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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

Fri, 24 Dec 2010 13:24:35 +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/t1293197592548ust8s96hr0vp.htm/, Retrieved Tue, 30 Apr 2024 06:26:11 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114932, Retrieved Tue, 30 Apr 2024 06:26:11 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

130

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]
-    D            [Recursive Partitioning (Regression Trees)] [Apple Inc - Recur...] [2010-12-24 13:24:35] [aa6b599ccd367bc74fed0d8f67004a46] [Current]
-    D              [Recursive Partitioning (Regression Trees)] [Apple Inc - Recur...] [2010-12-24 13:36:44] [afe9379cca749d06b3d6872e02cc47ed] 

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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	1
9.12	-0,2643	0	0	14163200	23.62	2151.83	0,0353	 109.2	2
11.03	-0,2643	0	0	18184800	21.90	1840.26	0,0292	 116.9	3
12.74	-0,1918	0	0	20810300	27.12	2116.24	0,0327	 109.9	4
9.98	-0,1918	0	0	12843000	27.70	2110.49	0,0362	 116.1	5
11.62	-0,1918	0	0	13866700	29.23	2160.54	0,0325	 118.9	6
9.40	-0,2246	0	0	15119200	26.50	2027.13	0,0272	 116.3	7
9.27	-0,2246	0	0	8301600	22.84	1805.43	0,0272	 114.0	8
7.76	-0,2246	0	0	14039600	20.49	1498.80	0,0265	 97.0	9
8.78	0,3654	0	0	12139700	23.28	1690.20	0,0213	 85.3	10
10.65	0,3654	0	0	9649000	25.71	1930.58	0,019	 84.9	11
10.95	0,3654	0	0	8513600	26.52	1950.40	0,0155	 94.6	12
12.36	0,0447	0	0	15278600	25.51	1934.03	0,0114	 97.8	13
10.85	0,0447	0	0	15590900	23.36	1731.49	0,0114	 95.0	14
11.84	0,0447	0	0	9691100	24.15	1845.35	0,0148	 110.7	15
12.14	-0,0312	0	0	10882700	20.92	1688.23	0,0164	 108.5	16
11.65	-0,0312	0	0	10294800	20.38	1615.73	0,0118	 110.3	17
8.86	-0,0312	0	0	16031900	21.90	1463.21	0,0107	 106.3	18
7.63	-0,0048	0	0	13683600	19.21	1328.26	0,0146	 97.4	19
7.38	-0,0048	0	0	8677200	19.65	1314.85	0,018	 94.5	20
7.25	-0,0048	0	0	9874100	17.51	1172.06	0,0151	 93.7	21
8.03	0,0705	0	0	10725500	21.41	1329.75	0,0203	 79.6	22
7.75	0,0705	0	0	8348400	23.09	1478.78	0,022	 84.9	23
7.16	0,0705	0	0	8046200	20.70	1335.51	0,0238	 80.7	24
7.18	-0,0134	0	0	10862300	19.00	1320.91	0,026	 78.8	25
7.51	-0,0134	0	0	8100300	19.04	1337.52	0,0298	 64.8	26
7.07	-0,0134	0	0	7287500	19.45	1341.17	0,0302	 61.4	27
7.11	0,0812	0	0	14002500	20.54	1464.31	0,0222	 81.0	28
8.98	0,0812	0	0	19037900	19.77	1595.91	0,0206	 83.6	29
9.53	0,0812	0	0	10774600	20.60	1622.80	0,0211	 83.5	30
10.54	0,1885	0	0	8960600	21.21	1735.02	0,0211	 77.0	31
11.31	0,1885	0	0	7773300	21.30	1810.45	0,0216	 81.7	32
10.36	0,1885	0	0	9579700	22.33	1786.94	0,0232	 77.0	33
11.44	0,3628	0	0	11270700	21.12	1932.21	0,0204	 81.7	34
10.45	0,3628	0	0	9492800	20.77	1960.26	0,0177	 92.5	35
10.69	0,3628	0	0	9136800	22.11	2003.37	0,0188	 91.7	36
11.28	0,2942	0	0	14487600	22.34	2066.15	0,0193	 96.4	37
11.96	0,2942	0	0	10133200	21.43	2029.82	0,0169	 88.5	38
13.52	0,2942	0	0	18659700	20.14	1994.22	0,0174	 88.5	39
12.89	0,3036	0	0	15980700	21.11	1920.15	0,0229	 93.0	40
14.03	0,3036	0	0	9732100	21.19	1986.74	0,0305	 93.1	41
16.27	0,3036	0	0	14626300	23.07	2047.79	0,0327	 102.8	42
16.17	0,3703	0	0	16904000	23.01	1887.36	0,0299	 105.7	43
17.25	0,3703	0	0	13616700	22.12	1838.10	0,0265	 98.7	44
19.38	0,3703	0	0	13772900	22.40	1896.84	0,0254	 96.7	45
26.20	0,7398	0	0	28749200	22.66	1974.99	0,0319	 92.9	46
33.53	0,7398	0	0	31408300	24.21	2096.81	0,0352	 92.6	47
32.20	0,7398	0	0	26342800	24.13	2175.44	0,0326	 102.7	48
38.45	0,6988	0	0	48909500	23.73	2062.41	0,0297	 105.1	49
44.86	0,6988	0	0	41542400	22.79	2051.72	0,0301	 104.4	50
41.67	0,6988	0	0	24857200	21.89	1999.23	0,0315	 103.0	51
36.06	0,7478	0	0	34093700	22.92	1921.65	0,0351	 97.5	52
39.76	0,7478	0	0	22555200	23.44	2068.22	0,028	 103.1	53
36.81	0,7478	0	0	19067500	22.57	2056.96	0,0253	 106.2	54
42.65	0,5651	0	0	19029100	23.27	2184.83	0,0317	 103.6	55
46.89	0,5651	0	0	15223200	24.95	2152.09	0,0364	 105.5	56
53.61	0,5651	0	0	21903700	23.45	2151.69	0,0469	 87.5	57
57.59	0,6473	0	0	33306600	23.42	2120.30	0,0435	 85.2	58
67.82	0,6473	0	0	23898100	25.30	2232.82	0,0346	 98.3	59
71.89	0,6473	0	0	23279600	23.90	2205.32	0,0342	 103.8	60
75.51	0,3441	0	0	40699800	25.73	2305.82	0,0399	 106.8	61
68.49	0,3441	0	0	37646000	24.64	2281.39	0,036	 102.7	62
62.72	0,3441	0	0	37277000	24.95	2339.79	0,0336	 107.5	63
70.39	0,2415	0	0	39246800	22.15	2322.57	0,0355	 109.8	64
59.77	0,2415	0	0	27418400	20.85	2178.88	0,0417	 104.7	65
57.27	0,2415	0	0	30318700	21.45	2172.09	0,0432	 105.7	66
67.96	0,3151	0	0	32808100	22.15	2091.47	0,0415	 107.0	67
67.85	0,3151	0	0	28668200	23.75	2183.75	0,0382	 100.2	68
76.98	0,3151	0	0	32370300	25.27	2258.43	0,0206	 105.9	69
81.08	0,239	0	0	24171100	26.53	2366.71	0,0131	 105.1	70
91.66	0,239	0	0	25009100	27.22	2431.77	0,0197	 105.3	71
84.84	0,239	0	0	32084300	27.69	2415.29	0,0254	 110.0	72
85.73	0,2127	0	0	50117500	28.61	2463.93	0,0208	 110.2	73
84.61	0,2127	0	0	27522200	26.21	2416.15	0,0242	 111.2	74
92.91	0,2127	0	0	26816800	25.93	2421.64	0,0278	 108.2	75
99.80	0,273	0	0	25136100	27.86	2525.09	0,0257	 106.3	76
121.19	0,273	0	0	30295600	28.65	2604.52	0,0269	 108.5	77
122.04	0,273	0,273	0	41526100	27.51	2603.23	0,0269	 105.3	78
131.76	0,3657	0,3657	0	43845100	27.06	2546.27	0,0236	 111.9	79
138.48	0,3657	0,3657	0	39188900	26.91	2596.36	0,0197	 105.6	80
153.47	0,3657	0,3657	0	40496400	27.60	2701.50	0,0276	 99.5	81
189.95	0,4643	0,4643	0	37438400	34.48	2859.12	0,0354	 95.2	82
182.22	0,4643	0,4643	0	46553700	31.58	2660.96	0,0431	 87.8	83
198.08	0,4643	0,4643	0	31771400	33.46	2652.28	0,0408	 90.6	84
135.36	0,5096	0,5096	0	62108100	30.64	2389.86	0,0428	 87.9	85
125.02	0,5096	0,5096	0	46645400	25.66	2271.48	0,0403	 76.4	86
143.50	0,5096	0,5096	0	42313100	26.78	2279.10	0,0398	 65.9	87
173.95	0,3592	0,3592	0	38841700	26.91	2412.80	0,0394	 62.3	88
188.75	0,3592	0,3592	0	32650300	26.82	2522.66	0,0418	 57.2	89
167.44	0,3592	0,3592	0	34281100	26.05	2292.98	0,0502	 50.4	90
158.95	0,7439	0,7439	0	33096200	24.36	2325.55	0,056	 51.9	91
169.53	0,7439	0,7439	0	23273800	25.94	2367.52	0,0537	 58.5	92
113.66	0,7439	0,7439	0	43697600	25.37	2091.88	0,0494	 61.4	93
107.59	0,139	0,139	0	66902300	21.23	1720.95	0,0366	 38.8	94
92.67	0,139	0,139	0	44957200	19.35	1535.57	0,0107	 44.9	95
85.35	0,139	0,139	0	33800900	18.61	1577.03	0,0009	 38.6	96
90.13	0,1383	0,1383	0	33487900	16.37	1476.42	0,0003	 4.0	97
89.31	0,1383	0,1383	0	27394900	15.56	1377.84	0,0024	 25.3	98
105.12	0,1383	0,1383	0	25963400	17.70	1528.59	-0,0038	 26.9	99
125.83	0,2874	0,2874	0	20952600	19.52	1717.30	-0,0074	 40.8	100
135.81	0,2874	0,2874	0	17702900	20.26	1774.33	-0,0128	 54.8	101
142.43	0,2874	0,2874	0	21282100	23.05	1835.04	-0,0143	 49.3	102
163.39	0,0596	0,0596	0	18449100	22.81	1978.50	-0,021	 47.4	103
168.21	0,0596	0,0596	0	14415700	24.04	2009.06	-0,0148	 54.5	104
185.35	0,0596	0,0596	0	17906300	25.08	2122.42	-0,0129	 53.4	105
188.50	0,3201	0,3201	0	22197500	27.04	2045.11	-0,0018	 48.7	106
199.91	0,3201	0,3201	0	15856500	28.81	2144.60	0,0184	 50.6	107
210.73	0,3201	0,3201	0	19068700	29.86	2269.15	0,0272	 53.6	108
192.06	0,486	0,486	0	30855100	27.61	2147.35	0,0263	 56.5	109
204.62	0,486	0,486	0	21209000	28.22	2238.26	0,0214	 46.4	110
235.00	0,486	0,486	0	19541600	28.83	2397.96	0,0231	 52.3	111
261.09	0,6129	0,6129	0,6129	21955000	30.06	2461.19	0,0224	 57.7	112
256.88	0,6129	0,6129	0,6129	33725900	25.51	2257.04	0,0202	 62.7	113
251.53	0,6129	0,6129	0,6129	28192800	22.75	2109.24	0,0105	 54.3	114
257.25	0,6665	0,6665	0,6665	27377000	25.52	2254.70	0,0124	 51.0	115
243.10	0,6665	0,6665	0,6665	16228100	23.33	2114.03	0,0115	 53.2	116
283.75	0,6665	0,6665	0,6665	21278900	24.34	2368.62	0,0114	 48.6	117

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	13 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 13 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114932&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114932&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114932&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	13 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.9756
R-squared	0.9518
RMSE	16.6129

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9756 \tabularnewline
R-squared & 0.9518 \tabularnewline
RMSE & 16.6129 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114932&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9756[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9518[/C][/ROW]
[ROW][C]RMSE[/C][C]16.6129[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114932&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114932&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	0.9756
R-squared	0.9518
RMSE	16.6129

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	10.81	10.83	-0.0199999999999978
2	9.12	10.83	-1.71
3	11.03	10.83	0.200000000000001
4	12.74	10.83	1.91000000000000
5	9.98	10.83	-0.849999999999998
6	11.62	10.83	0.790000000000001
7	9.4	10.83	-1.43000000000000
8	9.27	10.83	-1.56
9	7.76	7.66692307692308	0.0930769230769215
10	8.78	10.83	-2.05
11	10.65	10.83	-0.179999999999998
12	10.95	10.83	0.120000000000001
13	12.36	10.83	1.53
14	10.85	10.83	0.0200000000000014
15	11.84	10.83	1.01000000000000
16	12.14	10.83	1.31000000000000
17	11.65	10.83	0.820000000000002
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	10.83	-1.3
31	10.54	10.83	-0.289999999999999
32	11.31	10.83	0.480000000000002
33	10.36	10.83	-0.469999999999999
34	11.44	10.83	0.610000000000001
35	10.45	10.83	-0.379999999999999
36	10.69	10.83	-0.139999999999999
37	11.28	10.83	0.450000000000001
38	11.96	10.83	1.13000000000000
39	13.52	15.6442857142857	-2.12428571428571
40	12.89	15.6442857142857	-2.75428571428571
41	14.03	15.6442857142857	-1.61428571428571
42	16.27	15.6442857142857	0.625714285714286
43	16.17	15.6442857142857	0.525714285714288
44	17.25	15.6442857142857	1.60571428571429
45	19.38	15.6442857142857	3.73571428571429
46	26.2	37.219	-11.019
47	33.53	37.219	-3.689
48	32.2	37.219	-5.019
49	38.45	37.219	1.23100000000000
50	44.86	37.219	7.641
51	41.67	37.219	4.451
52	36.06	37.219	-1.159
53	39.76	37.219	2.54100000000000
54	36.81	37.219	-0.408999999999999
55	42.65	37.219	5.431
56	46.89	58.7057142857143	-11.8157142857143
57	53.61	58.7057142857143	-5.09571428571428
58	57.59	58.7057142857143	-1.11571428571428
59	67.82	78.0484615384615	-10.2284615384615
60	71.89	78.0484615384615	-6.15846153846154
61	75.51	78.0484615384615	-2.53846153846153
62	68.49	78.0484615384615	-9.55846153846154
63	62.72	78.0484615384615	-15.3284615384615
64	70.39	78.0484615384615	-7.65846153846154
65	59.77	58.7057142857143	1.06428571428572
66	57.27	58.7057142857143	-1.43571428571428
67	67.96	58.7057142857143	9.25428571428571
68	67.85	58.7057142857143	9.14428571428572
69	76.98	78.0484615384615	-1.06846153846153
70	81.08	78.0484615384615	3.03153846153846
71	91.66	78.0484615384615	13.6115384615385
72	84.84	78.0484615384615	6.79153846153847
73	85.73	78.0484615384615	7.68153846153847
74	84.61	78.0484615384615	6.56153846153846
75	92.91	78.0484615384615	14.8615384615385
76	99.8	151.770476190476	-51.9704761904762
77	121.19	151.770476190476	-30.5804761904762
78	122.04	151.770476190476	-29.7304761904762
79	131.76	151.770476190476	-20.0104761904762
80	138.48	151.770476190476	-13.2904761904762
81	153.47	151.770476190476	1.69952380952384
82	189.95	151.770476190476	38.1795238095238
83	182.22	151.770476190476	30.4495238095238
84	198.08	151.770476190476	46.3095238095239
85	135.36	151.770476190476	-16.4104761904761
86	125.02	151.770476190476	-26.7504761904762
87	143.5	151.770476190476	-8.27047619047616
88	173.95	151.770476190476	22.1795238095238
89	188.75	151.770476190476	36.9795238095238
90	167.44	151.770476190476	15.6695238095238
91	158.95	151.770476190476	7.17952380952383
92	169.53	151.770476190476	17.7595238095238
93	113.66	151.770476190476	-38.1104761904762
94	107.59	103.97625	3.61374999999998
95	92.67	103.97625	-11.3062500000000
96	85.35	103.97625	-18.6262500000000
97	90.13	103.97625	-13.8462500000000
98	89.31	103.97625	-14.6662500000000
99	105.12	103.97625	1.14374999999998
100	125.83	103.97625	21.8537500000000
101	135.81	103.97625	31.83375
102	142.43	151.770476190476	-9.34047619047615
103	163.39	151.770476190476	11.6195238095238
104	168.21	151.770476190476	16.4395238095238
105	185.35	228.443846153846	-43.0938461538462
106	188.5	228.443846153846	-39.9438461538462
107	199.91	228.443846153846	-28.5338461538462
108	210.73	228.443846153846	-17.7138461538462
109	192.06	228.443846153846	-36.3838461538462
110	204.62	228.443846153846	-23.8238461538461
111	235	228.443846153846	6.55615384615385
112	261.09	228.443846153846	32.6461538461538
113	256.88	228.443846153846	28.4361538461538
114	251.53	228.443846153846	23.0861538461538
115	257.25	228.443846153846	28.8061538461538
116	243.1	228.443846153846	14.6561538461538
117	283.75	228.443846153846	55.3061538461538

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 10.81 & 10.83 & -0.0199999999999978 \tabularnewline
2 & 9.12 & 10.83 & -1.71 \tabularnewline
3 & 11.03 & 10.83 & 0.200000000000001 \tabularnewline
4 & 12.74 & 10.83 & 1.91000000000000 \tabularnewline
5 & 9.98 & 10.83 & -0.849999999999998 \tabularnewline
6 & 11.62 & 10.83 & 0.790000000000001 \tabularnewline
7 & 9.4 & 10.83 & -1.43000000000000 \tabularnewline
8 & 9.27 & 10.83 & -1.56 \tabularnewline
9 & 7.76 & 7.66692307692308 & 0.0930769230769215 \tabularnewline
10 & 8.78 & 10.83 & -2.05 \tabularnewline
11 & 10.65 & 10.83 & -0.179999999999998 \tabularnewline
12 & 10.95 & 10.83 & 0.120000000000001 \tabularnewline
13 & 12.36 & 10.83 & 1.53 \tabularnewline
14 & 10.85 & 10.83 & 0.0200000000000014 \tabularnewline
15 & 11.84 & 10.83 & 1.01000000000000 \tabularnewline
16 & 12.14 & 10.83 & 1.31000000000000 \tabularnewline
17 & 11.65 & 10.83 & 0.820000000000002 \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 & 10.83 & -1.3 \tabularnewline
31 & 10.54 & 10.83 & -0.289999999999999 \tabularnewline
32 & 11.31 & 10.83 & 0.480000000000002 \tabularnewline
33 & 10.36 & 10.83 & -0.469999999999999 \tabularnewline
34 & 11.44 & 10.83 & 0.610000000000001 \tabularnewline
35 & 10.45 & 10.83 & -0.379999999999999 \tabularnewline
36 & 10.69 & 10.83 & -0.139999999999999 \tabularnewline
37 & 11.28 & 10.83 & 0.450000000000001 \tabularnewline
38 & 11.96 & 10.83 & 1.13000000000000 \tabularnewline
39 & 13.52 & 15.6442857142857 & -2.12428571428571 \tabularnewline
40 & 12.89 & 15.6442857142857 & -2.75428571428571 \tabularnewline
41 & 14.03 & 15.6442857142857 & -1.61428571428571 \tabularnewline
42 & 16.27 & 15.6442857142857 & 0.625714285714286 \tabularnewline
43 & 16.17 & 15.6442857142857 & 0.525714285714288 \tabularnewline
44 & 17.25 & 15.6442857142857 & 1.60571428571429 \tabularnewline
45 & 19.38 & 15.6442857142857 & 3.73571428571429 \tabularnewline
46 & 26.2 & 37.219 & -11.019 \tabularnewline
47 & 33.53 & 37.219 & -3.689 \tabularnewline
48 & 32.2 & 37.219 & -5.019 \tabularnewline
49 & 38.45 & 37.219 & 1.23100000000000 \tabularnewline
50 & 44.86 & 37.219 & 7.641 \tabularnewline
51 & 41.67 & 37.219 & 4.451 \tabularnewline
52 & 36.06 & 37.219 & -1.159 \tabularnewline
53 & 39.76 & 37.219 & 2.54100000000000 \tabularnewline
54 & 36.81 & 37.219 & -0.408999999999999 \tabularnewline
55 & 42.65 & 37.219 & 5.431 \tabularnewline
56 & 46.89 & 58.7057142857143 & -11.8157142857143 \tabularnewline
57 & 53.61 & 58.7057142857143 & -5.09571428571428 \tabularnewline
58 & 57.59 & 58.7057142857143 & -1.11571428571428 \tabularnewline
59 & 67.82 & 78.0484615384615 & -10.2284615384615 \tabularnewline
60 & 71.89 & 78.0484615384615 & -6.15846153846154 \tabularnewline
61 & 75.51 & 78.0484615384615 & -2.53846153846153 \tabularnewline
62 & 68.49 & 78.0484615384615 & -9.55846153846154 \tabularnewline
63 & 62.72 & 78.0484615384615 & -15.3284615384615 \tabularnewline
64 & 70.39 & 78.0484615384615 & -7.65846153846154 \tabularnewline
65 & 59.77 & 58.7057142857143 & 1.06428571428572 \tabularnewline
66 & 57.27 & 58.7057142857143 & -1.43571428571428 \tabularnewline
67 & 67.96 & 58.7057142857143 & 9.25428571428571 \tabularnewline
68 & 67.85 & 58.7057142857143 & 9.14428571428572 \tabularnewline
69 & 76.98 & 78.0484615384615 & -1.06846153846153 \tabularnewline
70 & 81.08 & 78.0484615384615 & 3.03153846153846 \tabularnewline
71 & 91.66 & 78.0484615384615 & 13.6115384615385 \tabularnewline
72 & 84.84 & 78.0484615384615 & 6.79153846153847 \tabularnewline
73 & 85.73 & 78.0484615384615 & 7.68153846153847 \tabularnewline
74 & 84.61 & 78.0484615384615 & 6.56153846153846 \tabularnewline
75 & 92.91 & 78.0484615384615 & 14.8615384615385 \tabularnewline
76 & 99.8 & 151.770476190476 & -51.9704761904762 \tabularnewline
77 & 121.19 & 151.770476190476 & -30.5804761904762 \tabularnewline
78 & 122.04 & 151.770476190476 & -29.7304761904762 \tabularnewline
79 & 131.76 & 151.770476190476 & -20.0104761904762 \tabularnewline
80 & 138.48 & 151.770476190476 & -13.2904761904762 \tabularnewline
81 & 153.47 & 151.770476190476 & 1.69952380952384 \tabularnewline
82 & 189.95 & 151.770476190476 & 38.1795238095238 \tabularnewline
83 & 182.22 & 151.770476190476 & 30.4495238095238 \tabularnewline
84 & 198.08 & 151.770476190476 & 46.3095238095239 \tabularnewline
85 & 135.36 & 151.770476190476 & -16.4104761904761 \tabularnewline
86 & 125.02 & 151.770476190476 & -26.7504761904762 \tabularnewline
87 & 143.5 & 151.770476190476 & -8.27047619047616 \tabularnewline
88 & 173.95 & 151.770476190476 & 22.1795238095238 \tabularnewline
89 & 188.75 & 151.770476190476 & 36.9795238095238 \tabularnewline
90 & 167.44 & 151.770476190476 & 15.6695238095238 \tabularnewline
91 & 158.95 & 151.770476190476 & 7.17952380952383 \tabularnewline
92 & 169.53 & 151.770476190476 & 17.7595238095238 \tabularnewline
93 & 113.66 & 151.770476190476 & -38.1104761904762 \tabularnewline
94 & 107.59 & 103.97625 & 3.61374999999998 \tabularnewline
95 & 92.67 & 103.97625 & -11.3062500000000 \tabularnewline
96 & 85.35 & 103.97625 & -18.6262500000000 \tabularnewline
97 & 90.13 & 103.97625 & -13.8462500000000 \tabularnewline
98 & 89.31 & 103.97625 & -14.6662500000000 \tabularnewline
99 & 105.12 & 103.97625 & 1.14374999999998 \tabularnewline
100 & 125.83 & 103.97625 & 21.8537500000000 \tabularnewline
101 & 135.81 & 103.97625 & 31.83375 \tabularnewline
102 & 142.43 & 151.770476190476 & -9.34047619047615 \tabularnewline
103 & 163.39 & 151.770476190476 & 11.6195238095238 \tabularnewline
104 & 168.21 & 151.770476190476 & 16.4395238095238 \tabularnewline
105 & 185.35 & 228.443846153846 & -43.0938461538462 \tabularnewline
106 & 188.5 & 228.443846153846 & -39.9438461538462 \tabularnewline
107 & 199.91 & 228.443846153846 & -28.5338461538462 \tabularnewline
108 & 210.73 & 228.443846153846 & -17.7138461538462 \tabularnewline
109 & 192.06 & 228.443846153846 & -36.3838461538462 \tabularnewline
110 & 204.62 & 228.443846153846 & -23.8238461538461 \tabularnewline
111 & 235 & 228.443846153846 & 6.55615384615385 \tabularnewline
112 & 261.09 & 228.443846153846 & 32.6461538461538 \tabularnewline
113 & 256.88 & 228.443846153846 & 28.4361538461538 \tabularnewline
114 & 251.53 & 228.443846153846 & 23.0861538461538 \tabularnewline
115 & 257.25 & 228.443846153846 & 28.8061538461538 \tabularnewline
116 & 243.1 & 228.443846153846 & 14.6561538461538 \tabularnewline
117 & 283.75 & 228.443846153846 & 55.3061538461538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114932&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]10.83[/C][C]-0.0199999999999978[/C][/ROW]
[ROW][C]2[/C][C]9.12[/C][C]10.83[/C][C]-1.71[/C][/ROW]
[ROW][C]3[/C][C]11.03[/C][C]10.83[/C][C]0.200000000000001[/C][/ROW]
[ROW][C]4[/C][C]12.74[/C][C]10.83[/C][C]1.91000000000000[/C][/ROW]
[ROW][C]5[/C][C]9.98[/C][C]10.83[/C][C]-0.849999999999998[/C][/ROW]
[ROW][C]6[/C][C]11.62[/C][C]10.83[/C][C]0.790000000000001[/C][/ROW]
[ROW][C]7[/C][C]9.4[/C][C]10.83[/C][C]-1.43000000000000[/C][/ROW]
[ROW][C]8[/C][C]9.27[/C][C]10.83[/C][C]-1.56[/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]10.83[/C][C]-2.05[/C][/ROW]
[ROW][C]11[/C][C]10.65[/C][C]10.83[/C][C]-0.179999999999998[/C][/ROW]
[ROW][C]12[/C][C]10.95[/C][C]10.83[/C][C]0.120000000000001[/C][/ROW]
[ROW][C]13[/C][C]12.36[/C][C]10.83[/C][C]1.53[/C][/ROW]
[ROW][C]14[/C][C]10.85[/C][C]10.83[/C][C]0.0200000000000014[/C][/ROW]
[ROW][C]15[/C][C]11.84[/C][C]10.83[/C][C]1.01000000000000[/C][/ROW]
[ROW][C]16[/C][C]12.14[/C][C]10.83[/C][C]1.31000000000000[/C][/ROW]
[ROW][C]17[/C][C]11.65[/C][C]10.83[/C][C]0.820000000000002[/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]10.83[/C][C]-1.3[/C][/ROW]
[ROW][C]31[/C][C]10.54[/C][C]10.83[/C][C]-0.289999999999999[/C][/ROW]
[ROW][C]32[/C][C]11.31[/C][C]10.83[/C][C]0.480000000000002[/C][/ROW]
[ROW][C]33[/C][C]10.36[/C][C]10.83[/C][C]-0.469999999999999[/C][/ROW]
[ROW][C]34[/C][C]11.44[/C][C]10.83[/C][C]0.610000000000001[/C][/ROW]
[ROW][C]35[/C][C]10.45[/C][C]10.83[/C][C]-0.379999999999999[/C][/ROW]
[ROW][C]36[/C][C]10.69[/C][C]10.83[/C][C]-0.139999999999999[/C][/ROW]
[ROW][C]37[/C][C]11.28[/C][C]10.83[/C][C]0.450000000000001[/C][/ROW]
[ROW][C]38[/C][C]11.96[/C][C]10.83[/C][C]1.13000000000000[/C][/ROW]
[ROW][C]39[/C][C]13.52[/C][C]15.6442857142857[/C][C]-2.12428571428571[/C][/ROW]
[ROW][C]40[/C][C]12.89[/C][C]15.6442857142857[/C][C]-2.75428571428571[/C][/ROW]
[ROW][C]41[/C][C]14.03[/C][C]15.6442857142857[/C][C]-1.61428571428571[/C][/ROW]
[ROW][C]42[/C][C]16.27[/C][C]15.6442857142857[/C][C]0.625714285714286[/C][/ROW]
[ROW][C]43[/C][C]16.17[/C][C]15.6442857142857[/C][C]0.525714285714288[/C][/ROW]
[ROW][C]44[/C][C]17.25[/C][C]15.6442857142857[/C][C]1.60571428571429[/C][/ROW]
[ROW][C]45[/C][C]19.38[/C][C]15.6442857142857[/C][C]3.73571428571429[/C][/ROW]
[ROW][C]46[/C][C]26.2[/C][C]37.219[/C][C]-11.019[/C][/ROW]
[ROW][C]47[/C][C]33.53[/C][C]37.219[/C][C]-3.689[/C][/ROW]
[ROW][C]48[/C][C]32.2[/C][C]37.219[/C][C]-5.019[/C][/ROW]
[ROW][C]49[/C][C]38.45[/C][C]37.219[/C][C]1.23100000000000[/C][/ROW]
[ROW][C]50[/C][C]44.86[/C][C]37.219[/C][C]7.641[/C][/ROW]
[ROW][C]51[/C][C]41.67[/C][C]37.219[/C][C]4.451[/C][/ROW]
[ROW][C]52[/C][C]36.06[/C][C]37.219[/C][C]-1.159[/C][/ROW]
[ROW][C]53[/C][C]39.76[/C][C]37.219[/C][C]2.54100000000000[/C][/ROW]
[ROW][C]54[/C][C]36.81[/C][C]37.219[/C][C]-0.408999999999999[/C][/ROW]
[ROW][C]55[/C][C]42.65[/C][C]37.219[/C][C]5.431[/C][/ROW]
[ROW][C]56[/C][C]46.89[/C][C]58.7057142857143[/C][C]-11.8157142857143[/C][/ROW]
[ROW][C]57[/C][C]53.61[/C][C]58.7057142857143[/C][C]-5.09571428571428[/C][/ROW]
[ROW][C]58[/C][C]57.59[/C][C]58.7057142857143[/C][C]-1.11571428571428[/C][/ROW]
[ROW][C]59[/C][C]67.82[/C][C]78.0484615384615[/C][C]-10.2284615384615[/C][/ROW]
[ROW][C]60[/C][C]71.89[/C][C]78.0484615384615[/C][C]-6.15846153846154[/C][/ROW]
[ROW][C]61[/C][C]75.51[/C][C]78.0484615384615[/C][C]-2.53846153846153[/C][/ROW]
[ROW][C]62[/C][C]68.49[/C][C]78.0484615384615[/C][C]-9.55846153846154[/C][/ROW]
[ROW][C]63[/C][C]62.72[/C][C]78.0484615384615[/C][C]-15.3284615384615[/C][/ROW]
[ROW][C]64[/C][C]70.39[/C][C]78.0484615384615[/C][C]-7.65846153846154[/C][/ROW]
[ROW][C]65[/C][C]59.77[/C][C]58.7057142857143[/C][C]1.06428571428572[/C][/ROW]
[ROW][C]66[/C][C]57.27[/C][C]58.7057142857143[/C][C]-1.43571428571428[/C][/ROW]
[ROW][C]67[/C][C]67.96[/C][C]58.7057142857143[/C][C]9.25428571428571[/C][/ROW]
[ROW][C]68[/C][C]67.85[/C][C]58.7057142857143[/C][C]9.14428571428572[/C][/ROW]
[ROW][C]69[/C][C]76.98[/C][C]78.0484615384615[/C][C]-1.06846153846153[/C][/ROW]
[ROW][C]70[/C][C]81.08[/C][C]78.0484615384615[/C][C]3.03153846153846[/C][/ROW]
[ROW][C]71[/C][C]91.66[/C][C]78.0484615384615[/C][C]13.6115384615385[/C][/ROW]
[ROW][C]72[/C][C]84.84[/C][C]78.0484615384615[/C][C]6.79153846153847[/C][/ROW]
[ROW][C]73[/C][C]85.73[/C][C]78.0484615384615[/C][C]7.68153846153847[/C][/ROW]
[ROW][C]74[/C][C]84.61[/C][C]78.0484615384615[/C][C]6.56153846153846[/C][/ROW]
[ROW][C]75[/C][C]92.91[/C][C]78.0484615384615[/C][C]14.8615384615385[/C][/ROW]
[ROW][C]76[/C][C]99.8[/C][C]151.770476190476[/C][C]-51.9704761904762[/C][/ROW]
[ROW][C]77[/C][C]121.19[/C][C]151.770476190476[/C][C]-30.5804761904762[/C][/ROW]
[ROW][C]78[/C][C]122.04[/C][C]151.770476190476[/C][C]-29.7304761904762[/C][/ROW]
[ROW][C]79[/C][C]131.76[/C][C]151.770476190476[/C][C]-20.0104761904762[/C][/ROW]
[ROW][C]80[/C][C]138.48[/C][C]151.770476190476[/C][C]-13.2904761904762[/C][/ROW]
[ROW][C]81[/C][C]153.47[/C][C]151.770476190476[/C][C]1.69952380952384[/C][/ROW]
[ROW][C]82[/C][C]189.95[/C][C]151.770476190476[/C][C]38.1795238095238[/C][/ROW]
[ROW][C]83[/C][C]182.22[/C][C]151.770476190476[/C][C]30.4495238095238[/C][/ROW]
[ROW][C]84[/C][C]198.08[/C][C]151.770476190476[/C][C]46.3095238095239[/C][/ROW]
[ROW][C]85[/C][C]135.36[/C][C]151.770476190476[/C][C]-16.4104761904761[/C][/ROW]
[ROW][C]86[/C][C]125.02[/C][C]151.770476190476[/C][C]-26.7504761904762[/C][/ROW]
[ROW][C]87[/C][C]143.5[/C][C]151.770476190476[/C][C]-8.27047619047616[/C][/ROW]
[ROW][C]88[/C][C]173.95[/C][C]151.770476190476[/C][C]22.1795238095238[/C][/ROW]
[ROW][C]89[/C][C]188.75[/C][C]151.770476190476[/C][C]36.9795238095238[/C][/ROW]
[ROW][C]90[/C][C]167.44[/C][C]151.770476190476[/C][C]15.6695238095238[/C][/ROW]
[ROW][C]91[/C][C]158.95[/C][C]151.770476190476[/C][C]7.17952380952383[/C][/ROW]
[ROW][C]92[/C][C]169.53[/C][C]151.770476190476[/C][C]17.7595238095238[/C][/ROW]
[ROW][C]93[/C][C]113.66[/C][C]151.770476190476[/C][C]-38.1104761904762[/C][/ROW]
[ROW][C]94[/C][C]107.59[/C][C]103.97625[/C][C]3.61374999999998[/C][/ROW]
[ROW][C]95[/C][C]92.67[/C][C]103.97625[/C][C]-11.3062500000000[/C][/ROW]
[ROW][C]96[/C][C]85.35[/C][C]103.97625[/C][C]-18.6262500000000[/C][/ROW]
[ROW][C]97[/C][C]90.13[/C][C]103.97625[/C][C]-13.8462500000000[/C][/ROW]
[ROW][C]98[/C][C]89.31[/C][C]103.97625[/C][C]-14.6662500000000[/C][/ROW]
[ROW][C]99[/C][C]105.12[/C][C]103.97625[/C][C]1.14374999999998[/C][/ROW]
[ROW][C]100[/C][C]125.83[/C][C]103.97625[/C][C]21.8537500000000[/C][/ROW]
[ROW][C]101[/C][C]135.81[/C][C]103.97625[/C][C]31.83375[/C][/ROW]
[ROW][C]102[/C][C]142.43[/C][C]151.770476190476[/C][C]-9.34047619047615[/C][/ROW]
[ROW][C]103[/C][C]163.39[/C][C]151.770476190476[/C][C]11.6195238095238[/C][/ROW]
[ROW][C]104[/C][C]168.21[/C][C]151.770476190476[/C][C]16.4395238095238[/C][/ROW]
[ROW][C]105[/C][C]185.35[/C][C]228.443846153846[/C][C]-43.0938461538462[/C][/ROW]
[ROW][C]106[/C][C]188.5[/C][C]228.443846153846[/C][C]-39.9438461538462[/C][/ROW]
[ROW][C]107[/C][C]199.91[/C][C]228.443846153846[/C][C]-28.5338461538462[/C][/ROW]
[ROW][C]108[/C][C]210.73[/C][C]228.443846153846[/C][C]-17.7138461538462[/C][/ROW]
[ROW][C]109[/C][C]192.06[/C][C]228.443846153846[/C][C]-36.3838461538462[/C][/ROW]
[ROW][C]110[/C][C]204.62[/C][C]228.443846153846[/C][C]-23.8238461538461[/C][/ROW]
[ROW][C]111[/C][C]235[/C][C]228.443846153846[/C][C]6.55615384615385[/C][/ROW]
[ROW][C]112[/C][C]261.09[/C][C]228.443846153846[/C][C]32.6461538461538[/C][/ROW]
[ROW][C]113[/C][C]256.88[/C][C]228.443846153846[/C][C]28.4361538461538[/C][/ROW]
[ROW][C]114[/C][C]251.53[/C][C]228.443846153846[/C][C]23.0861538461538[/C][/ROW]
[ROW][C]115[/C][C]257.25[/C][C]228.443846153846[/C][C]28.8061538461538[/C][/ROW]
[ROW][C]116[/C][C]243.1[/C][C]228.443846153846[/C][C]14.6561538461538[/C][/ROW]
[ROW][C]117[/C][C]283.75[/C][C]228.443846153846[/C][C]55.3061538461538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114932&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114932&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	10.83	-0.0199999999999978
2	9.12	10.83	-1.71
3	11.03	10.83	0.200000000000001
4	12.74	10.83	1.91000000000000
5	9.98	10.83	-0.849999999999998
6	11.62	10.83	0.790000000000001
7	9.4	10.83	-1.43000000000000
8	9.27	10.83	-1.56
9	7.76	7.66692307692308	0.0930769230769215
10	8.78	10.83	-2.05
11	10.65	10.83	-0.179999999999998
12	10.95	10.83	0.120000000000001
13	12.36	10.83	1.53
14	10.85	10.83	0.0200000000000014
15	11.84	10.83	1.01000000000000
16	12.14	10.83	1.31000000000000
17	11.65	10.83	0.820000000000002
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	10.83	-1.3
31	10.54	10.83	-0.289999999999999
32	11.31	10.83	0.480000000000002
33	10.36	10.83	-0.469999999999999
34	11.44	10.83	0.610000000000001
35	10.45	10.83	-0.379999999999999
36	10.69	10.83	-0.139999999999999
37	11.28	10.83	0.450000000000001
38	11.96	10.83	1.13000000000000
39	13.52	15.6442857142857	-2.12428571428571
40	12.89	15.6442857142857	-2.75428571428571
41	14.03	15.6442857142857	-1.61428571428571
42	16.27	15.6442857142857	0.625714285714286
43	16.17	15.6442857142857	0.525714285714288
44	17.25	15.6442857142857	1.60571428571429
45	19.38	15.6442857142857	3.73571428571429
46	26.2	37.219	-11.019
47	33.53	37.219	-3.689
48	32.2	37.219	-5.019
49	38.45	37.219	1.23100000000000
50	44.86	37.219	7.641
51	41.67	37.219	4.451
52	36.06	37.219	-1.159
53	39.76	37.219	2.54100000000000
54	36.81	37.219	-0.408999999999999
55	42.65	37.219	5.431
56	46.89	58.7057142857143	-11.8157142857143
57	53.61	58.7057142857143	-5.09571428571428
58	57.59	58.7057142857143	-1.11571428571428
59	67.82	78.0484615384615	-10.2284615384615
60	71.89	78.0484615384615	-6.15846153846154
61	75.51	78.0484615384615	-2.53846153846153
62	68.49	78.0484615384615	-9.55846153846154
63	62.72	78.0484615384615	-15.3284615384615
64	70.39	78.0484615384615	-7.65846153846154
65	59.77	58.7057142857143	1.06428571428572
66	57.27	58.7057142857143	-1.43571428571428
67	67.96	58.7057142857143	9.25428571428571
68	67.85	58.7057142857143	9.14428571428572
69	76.98	78.0484615384615	-1.06846153846153
70	81.08	78.0484615384615	3.03153846153846
71	91.66	78.0484615384615	13.6115384615385
72	84.84	78.0484615384615	6.79153846153847
73	85.73	78.0484615384615	7.68153846153847
74	84.61	78.0484615384615	6.56153846153846
75	92.91	78.0484615384615	14.8615384615385
76	99.8	151.770476190476	-51.9704761904762
77	121.19	151.770476190476	-30.5804761904762
78	122.04	151.770476190476	-29.7304761904762
79	131.76	151.770476190476	-20.0104761904762
80	138.48	151.770476190476	-13.2904761904762
81	153.47	151.770476190476	1.69952380952384
82	189.95	151.770476190476	38.1795238095238
83	182.22	151.770476190476	30.4495238095238
84	198.08	151.770476190476	46.3095238095239
85	135.36	151.770476190476	-16.4104761904761
86	125.02	151.770476190476	-26.7504761904762
87	143.5	151.770476190476	-8.27047619047616
88	173.95	151.770476190476	22.1795238095238
89	188.75	151.770476190476	36.9795238095238
90	167.44	151.770476190476	15.6695238095238
91	158.95	151.770476190476	7.17952380952383
92	169.53	151.770476190476	17.7595238095238
93	113.66	151.770476190476	-38.1104761904762
94	107.59	103.97625	3.61374999999998
95	92.67	103.97625	-11.3062500000000
96	85.35	103.97625	-18.6262500000000
97	90.13	103.97625	-13.8462500000000
98	89.31	103.97625	-14.6662500000000
99	105.12	103.97625	1.14374999999998
100	125.83	103.97625	21.8537500000000
101	135.81	103.97625	31.83375
102	142.43	151.770476190476	-9.34047619047615
103	163.39	151.770476190476	11.6195238095238
104	168.21	151.770476190476	16.4395238095238
105	185.35	228.443846153846	-43.0938461538462
106	188.5	228.443846153846	-39.9438461538462
107	199.91	228.443846153846	-28.5338461538462
108	210.73	228.443846153846	-17.7138461538462
109	192.06	228.443846153846	-36.3838461538462
110	204.62	228.443846153846	-23.8238461538461
111	235	228.443846153846	6.55615384615385
112	261.09	228.443846153846	32.6461538461538
113	256.88	228.443846153846	28.4361538461538
114	251.53	228.443846153846	23.0861538461538
115	257.25	228.443846153846	28.8061538461538
116	243.1	228.443846153846	14.6561538461538
117	283.75	228.443846153846	55.3061538461538

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 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = ; 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