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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Fri, 24 Dec 2010 12:31:43 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/24/t12931941457snoryv3x9deqxl.htm/, Retrieved Tue, 30 Apr 2024 02:51:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114858, Retrieved Tue, 30 Apr 2024 02:51:00 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

137

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

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2010-12-13 10:44:01] [afe9379cca749d06b3d6872e02cc47ed]
-    D    [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2010-12-13 14:00:29] [afe9379cca749d06b3d6872e02cc47ed]
-    D      [Recursive Partitioning (Regression Trees)] [apple Inc - Recur...] [2010-12-14 15:16:31] [afe9379cca749d06b3d6872e02cc47ed]
-    D        [Recursive Partitioning (Regression Trees)] [Apple Inc - Recur...] [2010-12-22 09:15:38] [afe9379cca749d06b3d6872e02cc47ed]
-   PD          [Recursive Partitioning (Regression Trees)] [Paper - Recursive...] [2010-12-22 11:34:12] [1f5baf2b24e732d76900bb8178fc04e7]
-    D              [Recursive Partitioning (Regression Trees)] [Paper - Recursive...] [2010-12-24 12:31:43] [ee4a783fb13f41eb2e9bc8a0c4f26279] [Current]
-    D                [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-26 15:33:18] [eeb33d252044f8583501f5ba0605ad6d] 

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Dataseries X:

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10.81	-0.2643	0	0	24563400	24.45	2772.73	0.0373	 115.7	5.98
9.12	-0.2643	0	0	14163200	23.62	2151.83	0.0353	 109.2	5.49
11.03	-0.2643	0	0	18184800	21.90	1840.26	0.0292	 116.9	5.31
12.74	-0.1918	0	0	20810300	27.12	2116.24	0.0327	 109.9	4.8
9.98	-0.1918	0	0	12843000	27.70	2110.49	0.0362	 116.1	4.21
11.62	-0.1918	0	0	13866700	29.23	2160.54	0.0325	 118.9	3.97
9.40	-0.2246	0	0	15119200	26.50	2027.13	0.0272	 116.3	3.77
9.27	-0.2246	0	0	8301600	22.84	1805.43	0.0272	 114.0	3.65
7.76	-0.2246	0	0	14039600	20.49	1498.80	0.0265	 97.0	3.07
8.78	0.3654	0	0	12139700	23.28	1690.20	0.0213	 85.3	2.49
10.65	0.3654	0	0	9649000	25.71	1930.58	0.019	 84.9	2.09
10.95	0.3654	0	0	8513600	26.52	1950.40	0.0155	 94.6	1.82
12.36	0.0447	0	0	15278600	25.51	1934.03	0.0114	 97.8	1.73
10.85	0.0447	0	0	15590900	23.36	1731.49	0.0114	 95.0	1.74
11.84	0.0447	0	0	9691100	24.15	1845.35	0.0148	 110.7	1.73
12.14	-0.0312	0	0	10882700	20.92	1688.23	0.0164	 108.5	1.75
11.65	-0.0312	0	0	10294800	20.38	1615.73	0.0118	 110.3	1.75
8.86	-0.0312	0	0	16031900	21.90	1463.21	0.0107	 106.3	1.75
7.63	-0.0048	0	0	13683600	19.21	1328.26	0.0146	 97.4	1.73
7.38	-0.0048	0	0	8677200	19.65	1314.85	0.018	 94.5	1.74
7.25	-0.0048	0	0	9874100	17.51	1172.06	0.0151	 93.7	1.75
8.03	0.0705	0	0	10725500	21.41	1329.75	0.0203	 79.6	1.75
7.75	0.0705	0	0	8348400	23.09	1478.78	0.022	 84.9	1.34
7.16	0.0705	0	0	8046200	20.70	1335.51	0.0238	 80.7	1.24
7.18	-0.0134	0	0	10862300	19.00	1320.91	0.026	 78.8	1.24
7.51	-0.0134	0	0	8100300	19.04	1337.52	0.0298	 64.8	1.26
7.07	-0.0134	0	0	7287500	19.45	1341.17	0.0302	 61.4	1.25
7.11	0.0812	0	0	14002500	20.54	1464.31	0.0222	 81.0	1.26
8.98	0.0812	0	0	19037900	19.77	1595.91	0.0206	 83.6	1.26
9.53	0.0812	0	0	10774600	20.60	1622.80	0.0211	 83.5	1.22
10.54	0.1885	0	0	8960600	21.21	1735.02	0.0211	 77.0	1.01
11.31	0.1885	0	0	7773300	21.30	1810.45	0.0216	 81.7	1.03
10.36	0.1885	0	0	9579700	22.33	1786.94	0.0232	 77.0	1.01
11.44	0.3628	0	0	11270700	21.12	1932.21	0.0204	 81.7	1.01
10.45	0.3628	0	0	9492800	20.77	1960.26	0.0177	 92.5	1
10.69	0.3628	0	0	9136800	22.11	2003.37	0.0188	 91.7	0.98
11.28	0.2942	0	0	14487600	22.34	2066.15	0.0193	 96.4	1
11.96	0.2942	0	0	10133200	21.43	2029.82	0.0169	 88.5	1.01
13.52	0.2942	0	0	18659700	20.14	1994.22	0.0174	 88.5	1
12.89	0.3036	0	0	15980700	21.11	1920.15	0.0229	 93.0	1
14.03	0.3036	0	0	9732100	21.19	1986.74	0.0305	 93.1	1
16.27	0.3036	0	0	14626300	23.07	2047.79	0.0327	 102.8	1.03
16.17	0.3703	0	0	16904000	23.01	1887.36	0.0299	 105.7	1.26
17.25	0.3703	0	0	13616700	22.12	1838.10	0.0265	 98.7	1.43
19.38	0.3703	0	0	13772900	22.40	1896.84	0.0254	 96.7	1.61
26.20	0.7398	0	0	28749200	22.66	1974.99	0.0319	 92.9	1.76
33.53	0.7398	0	0	31408300	24.21	2096.81	0.0352	 92.6	1.93
32.20	0.7398	0	0	26342800	24.13	2175.44	0.0326	 102.7	2.16
38.45	0.6988	0	0	48909500	23.73	2062.41	0.0297	 105.1	2.28
44.86	0.6988	0	0	41542400	22.79	2051.72	0.0301	 104.4	2.5
41.67	0.6988	0	0	24857200	21.89	1999.23	0.0315	 103.0	2.63
36.06	0.7478	0	0	34093700	22.92	1921.65	0.0351	 97.5	2.79
39.76	0.7478	0	0	22555200	23.44	2068.22	0.028	 103.1	3
36.81	0.7478	0	0	19067500	22.57	2056.96	0.0253	 106.2	3.04
42.65	0.5651	0	0	19029100	23.27	2184.83	0.0317	 103.6	3.26
46.89	0.5651	0	0	15223200	24.95	2152.09	0.0364	 105.5	3.5
53.61	0.5651	0	0	21903700	23.45	2151.69	0.0469	 87.5	3.62
57.59	0.6473	0	0	33306600	23.42	2120.30	0.0435	 85.2	3.78
67.82	0.6473	0	0	23898100	25.30	2232.82	0.0346	 98.3	4
71.89	0.6473	0	0	23279600	23.90	2205.32	0.0342	 103.8	4.16
75.51	0.3441	0	0	40699800	25.73	2305.82	0.0399	 106.8	4.29
68.49	0.3441	0	0	37646000	24.64	2281.39	0.036	 102.7	4.49
62.72	0.3441	0	0	37277000	24.95	2339.79	0.0336	 107.5	4.59
70.39	0.2415	0	0	39246800	22.15	2322.57	0.0355	 109.8	4.79
59.77	0.2415	0	0	27418400	20.85	2178.88	0.0417	 104.7	4.94
57.27	0.2415	0	0	30318700	21.45	2172.09	0.0432	 105.7	4.99
67.96	0.3151	0	0	32808100	22.15	2091.47	0.0415	 107.0	5.24
67.85	0.3151	0	0	28668200	23.75	2183.75	0.0382	 100.2	5.25
76.98	0.3151	0	0	32370300	25.27	2258.43	0.0206	 105.9	5.25
81.08	0.239	0	0	24171100	26.53	2366.71	0.0131	 105.1	5.25
91.66	0.239	0	0	25009100	27.22	2431.77	0.0197	 105.3	5.25
84.84	0.239	0	0	32084300	27.69	2415.29	0.0254	 110.0	5.24
85.73	0.2127	0	0	50117500	28.61	2463.93	0.0208	 110.2	5.25
84.61	0.2127	0	0	27522200	26.21	2416.15	0.0242	 111.2	5.26
92.91	0.2127	0	0	26816800	25.93	2421.64	0.0278	 108.2	5.26
99.80	0.273	0	0	25136100	27.86	2525.09	0.0257	 106.3	5.25
121.19	0.273	0	0	30295600	28.65	2604.52	0.0269	 108.5	5.25
122.04	0.273	0.273	0	41526100	27.51	2603.23	0.0269	 105.3	5.25
131.76	0.3657	0.3657	0	43845100	27.06	2546.27	0.0236	 111.9	5.26
138.48	0.3657	0.3657	0	39188900	26.91	2596.36	0.0197	 105.6	5.02
153.47	0.3657	0.3657	0	40496400	27.60	2701.50	0.0276	 99.5	4.94
189.95	0.4643	0.4643	0	37438400	34.48	2859.12	0.0354	 95.2	4.76
182.22	0.4643	0.4643	0	46553700	31.58	2660.96	0.0431	 87.8	4.49
198.08	0.4643	0.4643	0	31771400	33.46	2652.28	0.0408	 90.6	4.24
135.36	0.5096	0.5096	0	62108100	30.64	2389.86	0.0428	 87.9	3.94
125.02	0.5096	0.5096	0	46645400	25.66	2271.48	0.0403	 76.4	2.98
143.50	0.5096	0.5096	0	42313100	26.78	2279.10	0.0398	 65.9	2.61
173.95	0.3592	0.3592	0	38841700	26.91	2412.80	0.0394	 62.3	2.28
188.75	0.3592	0.3592	0	32650300	26.82	2522.66	0.0418	 57.2	1.98
167.44	0.3592	0.3592	0	34281100	26.05	2292.98	0.0502	 50.4	2
158.95	0.7439	0.7439	0	33096200	24.36	2325.55	0.056	 51.9	2.01
169.53	0.7439	0.7439	0	23273800	25.94	2367.52	0.0537	 58.5	2
113.66	0.7439	0.7439	0	43697600	25.37	2091.88	0.0494	 61.4	1.81
107.59	0.139	0.139	0	66902300	21.23	1720.95	0.0366	 38.8	0.97
92.67	0.139	0.139	0	44957200	19.35	1535.57	0.0107	 44.9	0.39
85.35	0.139	0.139	0	33800900	18.61	1577.03	0.0009	 38.6	0.16
90.13	0.1383	0.1383	0	33487900	16.37	1476.42	0.0003	 4.0	0.15
89.31	0.1383	0.1383	0	27394900	15.56	1377.84	0.0024	 25.3	0.22
105.12	0.1383	0.1383	0	25963400	17.70	1528.59	-0.0038	 26.9	0.18
125.83	0.2874	0.2874	0	20952600	19.52	1717.30	-0.0074	 40.8	0.15
135.81	0.2874	0.2874	0	17702900	20.26	1774.33	-0.0128	 54.8	0.18
142.43	0.2874	0.2874	0	21282100	23.05	1835.04	-0.0143	 49.3	0.21
163.39	0.0596	0.0596	0	18449100	22.81	1978.50	-0.021	 47.4	0.16
168.21	0.0596	0.0596	0	14415700	24.04	2009.06	-0.0148	 54.5	0.16
185.35	0.0596	0.0596	0	17906300	25.08	2122.42	-0.0129	 53.4	0.15
188.50	0.3201	0.3201	0	22197500	27.04	2045.11	-0.0018	 48.7	0.12
199.91	0.3201	0.3201	0	15856500	28.81	2144.60	0.0184	 50.6	0.12
210.73	0.3201	0.3201	0	19068700	29.86	2269.15	0.0272	 53.6	0.12
192.06	0.486	0.486	0	30855100	27.61	2147.35	0.0263	 56.5	0.11
204.62	0.486	0.486	0	21209000	28.22	2238.26	0.0214	 46.4	0.13
235.00	0.486	0.486	0	19541600	28.83	2397.96	0.0231	 52.3	0.16
261.09	0.6129	0.6129	0.6129	21955000	30.06	2461.19	0.0224	 57.7	0.2
256.88	0.6129	0.6129	0.6129	33725900	25.51	2257.04	0.0202	 62.7	0.2
251.53	0.6129	0.6129	0.6129	28192800	22.75	2109.24	0.0105	 54.3	0.18
257.25	0.6665	0.6665	0.6665	27377000	25.52	2254.70	0.0124	 51.0	0.18
243.10	0.6665	0.6665	0.6665	16228100	23.33	2114.03	0.0115	 53.2	0.19
283.75	0.6665	0.6665	0.6665	21278900	24.34	2368.62	0.0114	 48.6	0.19

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114858&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114858&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114858&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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Goodness of Fit
Correlation	0.9626
R-squared	0.9266
RMSE	20.5002

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9626 \tabularnewline
R-squared & 0.9266 \tabularnewline
RMSE & 20.5002 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114858&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9626[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9266[/C][/ROW]
[ROW][C]RMSE[/C][C]20.5002[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114858&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114858&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	0.9626
R-squared	0.9266
RMSE	20.5002

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	10.81	49.5305263157895	-38.7205263157895
2	9.12	11.4508	-2.3308
3	11.03	11.4508	-0.4208
4	12.74	11.4508	1.2892
5	9.98	11.4508	-1.4708
6	11.62	11.4508	0.1692
7	9.4	11.4508	-2.0508
8	9.27	11.4508	-2.1808
9	7.76	7.66692307692308	0.0930769230769215
10	8.78	23.2811111111111	-14.5011111111111
11	10.65	23.2811111111111	-12.6311111111111
12	10.95	23.2811111111111	-12.3311111111111
13	12.36	11.4508	0.9092
14	10.85	11.4508	-0.6008
15	11.84	11.4508	0.389200000000001
16	12.14	11.4508	0.689200000000001
17	11.65	11.4508	0.199200000000001
18	8.86	7.66692307692308	1.19307692307692
19	7.63	7.66692307692308	-0.0369230769230784
20	7.38	7.66692307692308	-0.286923076923078
21	7.25	7.66692307692308	-0.416923076923078
22	8.03	7.66692307692308	0.363076923076921
23	7.75	7.66692307692308	0.0830769230769217
24	7.16	7.66692307692308	-0.506923076923078
25	7.18	7.66692307692308	-0.486923076923079
26	7.51	7.66692307692308	-0.156923076923078
27	7.07	7.66692307692308	-0.596923076923078
28	7.11	7.66692307692308	-0.556923076923078
29	8.98	7.66692307692308	1.31307692307692
30	9.53	11.4508	-1.9208
31	10.54	11.4508	-0.9108
32	11.31	11.4508	-0.140799999999999
33	10.36	11.4508	-1.0908
34	11.44	11.4508	-0.0107999999999997
35	10.45	11.4508	-1.0008
36	10.69	11.4508	-0.7608
37	11.28	11.4508	-0.1708
38	11.96	11.4508	0.509200000000002
39	13.52	11.4508	2.0692
40	12.89	11.4508	1.4392
41	14.03	11.4508	2.5792
42	16.27	11.4508	4.8192
43	16.17	23.2811111111111	-7.11111111111111
44	17.25	23.2811111111111	-6.03111111111111
45	19.38	23.2811111111111	-3.90111111111111
46	26.2	49.5305263157895	-23.3305263157895
47	33.53	49.5305263157895	-16.0005263157895
48	32.2	49.5305263157895	-17.3305263157895
49	38.45	49.5305263157895	-11.0805263157895
50	44.86	49.5305263157895	-4.67052631578947
51	41.67	49.5305263157895	-7.86052631578947
52	36.06	49.5305263157895	-13.4705263157895
53	39.76	49.5305263157895	-9.77052631578947
54	36.81	23.2811111111111	13.5288888888889
55	42.65	23.2811111111111	19.3688888888889
56	46.89	23.2811111111111	23.6088888888889
57	53.61	49.5305263157895	4.07947368421053
58	57.59	49.5305263157895	8.05947368421053
59	67.82	87.4663636363636	-19.6463636363636
60	71.89	49.5305263157895	22.3594736842105
61	75.51	87.4663636363636	-11.9563636363636
62	68.49	49.5305263157895	18.9594736842105
63	62.72	49.5305263157895	13.1894736842105
64	70.39	49.5305263157895	20.8594736842105
65	59.77	49.5305263157895	10.2394736842105
66	57.27	49.5305263157895	7.73947368421053
67	67.96	49.5305263157895	18.4294736842105
68	67.85	49.5305263157895	18.3194736842105
69	76.98	87.4663636363636	-10.4863636363636
70	81.08	87.4663636363636	-6.38636363636363
71	91.66	87.4663636363636	4.19363636363637
72	84.84	87.4663636363636	-2.62636363636362
73	85.73	87.4663636363636	-1.73636363636362
74	84.61	87.4663636363636	-2.85636363636362
75	92.91	87.4663636363636	5.44363636363637
76	99.8	87.4663636363636	12.3336363636364
77	121.19	87.4663636363636	33.7236363636364
78	122.04	124.094615384615	-2.05461538461537
79	131.76	150.437272727273	-18.6772727272728
80	138.48	150.437272727273	-11.9572727272728
81	153.47	150.437272727273	3.03272727272724
82	189.95	150.437272727273	39.5127272727272
83	182.22	150.437272727273	31.7827272727272
84	198.08	218.733125	-20.653125
85	135.36	150.437272727273	-15.0772727272727
86	125.02	150.437272727273	-25.4172727272728
87	143.5	150.437272727273	-6.93727272727276
88	173.95	150.437272727273	23.5127272727272
89	188.75	218.733125	-29.983125
90	167.44	150.437272727273	17.0027272727272
91	158.95	218.733125	-59.783125
92	169.53	218.733125	-49.203125
93	113.66	150.437272727273	-36.7772727272728
94	107.59	124.094615384615	-16.5046153846154
95	92.67	124.094615384615	-31.4246153846154
96	85.35	124.094615384615	-38.7446153846154
97	90.13	124.094615384615	-33.9646153846154
98	89.31	124.094615384615	-34.7846153846154
99	105.12	124.094615384615	-18.9746153846154
100	125.83	124.094615384615	1.73538461538462
101	135.81	124.094615384615	11.7153846153846
102	142.43	124.094615384615	18.3353846153846
103	163.39	124.094615384615	39.2953846153846
104	168.21	124.094615384615	44.1153846153846
105	185.35	124.094615384615	61.2553846153846
106	188.5	218.733125	-30.233125
107	199.91	218.733125	-18.8231250000000
108	210.73	218.733125	-8.00312500000004
109	192.06	218.733125	-26.6731250000000
110	204.62	218.733125	-14.1131250000000
111	235	218.733125	16.2668750000000
112	261.09	218.733125	42.3568749999999
113	256.88	218.733125	38.146875
114	251.53	218.733125	32.796875
115	257.25	218.733125	38.516875
116	243.1	218.733125	24.3668750000000
117	283.75	218.733125	65.016875

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 10.81 & 49.5305263157895 & -38.7205263157895 \tabularnewline
2 & 9.12 & 11.4508 & -2.3308 \tabularnewline
3 & 11.03 & 11.4508 & -0.4208 \tabularnewline
4 & 12.74 & 11.4508 & 1.2892 \tabularnewline
5 & 9.98 & 11.4508 & -1.4708 \tabularnewline
6 & 11.62 & 11.4508 & 0.1692 \tabularnewline
7 & 9.4 & 11.4508 & -2.0508 \tabularnewline
8 & 9.27 & 11.4508 & -2.1808 \tabularnewline
9 & 7.76 & 7.66692307692308 & 0.0930769230769215 \tabularnewline
10 & 8.78 & 23.2811111111111 & -14.5011111111111 \tabularnewline
11 & 10.65 & 23.2811111111111 & -12.6311111111111 \tabularnewline
12 & 10.95 & 23.2811111111111 & -12.3311111111111 \tabularnewline
13 & 12.36 & 11.4508 & 0.9092 \tabularnewline
14 & 10.85 & 11.4508 & -0.6008 \tabularnewline
15 & 11.84 & 11.4508 & 0.389200000000001 \tabularnewline
16 & 12.14 & 11.4508 & 0.689200000000001 \tabularnewline
17 & 11.65 & 11.4508 & 0.199200000000001 \tabularnewline
18 & 8.86 & 7.66692307692308 & 1.19307692307692 \tabularnewline
19 & 7.63 & 7.66692307692308 & -0.0369230769230784 \tabularnewline
20 & 7.38 & 7.66692307692308 & -0.286923076923078 \tabularnewline
21 & 7.25 & 7.66692307692308 & -0.416923076923078 \tabularnewline
22 & 8.03 & 7.66692307692308 & 0.363076923076921 \tabularnewline
23 & 7.75 & 7.66692307692308 & 0.0830769230769217 \tabularnewline
24 & 7.16 & 7.66692307692308 & -0.506923076923078 \tabularnewline
25 & 7.18 & 7.66692307692308 & -0.486923076923079 \tabularnewline
26 & 7.51 & 7.66692307692308 & -0.156923076923078 \tabularnewline
27 & 7.07 & 7.66692307692308 & -0.596923076923078 \tabularnewline
28 & 7.11 & 7.66692307692308 & -0.556923076923078 \tabularnewline
29 & 8.98 & 7.66692307692308 & 1.31307692307692 \tabularnewline
30 & 9.53 & 11.4508 & -1.9208 \tabularnewline
31 & 10.54 & 11.4508 & -0.9108 \tabularnewline
32 & 11.31 & 11.4508 & -0.140799999999999 \tabularnewline
33 & 10.36 & 11.4508 & -1.0908 \tabularnewline
34 & 11.44 & 11.4508 & -0.0107999999999997 \tabularnewline
35 & 10.45 & 11.4508 & -1.0008 \tabularnewline
36 & 10.69 & 11.4508 & -0.7608 \tabularnewline
37 & 11.28 & 11.4508 & -0.1708 \tabularnewline
38 & 11.96 & 11.4508 & 0.509200000000002 \tabularnewline
39 & 13.52 & 11.4508 & 2.0692 \tabularnewline
40 & 12.89 & 11.4508 & 1.4392 \tabularnewline
41 & 14.03 & 11.4508 & 2.5792 \tabularnewline
42 & 16.27 & 11.4508 & 4.8192 \tabularnewline
43 & 16.17 & 23.2811111111111 & -7.11111111111111 \tabularnewline
44 & 17.25 & 23.2811111111111 & -6.03111111111111 \tabularnewline
45 & 19.38 & 23.2811111111111 & -3.90111111111111 \tabularnewline
46 & 26.2 & 49.5305263157895 & -23.3305263157895 \tabularnewline
47 & 33.53 & 49.5305263157895 & -16.0005263157895 \tabularnewline
48 & 32.2 & 49.5305263157895 & -17.3305263157895 \tabularnewline
49 & 38.45 & 49.5305263157895 & -11.0805263157895 \tabularnewline
50 & 44.86 & 49.5305263157895 & -4.67052631578947 \tabularnewline
51 & 41.67 & 49.5305263157895 & -7.86052631578947 \tabularnewline
52 & 36.06 & 49.5305263157895 & -13.4705263157895 \tabularnewline
53 & 39.76 & 49.5305263157895 & -9.77052631578947 \tabularnewline
54 & 36.81 & 23.2811111111111 & 13.5288888888889 \tabularnewline
55 & 42.65 & 23.2811111111111 & 19.3688888888889 \tabularnewline
56 & 46.89 & 23.2811111111111 & 23.6088888888889 \tabularnewline
57 & 53.61 & 49.5305263157895 & 4.07947368421053 \tabularnewline
58 & 57.59 & 49.5305263157895 & 8.05947368421053 \tabularnewline
59 & 67.82 & 87.4663636363636 & -19.6463636363636 \tabularnewline
60 & 71.89 & 49.5305263157895 & 22.3594736842105 \tabularnewline
61 & 75.51 & 87.4663636363636 & -11.9563636363636 \tabularnewline
62 & 68.49 & 49.5305263157895 & 18.9594736842105 \tabularnewline
63 & 62.72 & 49.5305263157895 & 13.1894736842105 \tabularnewline
64 & 70.39 & 49.5305263157895 & 20.8594736842105 \tabularnewline
65 & 59.77 & 49.5305263157895 & 10.2394736842105 \tabularnewline
66 & 57.27 & 49.5305263157895 & 7.73947368421053 \tabularnewline
67 & 67.96 & 49.5305263157895 & 18.4294736842105 \tabularnewline
68 & 67.85 & 49.5305263157895 & 18.3194736842105 \tabularnewline
69 & 76.98 & 87.4663636363636 & -10.4863636363636 \tabularnewline
70 & 81.08 & 87.4663636363636 & -6.38636363636363 \tabularnewline
71 & 91.66 & 87.4663636363636 & 4.19363636363637 \tabularnewline
72 & 84.84 & 87.4663636363636 & -2.62636363636362 \tabularnewline
73 & 85.73 & 87.4663636363636 & -1.73636363636362 \tabularnewline
74 & 84.61 & 87.4663636363636 & -2.85636363636362 \tabularnewline
75 & 92.91 & 87.4663636363636 & 5.44363636363637 \tabularnewline
76 & 99.8 & 87.4663636363636 & 12.3336363636364 \tabularnewline
77 & 121.19 & 87.4663636363636 & 33.7236363636364 \tabularnewline
78 & 122.04 & 124.094615384615 & -2.05461538461537 \tabularnewline
79 & 131.76 & 150.437272727273 & -18.6772727272728 \tabularnewline
80 & 138.48 & 150.437272727273 & -11.9572727272728 \tabularnewline
81 & 153.47 & 150.437272727273 & 3.03272727272724 \tabularnewline
82 & 189.95 & 150.437272727273 & 39.5127272727272 \tabularnewline
83 & 182.22 & 150.437272727273 & 31.7827272727272 \tabularnewline
84 & 198.08 & 218.733125 & -20.653125 \tabularnewline
85 & 135.36 & 150.437272727273 & -15.0772727272727 \tabularnewline
86 & 125.02 & 150.437272727273 & -25.4172727272728 \tabularnewline
87 & 143.5 & 150.437272727273 & -6.93727272727276 \tabularnewline
88 & 173.95 & 150.437272727273 & 23.5127272727272 \tabularnewline
89 & 188.75 & 218.733125 & -29.983125 \tabularnewline
90 & 167.44 & 150.437272727273 & 17.0027272727272 \tabularnewline
91 & 158.95 & 218.733125 & -59.783125 \tabularnewline
92 & 169.53 & 218.733125 & -49.203125 \tabularnewline
93 & 113.66 & 150.437272727273 & -36.7772727272728 \tabularnewline
94 & 107.59 & 124.094615384615 & -16.5046153846154 \tabularnewline
95 & 92.67 & 124.094615384615 & -31.4246153846154 \tabularnewline
96 & 85.35 & 124.094615384615 & -38.7446153846154 \tabularnewline
97 & 90.13 & 124.094615384615 & -33.9646153846154 \tabularnewline
98 & 89.31 & 124.094615384615 & -34.7846153846154 \tabularnewline
99 & 105.12 & 124.094615384615 & -18.9746153846154 \tabularnewline
100 & 125.83 & 124.094615384615 & 1.73538461538462 \tabularnewline
101 & 135.81 & 124.094615384615 & 11.7153846153846 \tabularnewline
102 & 142.43 & 124.094615384615 & 18.3353846153846 \tabularnewline
103 & 163.39 & 124.094615384615 & 39.2953846153846 \tabularnewline
104 & 168.21 & 124.094615384615 & 44.1153846153846 \tabularnewline
105 & 185.35 & 124.094615384615 & 61.2553846153846 \tabularnewline
106 & 188.5 & 218.733125 & -30.233125 \tabularnewline
107 & 199.91 & 218.733125 & -18.8231250000000 \tabularnewline
108 & 210.73 & 218.733125 & -8.00312500000004 \tabularnewline
109 & 192.06 & 218.733125 & -26.6731250000000 \tabularnewline
110 & 204.62 & 218.733125 & -14.1131250000000 \tabularnewline
111 & 235 & 218.733125 & 16.2668750000000 \tabularnewline
112 & 261.09 & 218.733125 & 42.3568749999999 \tabularnewline
113 & 256.88 & 218.733125 & 38.146875 \tabularnewline
114 & 251.53 & 218.733125 & 32.796875 \tabularnewline
115 & 257.25 & 218.733125 & 38.516875 \tabularnewline
116 & 243.1 & 218.733125 & 24.3668750000000 \tabularnewline
117 & 283.75 & 218.733125 & 65.016875 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114858&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]10.81[/C][C]49.5305263157895[/C][C]-38.7205263157895[/C][/ROW]
[ROW][C]2[/C][C]9.12[/C][C]11.4508[/C][C]-2.3308[/C][/ROW]
[ROW][C]3[/C][C]11.03[/C][C]11.4508[/C][C]-0.4208[/C][/ROW]
[ROW][C]4[/C][C]12.74[/C][C]11.4508[/C][C]1.2892[/C][/ROW]
[ROW][C]5[/C][C]9.98[/C][C]11.4508[/C][C]-1.4708[/C][/ROW]
[ROW][C]6[/C][C]11.62[/C][C]11.4508[/C][C]0.1692[/C][/ROW]
[ROW][C]7[/C][C]9.4[/C][C]11.4508[/C][C]-2.0508[/C][/ROW]
[ROW][C]8[/C][C]9.27[/C][C]11.4508[/C][C]-2.1808[/C][/ROW]
[ROW][C]9[/C][C]7.76[/C][C]7.66692307692308[/C][C]0.0930769230769215[/C][/ROW]
[ROW][C]10[/C][C]8.78[/C][C]23.2811111111111[/C][C]-14.5011111111111[/C][/ROW]
[ROW][C]11[/C][C]10.65[/C][C]23.2811111111111[/C][C]-12.6311111111111[/C][/ROW]
[ROW][C]12[/C][C]10.95[/C][C]23.2811111111111[/C][C]-12.3311111111111[/C][/ROW]
[ROW][C]13[/C][C]12.36[/C][C]11.4508[/C][C]0.9092[/C][/ROW]
[ROW][C]14[/C][C]10.85[/C][C]11.4508[/C][C]-0.6008[/C][/ROW]
[ROW][C]15[/C][C]11.84[/C][C]11.4508[/C][C]0.389200000000001[/C][/ROW]
[ROW][C]16[/C][C]12.14[/C][C]11.4508[/C][C]0.689200000000001[/C][/ROW]
[ROW][C]17[/C][C]11.65[/C][C]11.4508[/C][C]0.199200000000001[/C][/ROW]
[ROW][C]18[/C][C]8.86[/C][C]7.66692307692308[/C][C]1.19307692307692[/C][/ROW]
[ROW][C]19[/C][C]7.63[/C][C]7.66692307692308[/C][C]-0.0369230769230784[/C][/ROW]
[ROW][C]20[/C][C]7.38[/C][C]7.66692307692308[/C][C]-0.286923076923078[/C][/ROW]
[ROW][C]21[/C][C]7.25[/C][C]7.66692307692308[/C][C]-0.416923076923078[/C][/ROW]
[ROW][C]22[/C][C]8.03[/C][C]7.66692307692308[/C][C]0.363076923076921[/C][/ROW]
[ROW][C]23[/C][C]7.75[/C][C]7.66692307692308[/C][C]0.0830769230769217[/C][/ROW]
[ROW][C]24[/C][C]7.16[/C][C]7.66692307692308[/C][C]-0.506923076923078[/C][/ROW]
[ROW][C]25[/C][C]7.18[/C][C]7.66692307692308[/C][C]-0.486923076923079[/C][/ROW]
[ROW][C]26[/C][C]7.51[/C][C]7.66692307692308[/C][C]-0.156923076923078[/C][/ROW]
[ROW][C]27[/C][C]7.07[/C][C]7.66692307692308[/C][C]-0.596923076923078[/C][/ROW]
[ROW][C]28[/C][C]7.11[/C][C]7.66692307692308[/C][C]-0.556923076923078[/C][/ROW]
[ROW][C]29[/C][C]8.98[/C][C]7.66692307692308[/C][C]1.31307692307692[/C][/ROW]
[ROW][C]30[/C][C]9.53[/C][C]11.4508[/C][C]-1.9208[/C][/ROW]
[ROW][C]31[/C][C]10.54[/C][C]11.4508[/C][C]-0.9108[/C][/ROW]
[ROW][C]32[/C][C]11.31[/C][C]11.4508[/C][C]-0.140799999999999[/C][/ROW]
[ROW][C]33[/C][C]10.36[/C][C]11.4508[/C][C]-1.0908[/C][/ROW]
[ROW][C]34[/C][C]11.44[/C][C]11.4508[/C][C]-0.0107999999999997[/C][/ROW]
[ROW][C]35[/C][C]10.45[/C][C]11.4508[/C][C]-1.0008[/C][/ROW]
[ROW][C]36[/C][C]10.69[/C][C]11.4508[/C][C]-0.7608[/C][/ROW]
[ROW][C]37[/C][C]11.28[/C][C]11.4508[/C][C]-0.1708[/C][/ROW]
[ROW][C]38[/C][C]11.96[/C][C]11.4508[/C][C]0.509200000000002[/C][/ROW]
[ROW][C]39[/C][C]13.52[/C][C]11.4508[/C][C]2.0692[/C][/ROW]
[ROW][C]40[/C][C]12.89[/C][C]11.4508[/C][C]1.4392[/C][/ROW]
[ROW][C]41[/C][C]14.03[/C][C]11.4508[/C][C]2.5792[/C][/ROW]
[ROW][C]42[/C][C]16.27[/C][C]11.4508[/C][C]4.8192[/C][/ROW]
[ROW][C]43[/C][C]16.17[/C][C]23.2811111111111[/C][C]-7.11111111111111[/C][/ROW]
[ROW][C]44[/C][C]17.25[/C][C]23.2811111111111[/C][C]-6.03111111111111[/C][/ROW]
[ROW][C]45[/C][C]19.38[/C][C]23.2811111111111[/C][C]-3.90111111111111[/C][/ROW]
[ROW][C]46[/C][C]26.2[/C][C]49.5305263157895[/C][C]-23.3305263157895[/C][/ROW]
[ROW][C]47[/C][C]33.53[/C][C]49.5305263157895[/C][C]-16.0005263157895[/C][/ROW]
[ROW][C]48[/C][C]32.2[/C][C]49.5305263157895[/C][C]-17.3305263157895[/C][/ROW]
[ROW][C]49[/C][C]38.45[/C][C]49.5305263157895[/C][C]-11.0805263157895[/C][/ROW]
[ROW][C]50[/C][C]44.86[/C][C]49.5305263157895[/C][C]-4.67052631578947[/C][/ROW]
[ROW][C]51[/C][C]41.67[/C][C]49.5305263157895[/C][C]-7.86052631578947[/C][/ROW]
[ROW][C]52[/C][C]36.06[/C][C]49.5305263157895[/C][C]-13.4705263157895[/C][/ROW]
[ROW][C]53[/C][C]39.76[/C][C]49.5305263157895[/C][C]-9.77052631578947[/C][/ROW]
[ROW][C]54[/C][C]36.81[/C][C]23.2811111111111[/C][C]13.5288888888889[/C][/ROW]
[ROW][C]55[/C][C]42.65[/C][C]23.2811111111111[/C][C]19.3688888888889[/C][/ROW]
[ROW][C]56[/C][C]46.89[/C][C]23.2811111111111[/C][C]23.6088888888889[/C][/ROW]
[ROW][C]57[/C][C]53.61[/C][C]49.5305263157895[/C][C]4.07947368421053[/C][/ROW]
[ROW][C]58[/C][C]57.59[/C][C]49.5305263157895[/C][C]8.05947368421053[/C][/ROW]
[ROW][C]59[/C][C]67.82[/C][C]87.4663636363636[/C][C]-19.6463636363636[/C][/ROW]
[ROW][C]60[/C][C]71.89[/C][C]49.5305263157895[/C][C]22.3594736842105[/C][/ROW]
[ROW][C]61[/C][C]75.51[/C][C]87.4663636363636[/C][C]-11.9563636363636[/C][/ROW]
[ROW][C]62[/C][C]68.49[/C][C]49.5305263157895[/C][C]18.9594736842105[/C][/ROW]
[ROW][C]63[/C][C]62.72[/C][C]49.5305263157895[/C][C]13.1894736842105[/C][/ROW]
[ROW][C]64[/C][C]70.39[/C][C]49.5305263157895[/C][C]20.8594736842105[/C][/ROW]
[ROW][C]65[/C][C]59.77[/C][C]49.5305263157895[/C][C]10.2394736842105[/C][/ROW]
[ROW][C]66[/C][C]57.27[/C][C]49.5305263157895[/C][C]7.73947368421053[/C][/ROW]
[ROW][C]67[/C][C]67.96[/C][C]49.5305263157895[/C][C]18.4294736842105[/C][/ROW]
[ROW][C]68[/C][C]67.85[/C][C]49.5305263157895[/C][C]18.3194736842105[/C][/ROW]
[ROW][C]69[/C][C]76.98[/C][C]87.4663636363636[/C][C]-10.4863636363636[/C][/ROW]
[ROW][C]70[/C][C]81.08[/C][C]87.4663636363636[/C][C]-6.38636363636363[/C][/ROW]
[ROW][C]71[/C][C]91.66[/C][C]87.4663636363636[/C][C]4.19363636363637[/C][/ROW]
[ROW][C]72[/C][C]84.84[/C][C]87.4663636363636[/C][C]-2.62636363636362[/C][/ROW]
[ROW][C]73[/C][C]85.73[/C][C]87.4663636363636[/C][C]-1.73636363636362[/C][/ROW]
[ROW][C]74[/C][C]84.61[/C][C]87.4663636363636[/C][C]-2.85636363636362[/C][/ROW]
[ROW][C]75[/C][C]92.91[/C][C]87.4663636363636[/C][C]5.44363636363637[/C][/ROW]
[ROW][C]76[/C][C]99.8[/C][C]87.4663636363636[/C][C]12.3336363636364[/C][/ROW]
[ROW][C]77[/C][C]121.19[/C][C]87.4663636363636[/C][C]33.7236363636364[/C][/ROW]
[ROW][C]78[/C][C]122.04[/C][C]124.094615384615[/C][C]-2.05461538461537[/C][/ROW]
[ROW][C]79[/C][C]131.76[/C][C]150.437272727273[/C][C]-18.6772727272728[/C][/ROW]
[ROW][C]80[/C][C]138.48[/C][C]150.437272727273[/C][C]-11.9572727272728[/C][/ROW]
[ROW][C]81[/C][C]153.47[/C][C]150.437272727273[/C][C]3.03272727272724[/C][/ROW]
[ROW][C]82[/C][C]189.95[/C][C]150.437272727273[/C][C]39.5127272727272[/C][/ROW]
[ROW][C]83[/C][C]182.22[/C][C]150.437272727273[/C][C]31.7827272727272[/C][/ROW]
[ROW][C]84[/C][C]198.08[/C][C]218.733125[/C][C]-20.653125[/C][/ROW]
[ROW][C]85[/C][C]135.36[/C][C]150.437272727273[/C][C]-15.0772727272727[/C][/ROW]
[ROW][C]86[/C][C]125.02[/C][C]150.437272727273[/C][C]-25.4172727272728[/C][/ROW]
[ROW][C]87[/C][C]143.5[/C][C]150.437272727273[/C][C]-6.93727272727276[/C][/ROW]
[ROW][C]88[/C][C]173.95[/C][C]150.437272727273[/C][C]23.5127272727272[/C][/ROW]
[ROW][C]89[/C][C]188.75[/C][C]218.733125[/C][C]-29.983125[/C][/ROW]
[ROW][C]90[/C][C]167.44[/C][C]150.437272727273[/C][C]17.0027272727272[/C][/ROW]
[ROW][C]91[/C][C]158.95[/C][C]218.733125[/C][C]-59.783125[/C][/ROW]
[ROW][C]92[/C][C]169.53[/C][C]218.733125[/C][C]-49.203125[/C][/ROW]
[ROW][C]93[/C][C]113.66[/C][C]150.437272727273[/C][C]-36.7772727272728[/C][/ROW]
[ROW][C]94[/C][C]107.59[/C][C]124.094615384615[/C][C]-16.5046153846154[/C][/ROW]
[ROW][C]95[/C][C]92.67[/C][C]124.094615384615[/C][C]-31.4246153846154[/C][/ROW]
[ROW][C]96[/C][C]85.35[/C][C]124.094615384615[/C][C]-38.7446153846154[/C][/ROW]
[ROW][C]97[/C][C]90.13[/C][C]124.094615384615[/C][C]-33.9646153846154[/C][/ROW]
[ROW][C]98[/C][C]89.31[/C][C]124.094615384615[/C][C]-34.7846153846154[/C][/ROW]
[ROW][C]99[/C][C]105.12[/C][C]124.094615384615[/C][C]-18.9746153846154[/C][/ROW]
[ROW][C]100[/C][C]125.83[/C][C]124.094615384615[/C][C]1.73538461538462[/C][/ROW]
[ROW][C]101[/C][C]135.81[/C][C]124.094615384615[/C][C]11.7153846153846[/C][/ROW]
[ROW][C]102[/C][C]142.43[/C][C]124.094615384615[/C][C]18.3353846153846[/C][/ROW]
[ROW][C]103[/C][C]163.39[/C][C]124.094615384615[/C][C]39.2953846153846[/C][/ROW]
[ROW][C]104[/C][C]168.21[/C][C]124.094615384615[/C][C]44.1153846153846[/C][/ROW]
[ROW][C]105[/C][C]185.35[/C][C]124.094615384615[/C][C]61.2553846153846[/C][/ROW]
[ROW][C]106[/C][C]188.5[/C][C]218.733125[/C][C]-30.233125[/C][/ROW]
[ROW][C]107[/C][C]199.91[/C][C]218.733125[/C][C]-18.8231250000000[/C][/ROW]
[ROW][C]108[/C][C]210.73[/C][C]218.733125[/C][C]-8.00312500000004[/C][/ROW]
[ROW][C]109[/C][C]192.06[/C][C]218.733125[/C][C]-26.6731250000000[/C][/ROW]
[ROW][C]110[/C][C]204.62[/C][C]218.733125[/C][C]-14.1131250000000[/C][/ROW]
[ROW][C]111[/C][C]235[/C][C]218.733125[/C][C]16.2668750000000[/C][/ROW]
[ROW][C]112[/C][C]261.09[/C][C]218.733125[/C][C]42.3568749999999[/C][/ROW]
[ROW][C]113[/C][C]256.88[/C][C]218.733125[/C][C]38.146875[/C][/ROW]
[ROW][C]114[/C][C]251.53[/C][C]218.733125[/C][C]32.796875[/C][/ROW]
[ROW][C]115[/C][C]257.25[/C][C]218.733125[/C][C]38.516875[/C][/ROW]
[ROW][C]116[/C][C]243.1[/C][C]218.733125[/C][C]24.3668750000000[/C][/ROW]
[ROW][C]117[/C][C]283.75[/C][C]218.733125[/C][C]65.016875[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114858&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114858&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	10.81	49.5305263157895	-38.7205263157895
2	9.12	11.4508	-2.3308
3	11.03	11.4508	-0.4208
4	12.74	11.4508	1.2892
5	9.98	11.4508	-1.4708
6	11.62	11.4508	0.1692
7	9.4	11.4508	-2.0508
8	9.27	11.4508	-2.1808
9	7.76	7.66692307692308	0.0930769230769215
10	8.78	23.2811111111111	-14.5011111111111
11	10.65	23.2811111111111	-12.6311111111111
12	10.95	23.2811111111111	-12.3311111111111
13	12.36	11.4508	0.9092
14	10.85	11.4508	-0.6008
15	11.84	11.4508	0.389200000000001
16	12.14	11.4508	0.689200000000001
17	11.65	11.4508	0.199200000000001
18	8.86	7.66692307692308	1.19307692307692
19	7.63	7.66692307692308	-0.0369230769230784
20	7.38	7.66692307692308	-0.286923076923078
21	7.25	7.66692307692308	-0.416923076923078
22	8.03	7.66692307692308	0.363076923076921
23	7.75	7.66692307692308	0.0830769230769217
24	7.16	7.66692307692308	-0.506923076923078
25	7.18	7.66692307692308	-0.486923076923079
26	7.51	7.66692307692308	-0.156923076923078
27	7.07	7.66692307692308	-0.596923076923078
28	7.11	7.66692307692308	-0.556923076923078
29	8.98	7.66692307692308	1.31307692307692
30	9.53	11.4508	-1.9208
31	10.54	11.4508	-0.9108
32	11.31	11.4508	-0.140799999999999
33	10.36	11.4508	-1.0908
34	11.44	11.4508	-0.0107999999999997
35	10.45	11.4508	-1.0008
36	10.69	11.4508	-0.7608
37	11.28	11.4508	-0.1708
38	11.96	11.4508	0.509200000000002
39	13.52	11.4508	2.0692
40	12.89	11.4508	1.4392
41	14.03	11.4508	2.5792
42	16.27	11.4508	4.8192
43	16.17	23.2811111111111	-7.11111111111111
44	17.25	23.2811111111111	-6.03111111111111
45	19.38	23.2811111111111	-3.90111111111111
46	26.2	49.5305263157895	-23.3305263157895
47	33.53	49.5305263157895	-16.0005263157895
48	32.2	49.5305263157895	-17.3305263157895
49	38.45	49.5305263157895	-11.0805263157895
50	44.86	49.5305263157895	-4.67052631578947
51	41.67	49.5305263157895	-7.86052631578947
52	36.06	49.5305263157895	-13.4705263157895
53	39.76	49.5305263157895	-9.77052631578947
54	36.81	23.2811111111111	13.5288888888889
55	42.65	23.2811111111111	19.3688888888889
56	46.89	23.2811111111111	23.6088888888889
57	53.61	49.5305263157895	4.07947368421053
58	57.59	49.5305263157895	8.05947368421053
59	67.82	87.4663636363636	-19.6463636363636
60	71.89	49.5305263157895	22.3594736842105
61	75.51	87.4663636363636	-11.9563636363636
62	68.49	49.5305263157895	18.9594736842105
63	62.72	49.5305263157895	13.1894736842105
64	70.39	49.5305263157895	20.8594736842105
65	59.77	49.5305263157895	10.2394736842105
66	57.27	49.5305263157895	7.73947368421053
67	67.96	49.5305263157895	18.4294736842105
68	67.85	49.5305263157895	18.3194736842105
69	76.98	87.4663636363636	-10.4863636363636
70	81.08	87.4663636363636	-6.38636363636363
71	91.66	87.4663636363636	4.19363636363637
72	84.84	87.4663636363636	-2.62636363636362
73	85.73	87.4663636363636	-1.73636363636362
74	84.61	87.4663636363636	-2.85636363636362
75	92.91	87.4663636363636	5.44363636363637
76	99.8	87.4663636363636	12.3336363636364
77	121.19	87.4663636363636	33.7236363636364
78	122.04	124.094615384615	-2.05461538461537
79	131.76	150.437272727273	-18.6772727272728
80	138.48	150.437272727273	-11.9572727272728
81	153.47	150.437272727273	3.03272727272724
82	189.95	150.437272727273	39.5127272727272
83	182.22	150.437272727273	31.7827272727272
84	198.08	218.733125	-20.653125
85	135.36	150.437272727273	-15.0772727272727
86	125.02	150.437272727273	-25.4172727272728
87	143.5	150.437272727273	-6.93727272727276
88	173.95	150.437272727273	23.5127272727272
89	188.75	218.733125	-29.983125
90	167.44	150.437272727273	17.0027272727272
91	158.95	218.733125	-59.783125
92	169.53	218.733125	-49.203125
93	113.66	150.437272727273	-36.7772727272728
94	107.59	124.094615384615	-16.5046153846154
95	92.67	124.094615384615	-31.4246153846154
96	85.35	124.094615384615	-38.7446153846154
97	90.13	124.094615384615	-33.9646153846154
98	89.31	124.094615384615	-34.7846153846154
99	105.12	124.094615384615	-18.9746153846154
100	125.83	124.094615384615	1.73538461538462
101	135.81	124.094615384615	11.7153846153846
102	142.43	124.094615384615	18.3353846153846
103	163.39	124.094615384615	39.2953846153846
104	168.21	124.094615384615	44.1153846153846
105	185.35	124.094615384615	61.2553846153846
106	188.5	218.733125	-30.233125
107	199.91	218.733125	-18.8231250000000
108	210.73	218.733125	-8.00312500000004
109	192.06	218.733125	-26.6731250000000
110	204.62	218.733125	-14.1131250000000
111	235	218.733125	16.2668750000000
112	261.09	218.733125	42.3568749999999
113	256.88	218.733125	38.146875
114	251.53	218.733125	32.796875
115	257.25	218.733125	38.516875
116	243.1	218.733125	24.3668750000000
117	283.75	218.733125	65.016875

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