19.1818181818182[/C][C]33.8181818181818[/C][/ROW]
[ROW][C]12[/C][C]50[/C][C]30.7777777777778[/C][C]19.2222222222222[/C][/ROW]
[ROW][C]13[/C][C]49[/C][C]26.2222222222222[/C][C]22.7777777777778[/C][/ROW]
[ROW][C]14[/C][C]42[/C][C]19.1818181818182[/C][C]22.8181818181818[/C][/ROW]
[ROW][C]15[/C][C]39[/C][C]30.7777777777778[/C][C]8.22222222222222[/C][/ROW]
[ROW][C]16[/C][C]39[/C][C]132.333333333333[/C][C]-93.3333333333333[/C][/ROW]
[ROW][C]17[/C][C]34[/C][C]26.2222222222222[/C][C]7.77777777777778[/C][/ROW]
[ROW][C]18[/C][C]33[/C][C]26.2222222222222[/C][C]6.77777777777778[/C][/ROW]
[ROW][C]19[/C][C]32[/C][C]19.1818181818182[/C][C]12.8181818181818[/C][/ROW]
[ROW][C]20[/C][C]29[/C][C]30.7777777777778[/C][C]-1.77777777777778[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]30.7777777777778[/C][C]-3.77777777777778[/C][/ROW]
[ROW][C]22[/C][C]25[/C][C]19.1818181818182[/C][C]5.81818181818182[/C][/ROW]
[ROW][C]23[/C][C]23[/C][C]7[/C][C]16[/C][/ROW]
[ROW][C]24[/C][C]22[/C][C]19.1818181818182[/C][C]2.81818181818182[/C][/ROW]
[ROW][C]25[/C][C]21[/C][C]30.7777777777778[/C][C]-9.77777777777778[/C][/ROW]
[ROW][C]26[/C][C]20[/C][C]1.78723404255319[/C][C]18.2127659574468[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]26.2222222222222[/C][C]-6.22222222222222[/C][/ROW]
[ROW][C]28[/C][C]20[/C][C]5.47619047619048[/C][C]14.5238095238095[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]26.2222222222222[/C][C]-6.22222222222222[/C][/ROW]
[ROW][C]30[/C][C]19[/C][C]5.47619047619048[/C][C]13.5238095238095[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]5.47619047619048[/C][C]10.5238095238095[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]7[/C][C]8[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]26.2222222222222[/C][C]-11.2222222222222[/C][/ROW]
[ROW][C]34[/C][C]14[/C][C]19.1818181818182[/C][C]-5.18181818181818[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]19.1818181818182[/C][C]-5.18181818181818[/C][/ROW]
[ROW][C]36[/C][C]14[/C][C]26.2222222222222[/C][C]-12.2222222222222[/C][/ROW]
[ROW][C]37[/C][C]14[/C][C]5.47619047619048[/C][C]8.52380952380952[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]30.7777777777778[/C][C]-17.7777777777778[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]30.7777777777778[/C][C]-20.7777777777778[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]5.47619047619048[/C][C]3.52380952380952[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]5.47619047619048[/C][C]3.52380952380952[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]30.7777777777778[/C][C]-21.7777777777778[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]26.2222222222222[/C][C]-18.2222222222222[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]1.78723404255319[/C][C]6.21276595744681[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]1.78723404255319[/C][C]6.21276595744681[/C][/ROW]
[ROW][C]46[/C][C]8[/C][C]1.78723404255319[/C][C]6.21276595744681[/C][/ROW]
[ROW][C]47[/C][C]7[/C][C]1.78723404255319[/C][C]5.21276595744681[/C][/ROW]
[ROW][C]48[/C][C]7[/C][C]1.78723404255319[/C][C]5.21276595744681[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]19.1818181818182[/C][C]-13.1818181818182[/C][/ROW]
[ROW][C]50[/C][C]5[/C][C]7[/C][C]-2[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]5.47619047619048[/C][C]-0.476190476190476[/C][/ROW]
[ROW][C]52[/C][C]5[/C][C]5.47619047619048[/C][C]-0.476190476190476[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]26.2222222222222[/C][C]-21.2222222222222[/C][/ROW]
[ROW][C]54[/C][C]5[/C][C]7[/C][C]-2[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]1.78723404255319[/C][C]3.21276595744681[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]1.78723404255319[/C][C]2.21276595744681[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]5.47619047619048[/C][C]-1.47619047619048[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]26.2222222222222[/C][C]-22.2222222222222[/C][/ROW]
[ROW][C]59[/C][C]4[/C][C]5.47619047619048[/C][C]-1.47619047619048[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]19.1818181818182[/C][C]-16.1818181818182[/C][/ROW]
[ROW][C]61[/C][C]3[/C][C]5.47619047619048[/C][C]-2.47619047619048[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]26.2222222222222[/C][C]-23.2222222222222[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]1.78723404255319[/C][C]1.21276595744681[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.78723404255319[/C][C]0.212765957446809[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]5.47619047619048[/C][C]-3.47619047619048[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]1.78723404255319[/C][C]0.212765957446809[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]5.47619047619048[/C][C]-3.47619047619048[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]0.06[/C][C]1.94[/C][/ROW]
[ROW][C]69[/C][C]2[/C][C]5.47619047619048[/C][C]-3.47619047619048[/C][/ROW]
[ROW][C]70[/C][C]2[/C][C]1.78723404255319[/C][C]0.212765957446809[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]1.78723404255319[/C][C]0.212765957446809[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.78723404255319[/C][C]0.212765957446809[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]1.78723404255319[/C][C]0.212765957446809[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]0.06[/C][C]0.94[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]5.47619047619048[/C][C]-4.47619047619048[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]7[/C][C]-6[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.78723404255319[/C][C]-0.787234042553191[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.78723404255319[/C][C]-0.787234042553191[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]5.47619047619048[/C][C]-5.47619047619048[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]26.2222222222222[/C][C]-26.2222222222222[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]5.47619047619048[/C][C]-5.47619047619048[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]26.2222222222222[/C][C]-26.2222222222222[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]19.1818181818182[/C][C]-19.1818181818182[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]5.47619047619048[/C][C]-5.47619047619048[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]5.47619047619048[/C][C]-5.47619047619048[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]5.47619047619048[/C][C]-5.47619047619048[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]5.47619047619048[/C][C]-5.47619047619048[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]26.2222222222222[/C][C]-26.2222222222222[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]26.2222222222222[/C][C]-26.2222222222222[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]26.2222222222222[/C][C]-26.2222222222222[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]155[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]156[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]7[/C][C]-7[/C][/ROW]
[ROW][C]160[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]161[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]162[/C][C]0[/C][C]7[/C][C]-7[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]164[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]165[/C][C]0[/C][C]19.1818181818182[/C][C]-19.1818181818182[/C][/ROW]
[ROW][C]166[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]167[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]168[/C][C]0[/C][C]1.78723404255319[/C][C]-1.78723404255319[/C][/ROW]
[ROW][C]169[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]170[/C][C]0[/C][C]26.2222222222222[/C][C]-26.2222222222222[/C][/ROW]
[ROW][C]171[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[ROW][C]172[/C][C]0[/C][C]0.06[/C][C]-0.06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200529&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200529&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	317	132.333333333333	184.666666666667
2	267	26.2222222222222	240.777777777778
3	204	132.333333333333	71.6666666666667
4	198	132.333333333333	65.6666666666667
5	107	132.333333333333	-25.3333333333333
6	89	132.333333333333	-43.3333333333333
7	88	132.333333333333	-44.3333333333333
8	80	132.333333333333	-52.3333333333333
9	79	30.7777777777778	48.2222222222222
10	69	132.333333333333	-63.3333333333333
11	53	19.1818181818182	33.8181818181818
12	50	30.7777777777778	19.2222222222222
13	49	26.2222222222222	22.7777777777778
14	42	19.1818181818182	22.8181818181818
15	39	30.7777777777778	8.22222222222222
16	39	132.333333333333	-93.3333333333333
17	34	26.2222222222222	7.77777777777778
18	33	26.2222222222222	6.77777777777778
19	32	19.1818181818182	12.8181818181818
20	29	30.7777777777778	-1.77777777777778
21	27	30.7777777777778	-3.77777777777778
22	25	19.1818181818182	5.81818181818182
23	23	7	16
24	22	19.1818181818182	2.81818181818182
25	21	30.7777777777778	-9.77777777777778
26	20	1.78723404255319	18.2127659574468
27	20	26.2222222222222	-6.22222222222222
28	20	5.47619047619048	14.5238095238095
29	20	26.2222222222222	-6.22222222222222
30	19	5.47619047619048	13.5238095238095
31	16	5.47619047619048	10.5238095238095
32	15	7	8
33	15	26.2222222222222	-11.2222222222222
34	14	19.1818181818182	-5.18181818181818
35	14	19.1818181818182	-5.18181818181818
36	14	26.2222222222222	-12.2222222222222
37	14	5.47619047619048	8.52380952380952
38	13	30.7777777777778	-17.7777777777778
39	10	30.7777777777778	-20.7777777777778
40	9	5.47619047619048	3.52380952380952
41	9	5.47619047619048	3.52380952380952
42	9	30.7777777777778	-21.7777777777778
43	8	26.2222222222222	-18.2222222222222
44	8	1.78723404255319	6.21276595744681
45	8	1.78723404255319	6.21276595744681
46	8	1.78723404255319	6.21276595744681
47	7	1.78723404255319	5.21276595744681
48	7	1.78723404255319	5.21276595744681
49	6	19.1818181818182	-13.1818181818182
50	5	7	-2
51	5	5.47619047619048	-0.476190476190476
52	5	5.47619047619048	-0.476190476190476
53	5	26.2222222222222	-21.2222222222222
54	5	7	-2
55	5	1.78723404255319	3.21276595744681
56	4	1.78723404255319	2.21276595744681
57	4	5.47619047619048	-1.47619047619048
58	4	26.2222222222222	-22.2222222222222
59	4	5.47619047619048	-1.47619047619048
60	3	19.1818181818182	-16.1818181818182
61	3	5.47619047619048	-2.47619047619048
62	3	26.2222222222222	-23.2222222222222
63	3	1.78723404255319	1.21276595744681
64	2	1.78723404255319	0.212765957446809
65	2	5.47619047619048	-3.47619047619048
66	2	1.78723404255319	0.212765957446809
67	2	5.47619047619048	-3.47619047619048
68	2	0.06	1.94
69	2	5.47619047619048	-3.47619047619048
70	2	1.78723404255319	0.212765957446809
71	2	1.78723404255319	0.212765957446809
72	2	1.78723404255319	0.212765957446809
73	2	1.78723404255319	0.212765957446809
74	1	0.06	0.94
75	1	5.47619047619048	-4.47619047619048
76	1	7	-6
77	1	1.78723404255319	-0.787234042553191
78	1	1.78723404255319	-0.787234042553191
79	0	1.78723404255319	-1.78723404255319
80	0	0.06	-0.06
81	0	1.78723404255319	-1.78723404255319
82	0	5.47619047619048	-5.47619047619048
83	0	26.2222222222222	-26.2222222222222
84	0	1.78723404255319	-1.78723404255319
85	0	0.06	-0.06
86	0	0.06	-0.06
87	0	1.78723404255319	-1.78723404255319
88	0	1.78723404255319	-1.78723404255319
89	0	0.06	-0.06
90	0	0.06	-0.06
91	0	0.06	-0.06
92	0	0.06	-0.06
93	0	0.06	-0.06
94	0	0.06	-0.06
95	0	0.06	-0.06
96	0	5.47619047619048	-5.47619047619048
97	0	0.06	-0.06
98	0	0.06	-0.06
99	0	1.78723404255319	-1.78723404255319
100	0	0.06	-0.06
101	0	0.06	-0.06
102	0	1.78723404255319	-1.78723404255319
103	0	26.2222222222222	-26.2222222222222
104	0	1.78723404255319	-1.78723404255319
105	0	1.78723404255319	-1.78723404255319
106	0	0.06	-0.06
107	0	0.06	-0.06
108	0	19.1818181818182	-19.1818181818182
109	0	0.06	-0.06
110	0	0.06	-0.06
111	0	0.06	-0.06
112	0	0.06	-0.06
113	0	1.78723404255319	-1.78723404255319
114	0	0.06	-0.06
115	0	0.06	-0.06
116	0	5.47619047619048	-5.47619047619048
117	0	0.06	-0.06
118	0	5.47619047619048	-5.47619047619048
119	0	1.78723404255319	-1.78723404255319
120	0	1.78723404255319	-1.78723404255319
121	0	1.78723404255319	-1.78723404255319
122	0	0.06	-0.06
123	0	1.78723404255319	-1.78723404255319
124	0	0.06	-0.06
125	0	0.06	-0.06
126	0	1.78723404255319	-1.78723404255319
127	0	5.47619047619048	-5.47619047619048
128	0	0.06	-0.06
129	0	0.06	-0.06
130	0	0.06	-0.06
131	0	0.06	-0.06
132	0	5.47619047619048	-5.47619047619048
133	0	0.06	-0.06
134	0	1.78723404255319	-1.78723404255319
135	0	1.78723404255319	-1.78723404255319
136	0	0.06	-0.06
137	0	1.78723404255319	-1.78723404255319
138	0	0.06	-0.06
139	0	0.06	-0.06
140	0	0.06	-0.06
141	0	26.2222222222222	-26.2222222222222
142	0	26.2222222222222	-26.2222222222222
143	0	1.78723404255319	-1.78723404255319
144	0	0.06	-0.06
145	0	1.78723404255319	-1.78723404255319
146	0	1.78723404255319	-1.78723404255319
147	0	26.2222222222222	-26.2222222222222
148	0	0.06	-0.06
149	0	0.06	-0.06
150	0	1.78723404255319	-1.78723404255319
151	0	1.78723404255319	-1.78723404255319
152	0	0.06	-0.06
153	0	1.78723404255319	-1.78723404255319
154	0	0.06	-0.06
155	0	0.06	-0.06
156	0	1.78723404255319	-1.78723404255319
157	0	1.78723404255319	-1.78723404255319
158	0	1.78723404255319	-1.78723404255319
159	0	7	-7
160	0	0.06	-0.06
161	0	0.06	-0.06
162	0	7	-7
163	0	0.06	-0.06
164	0	0.06	-0.06
165	0	19.1818181818182	-19.1818181818182
166	0	1.78723404255319	-1.78723404255319
167	0	1.78723404255319	-1.78723404255319
168	0	1.78723404255319	-1.78723404255319
169	0	0.06	-0.06
170	0	26.2222222222222	-26.2222222222222
171	0	0.06	-0.06
172	0	0.06	-0.06

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):

par4 <- 'no'
par3 <- '3'
par2 <- 'none'
par1 <- '1'
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