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rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Wed, 22 Dec 2010 11:34:12 +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/22/t12930176800pcwxa9dt800i1o.htm/, Retrieved Mon, 06 May 2024 01:52:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114170, Retrieved Mon, 06 May 2024 01:52:41 +0000

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Estimated Impact

121

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] [ee4a783fb13f41eb2e9bc8a0c4f26279] [Current]
-    D              [Recursive Partitioning (Regression Trees)] [Paper - Recursive...] [2010-12-24 12:31:43] [1f5baf2b24e732d76900bb8178fc04e7] 
-    D                [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-26 15:33:18] [eeb33d252044f8583501f5ba0605ad6d] 

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10.81	-0,2643	0	0	24563400	24.45
9.12	-0,2643	0	0	14163200	23.62
11.03	-0,2643	0	0	18184800	21.90
12.74	-0,1918	0	0	20810300	27.12
9.98	-0,1918	0	0	12843000	27.70
11.62	-0,1918	0	0	13866700	29.23
9.40	-0,2246	0	0	15119200	26.50
9.27	-0,2246	0	0	8301600	22.84
7.76	-0,2246	0	0	14039600	20.49
8.78	0,3654	0	0	12139700	23.28
10.65	0,3654	0	0	9649000	25.71
10.95	0,3654	0	0	8513600	26.52
12.36	0,0447	0	0	15278600	25.51
10.85	0,0447	0	0	15590900	23.36
11.84	0,0447	0	0	9691100	24.15
12.14	-0,0312	0	0	10882700	20.92
11.65	-0,0312	0	0	10294800	20.38
8.86	-0,0312	0	0	16031900	21.90
7.63	-0,0048	0	0	13683600	19.21
7.38	-0,0048	0	0	8677200	19.65
7.25	-0,0048	0	0	9874100	17.51
8.03	0,0705	0	0	10725500	21.41
7.75	0,0705	0	0	8348400	23.09
7.16	0,0705	0	0	8046200	20.70
7.18	-0,0134	0	0	10862300	19.00
7.51	-0,0134	0	0	8100300	19.04
7.07	-0,0134	0	0	7287500	19.45
7.11	0,0812	0	0	14002500	20.54
8.98	0,0812	0	0	19037900	19.77
9.53	0,0812	0	0	10774600	20.60
10.54	0,1885	0	0	8960600	21.21
11.31	0,1885	0	0	7773300	21.30
10.36	0,1885	0	0	9579700	22.33
11.44	0,3628	0	0	11270700	21.12
10.45	0,3628	0	0	9492800	20.77
10.69	0,3628	0	0	9136800	22.11
11.28	0,2942	0	0	14487600	22.34
11.96	0,2942	0	0	10133200	21.43
13.52	0,2942	0	0	18659700	20.14
12.89	0,3036	0	0	15980700	21.11
14.03	0,3036	0	0	9732100	21.19
16.27	0,3036	0	0	14626300	23.07
16.17	0,3703	0	0	16904000	23.01
17.25	0,3703	0	0	13616700	22.12
19.38	0,3703	0	0	13772900	22.40
26.20	0,7398	0	0	28749200	22.66
33.53	0,7398	0	0	31408300	24.21
32.20	0,7398	0	0	26342800	24.13
38.45	0,6988	0	0	48909500	23.73
44.86	0,6988	0	0	41542400	22.79
41.67	0,6988	0	0	24857200	21.89
36.06	0,7478	0	0	34093700	22.92
39.76	0,7478	0	0	22555200	23.44
36.81	0,7478	0	0	19067500	22.57
42.65	0,5651	0	0	19029100	23.27
46.89	0,5651	0	0	15223200	24.95
53.61	0,5651	0	0	21903700	23.45
57.59	0,6473	0	0	33306600	23.42
67.82	0,6473	0	0	23898100	25.30
71.89	0,6473	0	0	23279600	23.90
75.51	0,3441	0	0	40699800	25.73
68.49	0,3441	0	0	37646000	24.64
62.72	0,3441	0	0	37277000	24.95
70.39	0,2415	0	0	39246800	22.15
59.77	0,2415	0	0	27418400	20.85
57.27	0,2415	0	0	30318700	21.45
67.96	0,3151	0	0	32808100	22.15
67.85	0,3151	0	0	28668200	23.75
76.98	0,3151	0	0	32370300	25.27
81.08	0,239	0	0	24171100	26.53
91.66	0,239	0	0	25009100	27.22
84.84	0,239	0	0	32084300	27.69
85.73	0,2127	0	0	50117500	28.61
84.61	0,2127	0	0	27522200	26.21
92.91	0,2127	0	0	26816800	25.93
99.80	0,273	0	0	25136100	27.86
121.19	0,273	0	0	30295600	28.65
122.04	0,273	0,273	0	41526100	27.51
131.76	0,3657	0,3657	0	43845100	27.06
138.48	0,3657	0,3657	0	39188900	26.91
153.47	0,3657	0,3657	0	40496400	27.60
189.95	0,4643	0,4643	0	37438400	34.48
182.22	0,4643	0,4643	0	46553700	31.58
198.08	0,4643	0,4643	0	31771400	33.46
135.36	0,5096	0,5096	0	62108100	30.64
125.02	0,5096	0,5096	0	46645400	25.66
143.50	0,5096	0,5096	0	42313100	26.78
173.95	0,3592	0,3592	0	38841700	26.91
188.75	0,3592	0,3592	0	32650300	26.82
167.44	0,3592	0,3592	0	34281100	26.05
158.95	0,7439	0,7439	0	33096200	24.36
169.53	0,7439	0,7439	0	23273800	25.94
113.66	0,7439	0,7439	0	43697600	25.37
107.59	0,139	0,139	0	66902300	21.23
92.67	0,139	0,139	0	44957200	19.35
85.35	0,139	0,139	0	33800900	18.61
90.13	0,1383	0,1383	0	33487900	16.37
89.31	0,1383	0,1383	0	27394900	15.56
105.12	0,1383	0,1383	0	25963400	17.70
125.83	0,2874	0,2874	0	20952600	19.52
135.81	0,2874	0,2874	0	17702900	20.26
142.43	0,2874	0,2874	0	21282100	23.05
163.39	0,0596	0,0596	0	18449100	22.81
168.21	0,0596	0,0596	0	14415700	24.04
185.35	0,0596	0,0596	0	17906300	25.08
188.50	0,3201	0,3201	0	22197500	27.04
199.91	0,3201	0,3201	0	15856500	28.81
210.73	0,3201	0,3201	0	19068700	29.86
192.06	0,486	0,486	0	30855100	27.61
204.62	0,486	0,486	0	21209000	28.22
235.00	0,486	0,486	0	19541600	28.83
261.09	0,6129	0,6129	0,6129	21955000	30.06
256.88	0,6129	0,6129	0,6129	33725900	25.51
251.53	0,6129	0,6129	0,6129	28192800	22.75
257.25	0,6665	0,6665	0,6665	27377000	25.52
243.10	0,6665	0,6665	0,6665	16228100	23.33
283.75	0,6665	0,6665	0,6665	21278900	24.34

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114170&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114170&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114170&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	8 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.9625
R-squared	0.9264
RMSE	20.5257

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9625 \tabularnewline
R-squared & 0.9264 \tabularnewline
RMSE & 20.5257 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114170&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9625[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9264[/C][/ROW]
[ROW][C]RMSE[/C][C]20.5257[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114170&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114170&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	0.9625
R-squared	0.9264
RMSE	20.5257

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	10.81	49.5305263157895	-38.7205263157895
2	9.12	10.1563157894737	-1.03631578947368
3	11.03	10.1563157894737	0.873684210526315
4	12.74	10.1563157894737	2.58368421052632
5	9.98	10.1563157894737	-0.176315789473684
6	11.62	10.1563157894737	1.46368421052632
7	9.4	10.1563157894737	-0.756315789473684
8	9.27	10.1563157894737	-0.886315789473684
9	7.76	10.1563157894737	-2.39631578947368
10	8.78	23.2811111111111	-14.5011111111111
11	10.65	23.2811111111111	-12.6311111111111
12	10.95	23.2811111111111	-12.3311111111111
13	12.36	10.1563157894737	2.20368421052632
14	10.85	10.1563157894737	0.693684210526316
15	11.84	10.1563157894737	1.68368421052632
16	12.14	10.1563157894737	1.98368421052632
17	11.65	10.1563157894737	1.49368421052632
18	8.86	10.1563157894737	-1.29631578947368
19	7.63	10.1563157894737	-2.52631578947368
20	7.38	10.1563157894737	-2.77631578947368
21	7.25	10.1563157894737	-2.90631578947368
22	8.03	10.1563157894737	-2.12631578947368
23	7.75	10.1563157894737	-2.40631578947368
24	7.16	10.1563157894737	-2.99631578947368
25	7.18	10.1563157894737	-2.97631578947368
26	7.51	10.1563157894737	-2.64631578947368
27	7.07	10.1563157894737	-3.08631578947368
28	7.11	10.1563157894737	-3.04631578947368
29	8.98	10.1563157894737	-1.17631578947368
30	9.53	10.1563157894737	-0.626315789473685
31	10.54	10.1563157894737	0.383684210526315
32	11.31	10.1563157894737	1.15368421052632
33	10.36	10.1563157894737	0.203684210526315
34	11.44	10.1563157894737	1.28368421052632
35	10.45	10.1563157894737	0.293684210526315
36	10.69	10.1563157894737	0.533684210526316
37	11.28	10.1563157894737	1.12368421052632
38	11.96	10.1563157894737	1.80368421052632
39	13.52	10.1563157894737	3.36368421052632
40	12.89	10.1563157894737	2.73368421052632
41	14.03	10.1563157894737	3.87368421052632
42	16.27	10.1563157894737	6.11368421052632
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	-6.38636363636364
71	91.66	87.4663636363636	4.19363636363636
72	84.84	87.4663636363636	-2.62636363636364
73	85.73	87.4663636363636	-1.73636363636363
74	84.61	87.4663636363636	-2.85636363636364
75	92.91	87.4663636363636	5.44363636363636
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	-18.6772727272727
80	138.48	150.437272727273	-11.9572727272727
81	153.47	150.437272727273	3.03272727272727
82	189.95	150.437272727273	39.5127272727273
83	182.22	150.437272727273	31.7827272727273
84	198.08	218.733125	-20.653125
85	135.36	150.437272727273	-15.0772727272727
86	125.02	150.437272727273	-25.4172727272727
87	143.5	150.437272727273	-6.93727272727273
88	173.95	150.437272727273	23.5127272727273
89	188.75	218.733125	-29.983125
90	167.44	150.437272727273	17.0027272727273
91	158.95	218.733125	-59.783125
92	169.53	218.733125	-49.203125
93	113.66	150.437272727273	-36.7772727272727
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	-18.823125
108	210.73	218.733125	-8.00312500000001
109	192.06	218.733125	-26.673125
110	204.62	218.733125	-14.113125
111	235	218.733125	16.266875
112	261.09	218.733125	42.356875
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	24.366875
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 & 10.1563157894737 & -1.03631578947368 \tabularnewline
3 & 11.03 & 10.1563157894737 & 0.873684210526315 \tabularnewline
4 & 12.74 & 10.1563157894737 & 2.58368421052632 \tabularnewline
5 & 9.98 & 10.1563157894737 & -0.176315789473684 \tabularnewline
6 & 11.62 & 10.1563157894737 & 1.46368421052632 \tabularnewline
7 & 9.4 & 10.1563157894737 & -0.756315789473684 \tabularnewline
8 & 9.27 & 10.1563157894737 & -0.886315789473684 \tabularnewline
9 & 7.76 & 10.1563157894737 & -2.39631578947368 \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 & 10.1563157894737 & 2.20368421052632 \tabularnewline
14 & 10.85 & 10.1563157894737 & 0.693684210526316 \tabularnewline
15 & 11.84 & 10.1563157894737 & 1.68368421052632 \tabularnewline
16 & 12.14 & 10.1563157894737 & 1.98368421052632 \tabularnewline
17 & 11.65 & 10.1563157894737 & 1.49368421052632 \tabularnewline
18 & 8.86 & 10.1563157894737 & -1.29631578947368 \tabularnewline
19 & 7.63 & 10.1563157894737 & -2.52631578947368 \tabularnewline
20 & 7.38 & 10.1563157894737 & -2.77631578947368 \tabularnewline
21 & 7.25 & 10.1563157894737 & -2.90631578947368 \tabularnewline
22 & 8.03 & 10.1563157894737 & -2.12631578947368 \tabularnewline
23 & 7.75 & 10.1563157894737 & -2.40631578947368 \tabularnewline
24 & 7.16 & 10.1563157894737 & -2.99631578947368 \tabularnewline
25 & 7.18 & 10.1563157894737 & -2.97631578947368 \tabularnewline
26 & 7.51 & 10.1563157894737 & -2.64631578947368 \tabularnewline
27 & 7.07 & 10.1563157894737 & -3.08631578947368 \tabularnewline
28 & 7.11 & 10.1563157894737 & -3.04631578947368 \tabularnewline
29 & 8.98 & 10.1563157894737 & -1.17631578947368 \tabularnewline
30 & 9.53 & 10.1563157894737 & -0.626315789473685 \tabularnewline
31 & 10.54 & 10.1563157894737 & 0.383684210526315 \tabularnewline
32 & 11.31 & 10.1563157894737 & 1.15368421052632 \tabularnewline
33 & 10.36 & 10.1563157894737 & 0.203684210526315 \tabularnewline
34 & 11.44 & 10.1563157894737 & 1.28368421052632 \tabularnewline
35 & 10.45 & 10.1563157894737 & 0.293684210526315 \tabularnewline
36 & 10.69 & 10.1563157894737 & 0.533684210526316 \tabularnewline
37 & 11.28 & 10.1563157894737 & 1.12368421052632 \tabularnewline
38 & 11.96 & 10.1563157894737 & 1.80368421052632 \tabularnewline
39 & 13.52 & 10.1563157894737 & 3.36368421052632 \tabularnewline
40 & 12.89 & 10.1563157894737 & 2.73368421052632 \tabularnewline
41 & 14.03 & 10.1563157894737 & 3.87368421052632 \tabularnewline
42 & 16.27 & 10.1563157894737 & 6.11368421052632 \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 & -6.38636363636364 \tabularnewline
71 & 91.66 & 87.4663636363636 & 4.19363636363636 \tabularnewline
72 & 84.84 & 87.4663636363636 & -2.62636363636364 \tabularnewline
73 & 85.73 & 87.4663636363636 & -1.73636363636363 \tabularnewline
74 & 84.61 & 87.4663636363636 & -2.85636363636364 \tabularnewline
75 & 92.91 & 87.4663636363636 & 5.44363636363636 \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 & -18.6772727272727 \tabularnewline
80 & 138.48 & 150.437272727273 & -11.9572727272727 \tabularnewline
81 & 153.47 & 150.437272727273 & 3.03272727272727 \tabularnewline
82 & 189.95 & 150.437272727273 & 39.5127272727273 \tabularnewline
83 & 182.22 & 150.437272727273 & 31.7827272727273 \tabularnewline
84 & 198.08 & 218.733125 & -20.653125 \tabularnewline
85 & 135.36 & 150.437272727273 & -15.0772727272727 \tabularnewline
86 & 125.02 & 150.437272727273 & -25.4172727272727 \tabularnewline
87 & 143.5 & 150.437272727273 & -6.93727272727273 \tabularnewline
88 & 173.95 & 150.437272727273 & 23.5127272727273 \tabularnewline
89 & 188.75 & 218.733125 & -29.983125 \tabularnewline
90 & 167.44 & 150.437272727273 & 17.0027272727273 \tabularnewline
91 & 158.95 & 218.733125 & -59.783125 \tabularnewline
92 & 169.53 & 218.733125 & -49.203125 \tabularnewline
93 & 113.66 & 150.437272727273 & -36.7772727272727 \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 & -18.823125 \tabularnewline
108 & 210.73 & 218.733125 & -8.00312500000001 \tabularnewline
109 & 192.06 & 218.733125 & -26.673125 \tabularnewline
110 & 204.62 & 218.733125 & -14.113125 \tabularnewline
111 & 235 & 218.733125 & 16.266875 \tabularnewline
112 & 261.09 & 218.733125 & 42.356875 \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 & 24.366875 \tabularnewline
117 & 283.75 & 218.733125 & 65.016875 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114170&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]10.1563157894737[/C][C]-1.03631578947368[/C][/ROW]
[ROW][C]3[/C][C]11.03[/C][C]10.1563157894737[/C][C]0.873684210526315[/C][/ROW]
[ROW][C]4[/C][C]12.74[/C][C]10.1563157894737[/C][C]2.58368421052632[/C][/ROW]
[ROW][C]5[/C][C]9.98[/C][C]10.1563157894737[/C][C]-0.176315789473684[/C][/ROW]
[ROW][C]6[/C][C]11.62[/C][C]10.1563157894737[/C][C]1.46368421052632[/C][/ROW]
[ROW][C]7[/C][C]9.4[/C][C]10.1563157894737[/C][C]-0.756315789473684[/C][/ROW]
[ROW][C]8[/C][C]9.27[/C][C]10.1563157894737[/C][C]-0.886315789473684[/C][/ROW]
[ROW][C]9[/C][C]7.76[/C][C]10.1563157894737[/C][C]-2.39631578947368[/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]10.1563157894737[/C][C]2.20368421052632[/C][/ROW]
[ROW][C]14[/C][C]10.85[/C][C]10.1563157894737[/C][C]0.693684210526316[/C][/ROW]
[ROW][C]15[/C][C]11.84[/C][C]10.1563157894737[/C][C]1.68368421052632[/C][/ROW]
[ROW][C]16[/C][C]12.14[/C][C]10.1563157894737[/C][C]1.98368421052632[/C][/ROW]
[ROW][C]17[/C][C]11.65[/C][C]10.1563157894737[/C][C]1.49368421052632[/C][/ROW]
[ROW][C]18[/C][C]8.86[/C][C]10.1563157894737[/C][C]-1.29631578947368[/C][/ROW]
[ROW][C]19[/C][C]7.63[/C][C]10.1563157894737[/C][C]-2.52631578947368[/C][/ROW]
[ROW][C]20[/C][C]7.38[/C][C]10.1563157894737[/C][C]-2.77631578947368[/C][/ROW]
[ROW][C]21[/C][C]7.25[/C][C]10.1563157894737[/C][C]-2.90631578947368[/C][/ROW]
[ROW][C]22[/C][C]8.03[/C][C]10.1563157894737[/C][C]-2.12631578947368[/C][/ROW]
[ROW][C]23[/C][C]7.75[/C][C]10.1563157894737[/C][C]-2.40631578947368[/C][/ROW]
[ROW][C]24[/C][C]7.16[/C][C]10.1563157894737[/C][C]-2.99631578947368[/C][/ROW]
[ROW][C]25[/C][C]7.18[/C][C]10.1563157894737[/C][C]-2.97631578947368[/C][/ROW]
[ROW][C]26[/C][C]7.51[/C][C]10.1563157894737[/C][C]-2.64631578947368[/C][/ROW]
[ROW][C]27[/C][C]7.07[/C][C]10.1563157894737[/C][C]-3.08631578947368[/C][/ROW]
[ROW][C]28[/C][C]7.11[/C][C]10.1563157894737[/C][C]-3.04631578947368[/C][/ROW]
[ROW][C]29[/C][C]8.98[/C][C]10.1563157894737[/C][C]-1.17631578947368[/C][/ROW]
[ROW][C]30[/C][C]9.53[/C][C]10.1563157894737[/C][C]-0.626315789473685[/C][/ROW]
[ROW][C]31[/C][C]10.54[/C][C]10.1563157894737[/C][C]0.383684210526315[/C][/ROW]
[ROW][C]32[/C][C]11.31[/C][C]10.1563157894737[/C][C]1.15368421052632[/C][/ROW]
[ROW][C]33[/C][C]10.36[/C][C]10.1563157894737[/C][C]0.203684210526315[/C][/ROW]
[ROW][C]34[/C][C]11.44[/C][C]10.1563157894737[/C][C]1.28368421052632[/C][/ROW]
[ROW][C]35[/C][C]10.45[/C][C]10.1563157894737[/C][C]0.293684210526315[/C][/ROW]
[ROW][C]36[/C][C]10.69[/C][C]10.1563157894737[/C][C]0.533684210526316[/C][/ROW]
[ROW][C]37[/C][C]11.28[/C][C]10.1563157894737[/C][C]1.12368421052632[/C][/ROW]
[ROW][C]38[/C][C]11.96[/C][C]10.1563157894737[/C][C]1.80368421052632[/C][/ROW]
[ROW][C]39[/C][C]13.52[/C][C]10.1563157894737[/C][C]3.36368421052632[/C][/ROW]
[ROW][C]40[/C][C]12.89[/C][C]10.1563157894737[/C][C]2.73368421052632[/C][/ROW]
[ROW][C]41[/C][C]14.03[/C][C]10.1563157894737[/C][C]3.87368421052632[/C][/ROW]
[ROW][C]42[/C][C]16.27[/C][C]10.1563157894737[/C][C]6.11368421052632[/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]-6.38636363636364[/C][/ROW]
[ROW][C]71[/C][C]91.66[/C][C]87.4663636363636[/C][C]4.19363636363636[/C][/ROW]
[ROW][C]72[/C][C]84.84[/C][C]87.4663636363636[/C][C]-2.62636363636364[/C][/ROW]
[ROW][C]73[/C][C]85.73[/C][C]87.4663636363636[/C][C]-1.73636363636363[/C][/ROW]
[ROW][C]74[/C][C]84.61[/C][C]87.4663636363636[/C][C]-2.85636363636364[/C][/ROW]
[ROW][C]75[/C][C]92.91[/C][C]87.4663636363636[/C][C]5.44363636363636[/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]-18.6772727272727[/C][/ROW]
[ROW][C]80[/C][C]138.48[/C][C]150.437272727273[/C][C]-11.9572727272727[/C][/ROW]
[ROW][C]81[/C][C]153.47[/C][C]150.437272727273[/C][C]3.03272727272727[/C][/ROW]
[ROW][C]82[/C][C]189.95[/C][C]150.437272727273[/C][C]39.5127272727273[/C][/ROW]
[ROW][C]83[/C][C]182.22[/C][C]150.437272727273[/C][C]31.7827272727273[/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]-25.4172727272727[/C][/ROW]
[ROW][C]87[/C][C]143.5[/C][C]150.437272727273[/C][C]-6.93727272727273[/C][/ROW]
[ROW][C]88[/C][C]173.95[/C][C]150.437272727273[/C][C]23.5127272727273[/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]17.0027272727273[/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]-36.7772727272727[/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]-18.823125[/C][/ROW]
[ROW][C]108[/C][C]210.73[/C][C]218.733125[/C][C]-8.00312500000001[/C][/ROW]
[ROW][C]109[/C][C]192.06[/C][C]218.733125[/C][C]-26.673125[/C][/ROW]
[ROW][C]110[/C][C]204.62[/C][C]218.733125[/C][C]-14.113125[/C][/ROW]
[ROW][C]111[/C][C]235[/C][C]218.733125[/C][C]16.266875[/C][/ROW]
[ROW][C]112[/C][C]261.09[/C][C]218.733125[/C][C]42.356875[/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]24.366875[/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=114170&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114170&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	10.1563157894737	-1.03631578947368
3	11.03	10.1563157894737	0.873684210526315
4	12.74	10.1563157894737	2.58368421052632
5	9.98	10.1563157894737	-0.176315789473684
6	11.62	10.1563157894737	1.46368421052632
7	9.4	10.1563157894737	-0.756315789473684
8	9.27	10.1563157894737	-0.886315789473684
9	7.76	10.1563157894737	-2.39631578947368
10	8.78	23.2811111111111	-14.5011111111111
11	10.65	23.2811111111111	-12.6311111111111
12	10.95	23.2811111111111	-12.3311111111111
13	12.36	10.1563157894737	2.20368421052632
14	10.85	10.1563157894737	0.693684210526316
15	11.84	10.1563157894737	1.68368421052632
16	12.14	10.1563157894737	1.98368421052632
17	11.65	10.1563157894737	1.49368421052632
18	8.86	10.1563157894737	-1.29631578947368
19	7.63	10.1563157894737	-2.52631578947368
20	7.38	10.1563157894737	-2.77631578947368
21	7.25	10.1563157894737	-2.90631578947368
22	8.03	10.1563157894737	-2.12631578947368
23	7.75	10.1563157894737	-2.40631578947368
24	7.16	10.1563157894737	-2.99631578947368
25	7.18	10.1563157894737	-2.97631578947368
26	7.51	10.1563157894737	-2.64631578947368
27	7.07	10.1563157894737	-3.08631578947368
28	7.11	10.1563157894737	-3.04631578947368
29	8.98	10.1563157894737	-1.17631578947368
30	9.53	10.1563157894737	-0.626315789473685
31	10.54	10.1563157894737	0.383684210526315
32	11.31	10.1563157894737	1.15368421052632
33	10.36	10.1563157894737	0.203684210526315
34	11.44	10.1563157894737	1.28368421052632
35	10.45	10.1563157894737	0.293684210526315
36	10.69	10.1563157894737	0.533684210526316
37	11.28	10.1563157894737	1.12368421052632
38	11.96	10.1563157894737	1.80368421052632
39	13.52	10.1563157894737	3.36368421052632
40	12.89	10.1563157894737	2.73368421052632
41	14.03	10.1563157894737	3.87368421052632
42	16.27	10.1563157894737	6.11368421052632
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	-6.38636363636364
71	91.66	87.4663636363636	4.19363636363636
72	84.84	87.4663636363636	-2.62636363636364
73	85.73	87.4663636363636	-1.73636363636363
74	84.61	87.4663636363636	-2.85636363636364
75	92.91	87.4663636363636	5.44363636363636
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	-18.6772727272727
80	138.48	150.437272727273	-11.9572727272727
81	153.47	150.437272727273	3.03272727272727
82	189.95	150.437272727273	39.5127272727273
83	182.22	150.437272727273	31.7827272727273
84	198.08	218.733125	-20.653125
85	135.36	150.437272727273	-15.0772727272727
86	125.02	150.437272727273	-25.4172727272727
87	143.5	150.437272727273	-6.93727272727273
88	173.95	150.437272727273	23.5127272727273
89	188.75	218.733125	-29.983125
90	167.44	150.437272727273	17.0027272727273
91	158.95	218.733125	-59.783125
92	169.53	218.733125	-49.203125
93	113.66	150.437272727273	-36.7772727272727
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	-18.823125
108	210.73	218.733125	-8.00312500000001
109	192.06	218.733125	-26.673125
110	204.62	218.733125	-14.113125
111	235	218.733125	16.266875
112	261.09	218.733125	42.356875
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	24.366875
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