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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Sun, 12 Aug 2012 09:07:26 -0400

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Aug/12/t1344776858n0ciqswq1772fu4.htm/, Retrieved Sun, 28 Apr 2024 15:17:10 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=169234, Retrieved Sun, 28 Apr 2024 15:17:10 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

160

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

-     [Recursive Partitioning (Regression Trees)] [tree] [2011-12-09 10:01:58] [22f8bc702946f784836540059d0d9516]
-   P   [Recursive Partitioning (Regression Trees)] [Deel 3 Tree] [2011-12-18 15:47:46] [845a0512200c8372fa3331a361537afe]
-    D    [Recursive Partitioning (Regression Trees)] [Deel 3 Tree] [2011-12-18 16:36:53] [845a0512200c8372fa3331a361537afe]
- R P         [Recursive Partitioning (Regression Trees)] [Berekening 19] [2012-08-12 13:07:26] [0b94335bf72158573fe52322b9537409] [Current]
-    D          [Recursive Partitioning (Regression Trees)] [Berekening 19] [2012-08-12 13:11:35] [eb6e95800005ec22b7fd76eead8d8a59] 
-   P             [Recursive Partitioning (Regression Trees)] [Berekening 19] [2012-08-12 13:13:25] [eb6e95800005ec22b7fd76eead8d8a59] 

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Dataseries X:

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Histogram

Boxplots

240	36	278	151036	26258
111	28	84	92556	14881
91	26	129	57803	16957
68	27	89	68701	18717
57	29	60	41152	8464
61	31	60	92596	26641
77	26	67	35728	9724
129	30	124	54002	20323
217	33	100	62897	22557
104	36	166	57021	14612
117	25	302	58092	4362
108	19	135	29245	6372
99	25	255	47007	13448
48	24	84	42009	14349
58	27	58	36022	10881
90	27	94	83700	20517
87	21	114	46456	13275
60	26	58	38844	8030
40	20	90	40078	16318
95	24	104	46609	11252
61	24	94	73116	11219
67	36	86	74990	19365
67	21	94	49598	14426
68	25	127	40400	2570
54	33	49	29010	9863
149	25	90	58534	15657
80	31	113	66616	21224
64	30	113	52143	20488
52	20	74	33629	10265
86	38	59	57476	7768
42	20	111	31076	10462
74	24	59	63369	5989
85	18	109	20465	6200
77	28	55	44663	15329
38	23	70	27525	10698
56	21	48	57786	6837
65	25	88	41211	9188
41	28	76	34711	9556
54	30	54	31172	8247
56	25	68	37477	7028
32	26	56	23284	5753
92	28	119	28549	6852
55	26	171	41554	10615
71	31	61	29351	9289
77	19	81	59147	6185
60	34	89	92901	12964
46	26	71	48140	8895
80	21	41	36205	5299
37	29	97	44810	9435
49	17	50	28735	5389
89	28	69	75043	9970
85	22	40	37403	13958
25	21	160	30165	7233
69	23	87	76542	13178
75	29	72	66856	14884
48	21	72	40715	7820
53	23	89	33287	10165
52	33	92	78950	24369
52	18	37	100674	7642
52	29	65	33277	13823
45	32	267	53349	13073
65	25	152	29653	9422
54	27	111	34241	11580
53	34	62	44093	15604
49	20	61	26757	9831
42	29	127	33994	12840
57	15	81	53216	6616
41	23	55	31032	7987
82	34	142	46154	17327
91	26	154	25629	8874
72	24	158	49830	8058
41	30	87	28113	8683
95	17	87	74608	7192
47	21	54	39644	13400
50	11	74	23110	8374
44	23	84	32665	8208
37	28	59	65897	12112
56	19	84	61281	10170
61	21	51	30874	12396
37	23	56	38610	6873
59	23	32	35139	14146
49	29	60	41194	4260
56	21	65	32683	10566
26	25	66	22527	5953
65	21	57	37941	5322
61	12	80	50008	5413
36	24	48	64622	12310
33	24	46	25820	7573
71	19	81	31141	7230
65	21	89	13310	3394
43	25	49	28470	8191
49	34	164	33797	5162
32	25	132	28263	10226
42	22	46	60132	11270
62	28	60	52338	10837
37	29	36	35378	9791
53	23	66	19249	7488
29	23	35	84205	9184
18	22	50	23494	8181
49	14	137	23333	8022
54	24	49	41622	8620
31	10	32	13018	1350
39	17	46	22698	5141
94	21	83	21055	7945
31	25	58	98177	10623
71	31	105	42249	10138
36	26	44	61849	1661
95	22	71	22883	6900
31	35	49	24460	7162
64	26	49	42005	14697
37	12	27	15292	4574
22	26	72	27084	7509
37	32	79	10956	3495
57	21	71	34545	1900
52	15	72	13497	70
23	17	48	8019	3080
39	12	29	32689	443
23	21	76	21152	4911
20	20	44	31747	6562
26	23	49	14399	4204
52	15	40	10726	3149
27	4	11	2325	593
42	22	49	17215	3456
34	13	30	40911	9570
28	22	37	22346	2102
19	1	63	2781	4
16	4	34	5444	775
15	21	40	4157	1283
24	18	49	53249	3878
22	16	59	5842	522
12	2	19	5752	2416
12	1	15	3895	786
9	2	22	0	0
9	0	7	0	0
13	4	18	2179	548
18	0	1	1423	603
4	0	0	0	0
3	1	5	0	0
3	0	1	0	0
5	0	5	0	0
1	0	0	0	0
0	0	0	0	0
0	0	0	0	0
0	0	0	0	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169234&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=169234&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169234&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Goodness of Fit
Correlation	0.7116
R-squared	0.5064
RMSE	24.2981

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7116 \tabularnewline
R-squared & 0.5064 \tabularnewline
RMSE & 24.2981 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169234&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7116[/C][/ROW]
[ROW][C]R-squared[/C][C]0.5064[/C][/ROW]
[ROW][C]RMSE[/C][C]24.2981[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=169234&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169234&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	0.7116
R-squared	0.5064
RMSE	24.2981

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	240	101.736842105263	138.263157894737
2	111	101.736842105263	9.26315789473684
3	91	101.736842105263	-10.7368421052632
4	68	101.736842105263	-33.7368421052632
5	57	47.5254237288136	9.47457627118644
6	61	67.7142857142857	-6.71428571428571
7	77	47.5254237288136	29.4745762711864
8	129	101.736842105263	27.2631578947368
9	217	101.736842105263	115.263157894737
10	104	101.736842105263	2.26315789473684
11	117	101.736842105263	15.2631578947368
12	108	63.3529411764706	44.6470588235294
13	99	63.3529411764706	35.6470588235294
14	48	63.3529411764706	-15.3529411764706
15	58	47.5254237288136	10.4745762711864
16	90	101.736842105263	-11.7368421052632
17	87	63.3529411764706	23.6470588235294
18	60	47.5254237288136	12.4745762711864
19	40	63.3529411764706	-23.3529411764706
20	95	63.3529411764706	31.6470588235294
21	61	101.736842105263	-40.7368421052632
22	67	101.736842105263	-34.7368421052632
23	67	63.3529411764706	3.64705882352941
24	68	63.3529411764706	4.64705882352941
25	54	47.5254237288136	6.47457627118644
26	149	101.736842105263	47.2631578947368
27	80	101.736842105263	-21.7368421052632
28	64	63.3529411764706	0.647058823529413
29	52	47.5254237288136	4.47457627118644
30	86	47.5254237288136	38.4745762711864
31	42	63.3529411764706	-21.3529411764706
32	74	47.5254237288136	26.4745762711864
33	85	63.3529411764706	21.6470588235294
34	77	67.7142857142857	9.28571428571429
35	38	47.5254237288136	-9.52542372881356
36	56	47.5254237288136	8.47457627118644
37	65	63.3529411764706	1.64705882352941
38	41	47.5254237288136	-6.52542372881356
39	54	47.5254237288136	6.47457627118644
40	56	47.5254237288136	8.47457627118644
41	32	47.5254237288136	-15.5254237288136
42	92	63.3529411764706	28.6470588235294
43	55	63.3529411764706	-8.35294117647059
44	71	47.5254237288136	23.4745762711864
45	77	101.736842105263	-24.7368421052632
46	60	101.736842105263	-41.7368421052632
47	46	47.5254237288136	-1.52542372881356
48	80	47.5254237288136	32.4745762711864
49	37	63.3529411764706	-26.3529411764706
50	49	47.5254237288136	1.47457627118644
51	89	47.5254237288136	41.4745762711864
52	85	67.7142857142857	17.2857142857143
53	25	63.3529411764706	-38.3529411764706
54	69	101.736842105263	-32.7368421052632
55	75	67.7142857142857	7.28571428571429
56	48	47.5254237288136	0.474576271186443
57	53	63.3529411764706	-10.3529411764706
58	52	101.736842105263	-49.7368421052632
59	52	47.5254237288136	4.47457627118644
60	52	47.5254237288136	4.47457627118644
61	45	63.3529411764706	-18.3529411764706
62	65	63.3529411764706	1.64705882352941
63	54	63.3529411764706	-9.35294117647059
64	53	67.7142857142857	-14.7142857142857
65	49	47.5254237288136	1.47457627118644
66	42	63.3529411764706	-21.3529411764706
67	57	63.3529411764706	-6.35294117647059
68	41	47.5254237288136	-6.52542372881356
69	82	63.3529411764706	18.6470588235294
70	91	63.3529411764706	27.6470588235294
71	72	63.3529411764706	8.64705882352941
72	41	63.3529411764706	-22.3529411764706
73	95	101.736842105263	-6.73684210526316
74	47	47.5254237288136	-0.525423728813557
75	50	47.5254237288136	2.47457627118644
76	44	63.3529411764706	-19.3529411764706
77	37	47.5254237288136	-10.5254237288136
78	56	101.736842105263	-45.7368421052632
79	61	47.5254237288136	13.4745762711864
80	37	47.5254237288136	-10.5254237288136
81	59	67.7142857142857	-8.71428571428571
82	49	47.5254237288136	1.47457627118644
83	56	47.5254237288136	8.47457627118644
84	26	47.5254237288136	-21.5254237288136
85	65	47.5254237288136	17.4745762711864
86	61	47.5254237288136	13.4745762711864
87	36	47.5254237288136	-11.5254237288136
88	33	47.5254237288136	-14.5254237288136
89	71	63.3529411764706	7.64705882352941
90	65	63.3529411764706	1.64705882352941
91	43	47.5254237288136	-4.52542372881356
92	49	63.3529411764706	-14.3529411764706
93	32	63.3529411764706	-31.3529411764706
94	42	47.5254237288136	-5.52542372881356
95	62	47.5254237288136	14.4745762711864
96	37	47.5254237288136	-10.5254237288136
97	53	47.5254237288136	5.47457627118644
98	29	47.5254237288136	-18.5254237288136
99	18	47.5254237288136	-29.5254237288136
100	49	63.3529411764706	-14.3529411764706
101	54	47.5254237288136	6.47457627118644
102	31	31.5	-0.5
103	39	47.5254237288136	-8.52542372881356
104	94	63.3529411764706	30.6470588235294
105	31	47.5254237288136	-16.5254237288136
106	71	63.3529411764706	7.64705882352941
107	36	31.5	4.5
108	95	47.5254237288136	47.4745762711864
109	31	47.5254237288136	-16.5254237288136
110	64	67.7142857142857	-3.71428571428571
111	37	47.5254237288136	-10.5254237288136
112	22	47.5254237288136	-25.5254237288136
113	37	47.5254237288136	-10.5254237288136
114	57	31.5	25.5
115	52	31.5	20.5
116	23	47.5254237288136	-24.5254237288136
117	39	31.5	7.5
118	23	47.5254237288136	-24.5254237288136
119	20	47.5254237288136	-27.5254237288136
120	26	47.5254237288136	-21.5254237288136
121	52	47.5254237288136	4.47457627118644
122	27	7.73333333333333	19.2666666666667
123	42	47.5254237288136	-5.52542372881356
124	34	47.5254237288136	-13.5254237288136
125	28	31.5	-3.5
126	19	31.5	-12.5
127	16	31.5	-15.5
128	15	31.5	-16.5
129	24	47.5254237288136	-23.5254237288136
130	22	31.5	-9.5
131	12	7.73333333333333	4.26666666666667
132	12	7.73333333333333	4.26666666666667
133	9	7.73333333333333	1.26666666666667
134	9	7.73333333333333	1.26666666666667
135	13	7.73333333333333	5.26666666666667
136	18	7.73333333333333	10.2666666666667
137	4	7.73333333333333	-3.73333333333333
138	3	7.73333333333333	-4.73333333333333
139	3	7.73333333333333	-4.73333333333333
140	5	7.73333333333333	-2.73333333333333
141	1	7.73333333333333	-6.73333333333333
142	0	7.73333333333333	-7.73333333333333
143	0	7.73333333333333	-7.73333333333333
144	0	7.73333333333333	-7.73333333333333

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 240 & 101.736842105263 & 138.263157894737 \tabularnewline
2 & 111 & 101.736842105263 & 9.26315789473684 \tabularnewline
3 & 91 & 101.736842105263 & -10.7368421052632 \tabularnewline
4 & 68 & 101.736842105263 & -33.7368421052632 \tabularnewline
5 & 57 & 47.5254237288136 & 9.47457627118644 \tabularnewline
6 & 61 & 67.7142857142857 & -6.71428571428571 \tabularnewline
7 & 77 & 47.5254237288136 & 29.4745762711864 \tabularnewline
8 & 129 & 101.736842105263 & 27.2631578947368 \tabularnewline
9 & 217 & 101.736842105263 & 115.263157894737 \tabularnewline
10 & 104 & 101.736842105263 & 2.26315789473684 \tabularnewline
11 & 117 & 101.736842105263 & 15.2631578947368 \tabularnewline
12 & 108 & 63.3529411764706 & 44.6470588235294 \tabularnewline
13 & 99 & 63.3529411764706 & 35.6470588235294 \tabularnewline
14 & 48 & 63.3529411764706 & -15.3529411764706 \tabularnewline
15 & 58 & 47.5254237288136 & 10.4745762711864 \tabularnewline
16 & 90 & 101.736842105263 & -11.7368421052632 \tabularnewline
17 & 87 & 63.3529411764706 & 23.6470588235294 \tabularnewline
18 & 60 & 47.5254237288136 & 12.4745762711864 \tabularnewline
19 & 40 & 63.3529411764706 & -23.3529411764706 \tabularnewline
20 & 95 & 63.3529411764706 & 31.6470588235294 \tabularnewline
21 & 61 & 101.736842105263 & -40.7368421052632 \tabularnewline
22 & 67 & 101.736842105263 & -34.7368421052632 \tabularnewline
23 & 67 & 63.3529411764706 & 3.64705882352941 \tabularnewline
24 & 68 & 63.3529411764706 & 4.64705882352941 \tabularnewline
25 & 54 & 47.5254237288136 & 6.47457627118644 \tabularnewline
26 & 149 & 101.736842105263 & 47.2631578947368 \tabularnewline
27 & 80 & 101.736842105263 & -21.7368421052632 \tabularnewline
28 & 64 & 63.3529411764706 & 0.647058823529413 \tabularnewline
29 & 52 & 47.5254237288136 & 4.47457627118644 \tabularnewline
30 & 86 & 47.5254237288136 & 38.4745762711864 \tabularnewline
31 & 42 & 63.3529411764706 & -21.3529411764706 \tabularnewline
32 & 74 & 47.5254237288136 & 26.4745762711864 \tabularnewline
33 & 85 & 63.3529411764706 & 21.6470588235294 \tabularnewline
34 & 77 & 67.7142857142857 & 9.28571428571429 \tabularnewline
35 & 38 & 47.5254237288136 & -9.52542372881356 \tabularnewline
36 & 56 & 47.5254237288136 & 8.47457627118644 \tabularnewline
37 & 65 & 63.3529411764706 & 1.64705882352941 \tabularnewline
38 & 41 & 47.5254237288136 & -6.52542372881356 \tabularnewline
39 & 54 & 47.5254237288136 & 6.47457627118644 \tabularnewline
40 & 56 & 47.5254237288136 & 8.47457627118644 \tabularnewline
41 & 32 & 47.5254237288136 & -15.5254237288136 \tabularnewline
42 & 92 & 63.3529411764706 & 28.6470588235294 \tabularnewline
43 & 55 & 63.3529411764706 & -8.35294117647059 \tabularnewline
44 & 71 & 47.5254237288136 & 23.4745762711864 \tabularnewline
45 & 77 & 101.736842105263 & -24.7368421052632 \tabularnewline
46 & 60 & 101.736842105263 & -41.7368421052632 \tabularnewline
47 & 46 & 47.5254237288136 & -1.52542372881356 \tabularnewline
48 & 80 & 47.5254237288136 & 32.4745762711864 \tabularnewline
49 & 37 & 63.3529411764706 & -26.3529411764706 \tabularnewline
50 & 49 & 47.5254237288136 & 1.47457627118644 \tabularnewline
51 & 89 & 47.5254237288136 & 41.4745762711864 \tabularnewline
52 & 85 & 67.7142857142857 & 17.2857142857143 \tabularnewline
53 & 25 & 63.3529411764706 & -38.3529411764706 \tabularnewline
54 & 69 & 101.736842105263 & -32.7368421052632 \tabularnewline
55 & 75 & 67.7142857142857 & 7.28571428571429 \tabularnewline
56 & 48 & 47.5254237288136 & 0.474576271186443 \tabularnewline
57 & 53 & 63.3529411764706 & -10.3529411764706 \tabularnewline
58 & 52 & 101.736842105263 & -49.7368421052632 \tabularnewline
59 & 52 & 47.5254237288136 & 4.47457627118644 \tabularnewline
60 & 52 & 47.5254237288136 & 4.47457627118644 \tabularnewline
61 & 45 & 63.3529411764706 & -18.3529411764706 \tabularnewline
62 & 65 & 63.3529411764706 & 1.64705882352941 \tabularnewline
63 & 54 & 63.3529411764706 & -9.35294117647059 \tabularnewline
64 & 53 & 67.7142857142857 & -14.7142857142857 \tabularnewline
65 & 49 & 47.5254237288136 & 1.47457627118644 \tabularnewline
66 & 42 & 63.3529411764706 & -21.3529411764706 \tabularnewline
67 & 57 & 63.3529411764706 & -6.35294117647059 \tabularnewline
68 & 41 & 47.5254237288136 & -6.52542372881356 \tabularnewline
69 & 82 & 63.3529411764706 & 18.6470588235294 \tabularnewline
70 & 91 & 63.3529411764706 & 27.6470588235294 \tabularnewline
71 & 72 & 63.3529411764706 & 8.64705882352941 \tabularnewline
72 & 41 & 63.3529411764706 & -22.3529411764706 \tabularnewline
73 & 95 & 101.736842105263 & -6.73684210526316 \tabularnewline
74 & 47 & 47.5254237288136 & -0.525423728813557 \tabularnewline
75 & 50 & 47.5254237288136 & 2.47457627118644 \tabularnewline
76 & 44 & 63.3529411764706 & -19.3529411764706 \tabularnewline
77 & 37 & 47.5254237288136 & -10.5254237288136 \tabularnewline
78 & 56 & 101.736842105263 & -45.7368421052632 \tabularnewline
79 & 61 & 47.5254237288136 & 13.4745762711864 \tabularnewline
80 & 37 & 47.5254237288136 & -10.5254237288136 \tabularnewline
81 & 59 & 67.7142857142857 & -8.71428571428571 \tabularnewline
82 & 49 & 47.5254237288136 & 1.47457627118644 \tabularnewline
83 & 56 & 47.5254237288136 & 8.47457627118644 \tabularnewline
84 & 26 & 47.5254237288136 & -21.5254237288136 \tabularnewline
85 & 65 & 47.5254237288136 & 17.4745762711864 \tabularnewline
86 & 61 & 47.5254237288136 & 13.4745762711864 \tabularnewline
87 & 36 & 47.5254237288136 & -11.5254237288136 \tabularnewline
88 & 33 & 47.5254237288136 & -14.5254237288136 \tabularnewline
89 & 71 & 63.3529411764706 & 7.64705882352941 \tabularnewline
90 & 65 & 63.3529411764706 & 1.64705882352941 \tabularnewline
91 & 43 & 47.5254237288136 & -4.52542372881356 \tabularnewline
92 & 49 & 63.3529411764706 & -14.3529411764706 \tabularnewline
93 & 32 & 63.3529411764706 & -31.3529411764706 \tabularnewline
94 & 42 & 47.5254237288136 & -5.52542372881356 \tabularnewline
95 & 62 & 47.5254237288136 & 14.4745762711864 \tabularnewline
96 & 37 & 47.5254237288136 & -10.5254237288136 \tabularnewline
97 & 53 & 47.5254237288136 & 5.47457627118644 \tabularnewline
98 & 29 & 47.5254237288136 & -18.5254237288136 \tabularnewline
99 & 18 & 47.5254237288136 & -29.5254237288136 \tabularnewline
100 & 49 & 63.3529411764706 & -14.3529411764706 \tabularnewline
101 & 54 & 47.5254237288136 & 6.47457627118644 \tabularnewline
102 & 31 & 31.5 & -0.5 \tabularnewline
103 & 39 & 47.5254237288136 & -8.52542372881356 \tabularnewline
104 & 94 & 63.3529411764706 & 30.6470588235294 \tabularnewline
105 & 31 & 47.5254237288136 & -16.5254237288136 \tabularnewline
106 & 71 & 63.3529411764706 & 7.64705882352941 \tabularnewline
107 & 36 & 31.5 & 4.5 \tabularnewline
108 & 95 & 47.5254237288136 & 47.4745762711864 \tabularnewline
109 & 31 & 47.5254237288136 & -16.5254237288136 \tabularnewline
110 & 64 & 67.7142857142857 & -3.71428571428571 \tabularnewline
111 & 37 & 47.5254237288136 & -10.5254237288136 \tabularnewline
112 & 22 & 47.5254237288136 & -25.5254237288136 \tabularnewline
113 & 37 & 47.5254237288136 & -10.5254237288136 \tabularnewline
114 & 57 & 31.5 & 25.5 \tabularnewline
115 & 52 & 31.5 & 20.5 \tabularnewline
116 & 23 & 47.5254237288136 & -24.5254237288136 \tabularnewline
117 & 39 & 31.5 & 7.5 \tabularnewline
118 & 23 & 47.5254237288136 & -24.5254237288136 \tabularnewline
119 & 20 & 47.5254237288136 & -27.5254237288136 \tabularnewline
120 & 26 & 47.5254237288136 & -21.5254237288136 \tabularnewline
121 & 52 & 47.5254237288136 & 4.47457627118644 \tabularnewline
122 & 27 & 7.73333333333333 & 19.2666666666667 \tabularnewline
123 & 42 & 47.5254237288136 & -5.52542372881356 \tabularnewline
124 & 34 & 47.5254237288136 & -13.5254237288136 \tabularnewline
125 & 28 & 31.5 & -3.5 \tabularnewline
126 & 19 & 31.5 & -12.5 \tabularnewline
127 & 16 & 31.5 & -15.5 \tabularnewline
128 & 15 & 31.5 & -16.5 \tabularnewline
129 & 24 & 47.5254237288136 & -23.5254237288136 \tabularnewline
130 & 22 & 31.5 & -9.5 \tabularnewline
131 & 12 & 7.73333333333333 & 4.26666666666667 \tabularnewline
132 & 12 & 7.73333333333333 & 4.26666666666667 \tabularnewline
133 & 9 & 7.73333333333333 & 1.26666666666667 \tabularnewline
134 & 9 & 7.73333333333333 & 1.26666666666667 \tabularnewline
135 & 13 & 7.73333333333333 & 5.26666666666667 \tabularnewline
136 & 18 & 7.73333333333333 & 10.2666666666667 \tabularnewline
137 & 4 & 7.73333333333333 & -3.73333333333333 \tabularnewline
138 & 3 & 7.73333333333333 & -4.73333333333333 \tabularnewline
139 & 3 & 7.73333333333333 & -4.73333333333333 \tabularnewline
140 & 5 & 7.73333333333333 & -2.73333333333333 \tabularnewline
141 & 1 & 7.73333333333333 & -6.73333333333333 \tabularnewline
142 & 0 & 7.73333333333333 & -7.73333333333333 \tabularnewline
143 & 0 & 7.73333333333333 & -7.73333333333333 \tabularnewline
144 & 0 & 7.73333333333333 & -7.73333333333333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=169234&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]240[/C][C]101.736842105263[/C][C]138.263157894737[/C][/ROW]
[ROW][C]2[/C][C]111[/C][C]101.736842105263[/C][C]9.26315789473684[/C][/ROW]
[ROW][C]3[/C][C]91[/C][C]101.736842105263[/C][C]-10.7368421052632[/C][/ROW]
[ROW][C]4[/C][C]68[/C][C]101.736842105263[/C][C]-33.7368421052632[/C][/ROW]
[ROW][C]5[/C][C]57[/C][C]47.5254237288136[/C][C]9.47457627118644[/C][/ROW]
[ROW][C]6[/C][C]61[/C][C]67.7142857142857[/C][C]-6.71428571428571[/C][/ROW]
[ROW][C]7[/C][C]77[/C][C]47.5254237288136[/C][C]29.4745762711864[/C][/ROW]
[ROW][C]8[/C][C]129[/C][C]101.736842105263[/C][C]27.2631578947368[/C][/ROW]
[ROW][C]9[/C][C]217[/C][C]101.736842105263[/C][C]115.263157894737[/C][/ROW]
[ROW][C]10[/C][C]104[/C][C]101.736842105263[/C][C]2.26315789473684[/C][/ROW]
[ROW][C]11[/C][C]117[/C][C]101.736842105263[/C][C]15.2631578947368[/C][/ROW]
[ROW][C]12[/C][C]108[/C][C]63.3529411764706[/C][C]44.6470588235294[/C][/ROW]
[ROW][C]13[/C][C]99[/C][C]63.3529411764706[/C][C]35.6470588235294[/C][/ROW]
[ROW][C]14[/C][C]48[/C][C]63.3529411764706[/C][C]-15.3529411764706[/C][/ROW]
[ROW][C]15[/C][C]58[/C][C]47.5254237288136[/C][C]10.4745762711864[/C][/ROW]
[ROW][C]16[/C][C]90[/C][C]101.736842105263[/C][C]-11.7368421052632[/C][/ROW]
[ROW][C]17[/C][C]87[/C][C]63.3529411764706[/C][C]23.6470588235294[/C][/ROW]
[ROW][C]18[/C][C]60[/C][C]47.5254237288136[/C][C]12.4745762711864[/C][/ROW]
[ROW][C]19[/C][C]40[/C][C]63.3529411764706[/C][C]-23.3529411764706[/C][/ROW]
[ROW][C]20[/C][C]95[/C][C]63.3529411764706[/C][C]31.6470588235294[/C][/ROW]
[ROW][C]21[/C][C]61[/C][C]101.736842105263[/C][C]-40.7368421052632[/C][/ROW]
[ROW][C]22[/C][C]67[/C][C]101.736842105263[/C][C]-34.7368421052632[/C][/ROW]
[ROW][C]23[/C][C]67[/C][C]63.3529411764706[/C][C]3.64705882352941[/C][/ROW]
[ROW][C]24[/C][C]68[/C][C]63.3529411764706[/C][C]4.64705882352941[/C][/ROW]
[ROW][C]25[/C][C]54[/C][C]47.5254237288136[/C][C]6.47457627118644[/C][/ROW]
[ROW][C]26[/C][C]149[/C][C]101.736842105263[/C][C]47.2631578947368[/C][/ROW]
[ROW][C]27[/C][C]80[/C][C]101.736842105263[/C][C]-21.7368421052632[/C][/ROW]
[ROW][C]28[/C][C]64[/C][C]63.3529411764706[/C][C]0.647058823529413[/C][/ROW]
[ROW][C]29[/C][C]52[/C][C]47.5254237288136[/C][C]4.47457627118644[/C][/ROW]
[ROW][C]30[/C][C]86[/C][C]47.5254237288136[/C][C]38.4745762711864[/C][/ROW]
[ROW][C]31[/C][C]42[/C][C]63.3529411764706[/C][C]-21.3529411764706[/C][/ROW]
[ROW][C]32[/C][C]74[/C][C]47.5254237288136[/C][C]26.4745762711864[/C][/ROW]
[ROW][C]33[/C][C]85[/C][C]63.3529411764706[/C][C]21.6470588235294[/C][/ROW]
[ROW][C]34[/C][C]77[/C][C]67.7142857142857[/C][C]9.28571428571429[/C][/ROW]
[ROW][C]35[/C][C]38[/C][C]47.5254237288136[/C][C]-9.52542372881356[/C][/ROW]
[ROW][C]36[/C][C]56[/C][C]47.5254237288136[/C][C]8.47457627118644[/C][/ROW]
[ROW][C]37[/C][C]65[/C][C]63.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]38[/C][C]41[/C][C]47.5254237288136[/C][C]-6.52542372881356[/C][/ROW]
[ROW][C]39[/C][C]54[/C][C]47.5254237288136[/C][C]6.47457627118644[/C][/ROW]
[ROW][C]40[/C][C]56[/C][C]47.5254237288136[/C][C]8.47457627118644[/C][/ROW]
[ROW][C]41[/C][C]32[/C][C]47.5254237288136[/C][C]-15.5254237288136[/C][/ROW]
[ROW][C]42[/C][C]92[/C][C]63.3529411764706[/C][C]28.6470588235294[/C][/ROW]
[ROW][C]43[/C][C]55[/C][C]63.3529411764706[/C][C]-8.35294117647059[/C][/ROW]
[ROW][C]44[/C][C]71[/C][C]47.5254237288136[/C][C]23.4745762711864[/C][/ROW]
[ROW][C]45[/C][C]77[/C][C]101.736842105263[/C][C]-24.7368421052632[/C][/ROW]
[ROW][C]46[/C][C]60[/C][C]101.736842105263[/C][C]-41.7368421052632[/C][/ROW]
[ROW][C]47[/C][C]46[/C][C]47.5254237288136[/C][C]-1.52542372881356[/C][/ROW]
[ROW][C]48[/C][C]80[/C][C]47.5254237288136[/C][C]32.4745762711864[/C][/ROW]
[ROW][C]49[/C][C]37[/C][C]63.3529411764706[/C][C]-26.3529411764706[/C][/ROW]
[ROW][C]50[/C][C]49[/C][C]47.5254237288136[/C][C]1.47457627118644[/C][/ROW]
[ROW][C]51[/C][C]89[/C][C]47.5254237288136[/C][C]41.4745762711864[/C][/ROW]
[ROW][C]52[/C][C]85[/C][C]67.7142857142857[/C][C]17.2857142857143[/C][/ROW]
[ROW][C]53[/C][C]25[/C][C]63.3529411764706[/C][C]-38.3529411764706[/C][/ROW]
[ROW][C]54[/C][C]69[/C][C]101.736842105263[/C][C]-32.7368421052632[/C][/ROW]
[ROW][C]55[/C][C]75[/C][C]67.7142857142857[/C][C]7.28571428571429[/C][/ROW]
[ROW][C]56[/C][C]48[/C][C]47.5254237288136[/C][C]0.474576271186443[/C][/ROW]
[ROW][C]57[/C][C]53[/C][C]63.3529411764706[/C][C]-10.3529411764706[/C][/ROW]
[ROW][C]58[/C][C]52[/C][C]101.736842105263[/C][C]-49.7368421052632[/C][/ROW]
[ROW][C]59[/C][C]52[/C][C]47.5254237288136[/C][C]4.47457627118644[/C][/ROW]
[ROW][C]60[/C][C]52[/C][C]47.5254237288136[/C][C]4.47457627118644[/C][/ROW]
[ROW][C]61[/C][C]45[/C][C]63.3529411764706[/C][C]-18.3529411764706[/C][/ROW]
[ROW][C]62[/C][C]65[/C][C]63.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]63[/C][C]54[/C][C]63.3529411764706[/C][C]-9.35294117647059[/C][/ROW]
[ROW][C]64[/C][C]53[/C][C]67.7142857142857[/C][C]-14.7142857142857[/C][/ROW]
[ROW][C]65[/C][C]49[/C][C]47.5254237288136[/C][C]1.47457627118644[/C][/ROW]
[ROW][C]66[/C][C]42[/C][C]63.3529411764706[/C][C]-21.3529411764706[/C][/ROW]
[ROW][C]67[/C][C]57[/C][C]63.3529411764706[/C][C]-6.35294117647059[/C][/ROW]
[ROW][C]68[/C][C]41[/C][C]47.5254237288136[/C][C]-6.52542372881356[/C][/ROW]
[ROW][C]69[/C][C]82[/C][C]63.3529411764706[/C][C]18.6470588235294[/C][/ROW]
[ROW][C]70[/C][C]91[/C][C]63.3529411764706[/C][C]27.6470588235294[/C][/ROW]
[ROW][C]71[/C][C]72[/C][C]63.3529411764706[/C][C]8.64705882352941[/C][/ROW]
[ROW][C]72[/C][C]41[/C][C]63.3529411764706[/C][C]-22.3529411764706[/C][/ROW]
[ROW][C]73[/C][C]95[/C][C]101.736842105263[/C][C]-6.73684210526316[/C][/ROW]
[ROW][C]74[/C][C]47[/C][C]47.5254237288136[/C][C]-0.525423728813557[/C][/ROW]
[ROW][C]75[/C][C]50[/C][C]47.5254237288136[/C][C]2.47457627118644[/C][/ROW]
[ROW][C]76[/C][C]44[/C][C]63.3529411764706[/C][C]-19.3529411764706[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]47.5254237288136[/C][C]-10.5254237288136[/C][/ROW]
[ROW][C]78[/C][C]56[/C][C]101.736842105263[/C][C]-45.7368421052632[/C][/ROW]
[ROW][C]79[/C][C]61[/C][C]47.5254237288136[/C][C]13.4745762711864[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]47.5254237288136[/C][C]-10.5254237288136[/C][/ROW]
[ROW][C]81[/C][C]59[/C][C]67.7142857142857[/C][C]-8.71428571428571[/C][/ROW]
[ROW][C]82[/C][C]49[/C][C]47.5254237288136[/C][C]1.47457627118644[/C][/ROW]
[ROW][C]83[/C][C]56[/C][C]47.5254237288136[/C][C]8.47457627118644[/C][/ROW]
[ROW][C]84[/C][C]26[/C][C]47.5254237288136[/C][C]-21.5254237288136[/C][/ROW]
[ROW][C]85[/C][C]65[/C][C]47.5254237288136[/C][C]17.4745762711864[/C][/ROW]
[ROW][C]86[/C][C]61[/C][C]47.5254237288136[/C][C]13.4745762711864[/C][/ROW]
[ROW][C]87[/C][C]36[/C][C]47.5254237288136[/C][C]-11.5254237288136[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]47.5254237288136[/C][C]-14.5254237288136[/C][/ROW]
[ROW][C]89[/C][C]71[/C][C]63.3529411764706[/C][C]7.64705882352941[/C][/ROW]
[ROW][C]90[/C][C]65[/C][C]63.3529411764706[/C][C]1.64705882352941[/C][/ROW]
[ROW][C]91[/C][C]43[/C][C]47.5254237288136[/C][C]-4.52542372881356[/C][/ROW]
[ROW][C]92[/C][C]49[/C][C]63.3529411764706[/C][C]-14.3529411764706[/C][/ROW]
[ROW][C]93[/C][C]32[/C][C]63.3529411764706[/C][C]-31.3529411764706[/C][/ROW]
[ROW][C]94[/C][C]42[/C][C]47.5254237288136[/C][C]-5.52542372881356[/C][/ROW]
[ROW][C]95[/C][C]62[/C][C]47.5254237288136[/C][C]14.4745762711864[/C][/ROW]
[ROW][C]96[/C][C]37[/C][C]47.5254237288136[/C][C]-10.5254237288136[/C][/ROW]
[ROW][C]97[/C][C]53[/C][C]47.5254237288136[/C][C]5.47457627118644[/C][/ROW]
[ROW][C]98[/C][C]29[/C][C]47.5254237288136[/C][C]-18.5254237288136[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]47.5254237288136[/C][C]-29.5254237288136[/C][/ROW]
[ROW][C]100[/C][C]49[/C][C]63.3529411764706[/C][C]-14.3529411764706[/C][/ROW]
[ROW][C]101[/C][C]54[/C][C]47.5254237288136[/C][C]6.47457627118644[/C][/ROW]
[ROW][C]102[/C][C]31[/C][C]31.5[/C][C]-0.5[/C][/ROW]
[ROW][C]103[/C][C]39[/C][C]47.5254237288136[/C][C]-8.52542372881356[/C][/ROW]
[ROW][C]104[/C][C]94[/C][C]63.3529411764706[/C][C]30.6470588235294[/C][/ROW]
[ROW][C]105[/C][C]31[/C][C]47.5254237288136[/C][C]-16.5254237288136[/C][/ROW]
[ROW][C]106[/C][C]71[/C][C]63.3529411764706[/C][C]7.64705882352941[/C][/ROW]
[ROW][C]107[/C][C]36[/C][C]31.5[/C][C]4.5[/C][/ROW]
[ROW][C]108[/C][C]95[/C][C]47.5254237288136[/C][C]47.4745762711864[/C][/ROW]
[ROW][C]109[/C][C]31[/C][C]47.5254237288136[/C][C]-16.5254237288136[/C][/ROW]
[ROW][C]110[/C][C]64[/C][C]67.7142857142857[/C][C]-3.71428571428571[/C][/ROW]
[ROW][C]111[/C][C]37[/C][C]47.5254237288136[/C][C]-10.5254237288136[/C][/ROW]
[ROW][C]112[/C][C]22[/C][C]47.5254237288136[/C][C]-25.5254237288136[/C][/ROW]
[ROW][C]113[/C][C]37[/C][C]47.5254237288136[/C][C]-10.5254237288136[/C][/ROW]
[ROW][C]114[/C][C]57[/C][C]31.5[/C][C]25.5[/C][/ROW]
[ROW][C]115[/C][C]52[/C][C]31.5[/C][C]20.5[/C][/ROW]
[ROW][C]116[/C][C]23[/C][C]47.5254237288136[/C][C]-24.5254237288136[/C][/ROW]
[ROW][C]117[/C][C]39[/C][C]31.5[/C][C]7.5[/C][/ROW]
[ROW][C]118[/C][C]23[/C][C]47.5254237288136[/C][C]-24.5254237288136[/C][/ROW]
[ROW][C]119[/C][C]20[/C][C]47.5254237288136[/C][C]-27.5254237288136[/C][/ROW]
[ROW][C]120[/C][C]26[/C][C]47.5254237288136[/C][C]-21.5254237288136[/C][/ROW]
[ROW][C]121[/C][C]52[/C][C]47.5254237288136[/C][C]4.47457627118644[/C][/ROW]
[ROW][C]122[/C][C]27[/C][C]7.73333333333333[/C][C]19.2666666666667[/C][/ROW]
[ROW][C]123[/C][C]42[/C][C]47.5254237288136[/C][C]-5.52542372881356[/C][/ROW]
[ROW][C]124[/C][C]34[/C][C]47.5254237288136[/C][C]-13.5254237288136[/C][/ROW]
[ROW][C]125[/C][C]28[/C][C]31.5[/C][C]-3.5[/C][/ROW]
[ROW][C]126[/C][C]19[/C][C]31.5[/C][C]-12.5[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]31.5[/C][C]-15.5[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]31.5[/C][C]-16.5[/C][/ROW]
[ROW][C]129[/C][C]24[/C][C]47.5254237288136[/C][C]-23.5254237288136[/C][/ROW]
[ROW][C]130[/C][C]22[/C][C]31.5[/C][C]-9.5[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]7.73333333333333[/C][C]4.26666666666667[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]7.73333333333333[/C][C]4.26666666666667[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]7.73333333333333[/C][C]1.26666666666667[/C][/ROW]
[ROW][C]134[/C][C]9[/C][C]7.73333333333333[/C][C]1.26666666666667[/C][/ROW]
[ROW][C]135[/C][C]13[/C][C]7.73333333333333[/C][C]5.26666666666667[/C][/ROW]
[ROW][C]136[/C][C]18[/C][C]7.73333333333333[/C][C]10.2666666666667[/C][/ROW]
[ROW][C]137[/C][C]4[/C][C]7.73333333333333[/C][C]-3.73333333333333[/C][/ROW]
[ROW][C]138[/C][C]3[/C][C]7.73333333333333[/C][C]-4.73333333333333[/C][/ROW]
[ROW][C]139[/C][C]3[/C][C]7.73333333333333[/C][C]-4.73333333333333[/C][/ROW]
[ROW][C]140[/C][C]5[/C][C]7.73333333333333[/C][C]-2.73333333333333[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]7.73333333333333[/C][C]-6.73333333333333[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]7.73333333333333[/C][C]-7.73333333333333[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]7.73333333333333[/C][C]-7.73333333333333[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]7.73333333333333[/C][C]-7.73333333333333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=169234&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=169234&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	240	101.736842105263	138.263157894737
2	111	101.736842105263	9.26315789473684
3	91	101.736842105263	-10.7368421052632
4	68	101.736842105263	-33.7368421052632
5	57	47.5254237288136	9.47457627118644
6	61	67.7142857142857	-6.71428571428571
7	77	47.5254237288136	29.4745762711864
8	129	101.736842105263	27.2631578947368
9	217	101.736842105263	115.263157894737
10	104	101.736842105263	2.26315789473684
11	117	101.736842105263	15.2631578947368
12	108	63.3529411764706	44.6470588235294
13	99	63.3529411764706	35.6470588235294
14	48	63.3529411764706	-15.3529411764706
15	58	47.5254237288136	10.4745762711864
16	90	101.736842105263	-11.7368421052632
17	87	63.3529411764706	23.6470588235294
18	60	47.5254237288136	12.4745762711864
19	40	63.3529411764706	-23.3529411764706
20	95	63.3529411764706	31.6470588235294
21	61	101.736842105263	-40.7368421052632
22	67	101.736842105263	-34.7368421052632
23	67	63.3529411764706	3.64705882352941
24	68	63.3529411764706	4.64705882352941
25	54	47.5254237288136	6.47457627118644
26	149	101.736842105263	47.2631578947368
27	80	101.736842105263	-21.7368421052632
28	64	63.3529411764706	0.647058823529413
29	52	47.5254237288136	4.47457627118644
30	86	47.5254237288136	38.4745762711864
31	42	63.3529411764706	-21.3529411764706
32	74	47.5254237288136	26.4745762711864
33	85	63.3529411764706	21.6470588235294
34	77	67.7142857142857	9.28571428571429
35	38	47.5254237288136	-9.52542372881356
36	56	47.5254237288136	8.47457627118644
37	65	63.3529411764706	1.64705882352941
38	41	47.5254237288136	-6.52542372881356
39	54	47.5254237288136	6.47457627118644
40	56	47.5254237288136	8.47457627118644
41	32	47.5254237288136	-15.5254237288136
42	92	63.3529411764706	28.6470588235294
43	55	63.3529411764706	-8.35294117647059
44	71	47.5254237288136	23.4745762711864
45	77	101.736842105263	-24.7368421052632
46	60	101.736842105263	-41.7368421052632
47	46	47.5254237288136	-1.52542372881356
48	80	47.5254237288136	32.4745762711864
49	37	63.3529411764706	-26.3529411764706
50	49	47.5254237288136	1.47457627118644
51	89	47.5254237288136	41.4745762711864
52	85	67.7142857142857	17.2857142857143
53	25	63.3529411764706	-38.3529411764706
54	69	101.736842105263	-32.7368421052632
55	75	67.7142857142857	7.28571428571429
56	48	47.5254237288136	0.474576271186443
57	53	63.3529411764706	-10.3529411764706
58	52	101.736842105263	-49.7368421052632
59	52	47.5254237288136	4.47457627118644
60	52	47.5254237288136	4.47457627118644
61	45	63.3529411764706	-18.3529411764706
62	65	63.3529411764706	1.64705882352941
63	54	63.3529411764706	-9.35294117647059
64	53	67.7142857142857	-14.7142857142857
65	49	47.5254237288136	1.47457627118644
66	42	63.3529411764706	-21.3529411764706
67	57	63.3529411764706	-6.35294117647059
68	41	47.5254237288136	-6.52542372881356
69	82	63.3529411764706	18.6470588235294
70	91	63.3529411764706	27.6470588235294
71	72	63.3529411764706	8.64705882352941
72	41	63.3529411764706	-22.3529411764706
73	95	101.736842105263	-6.73684210526316
74	47	47.5254237288136	-0.525423728813557
75	50	47.5254237288136	2.47457627118644
76	44	63.3529411764706	-19.3529411764706
77	37	47.5254237288136	-10.5254237288136
78	56	101.736842105263	-45.7368421052632
79	61	47.5254237288136	13.4745762711864
80	37	47.5254237288136	-10.5254237288136
81	59	67.7142857142857	-8.71428571428571
82	49	47.5254237288136	1.47457627118644
83	56	47.5254237288136	8.47457627118644
84	26	47.5254237288136	-21.5254237288136
85	65	47.5254237288136	17.4745762711864
86	61	47.5254237288136	13.4745762711864
87	36	47.5254237288136	-11.5254237288136
88	33	47.5254237288136	-14.5254237288136
89	71	63.3529411764706	7.64705882352941
90	65	63.3529411764706	1.64705882352941
91	43	47.5254237288136	-4.52542372881356
92	49	63.3529411764706	-14.3529411764706
93	32	63.3529411764706	-31.3529411764706
94	42	47.5254237288136	-5.52542372881356
95	62	47.5254237288136	14.4745762711864
96	37	47.5254237288136	-10.5254237288136
97	53	47.5254237288136	5.47457627118644
98	29	47.5254237288136	-18.5254237288136
99	18	47.5254237288136	-29.5254237288136
100	49	63.3529411764706	-14.3529411764706
101	54	47.5254237288136	6.47457627118644
102	31	31.5	-0.5
103	39	47.5254237288136	-8.52542372881356
104	94	63.3529411764706	30.6470588235294
105	31	47.5254237288136	-16.5254237288136
106	71	63.3529411764706	7.64705882352941
107	36	31.5	4.5
108	95	47.5254237288136	47.4745762711864
109	31	47.5254237288136	-16.5254237288136
110	64	67.7142857142857	-3.71428571428571
111	37	47.5254237288136	-10.5254237288136
112	22	47.5254237288136	-25.5254237288136
113	37	47.5254237288136	-10.5254237288136
114	57	31.5	25.5
115	52	31.5	20.5
116	23	47.5254237288136	-24.5254237288136
117	39	31.5	7.5
118	23	47.5254237288136	-24.5254237288136
119	20	47.5254237288136	-27.5254237288136
120	26	47.5254237288136	-21.5254237288136
121	52	47.5254237288136	4.47457627118644
122	27	7.73333333333333	19.2666666666667
123	42	47.5254237288136	-5.52542372881356
124	34	47.5254237288136	-13.5254237288136
125	28	31.5	-3.5
126	19	31.5	-12.5
127	16	31.5	-15.5
128	15	31.5	-16.5
129	24	47.5254237288136	-23.5254237288136
130	22	31.5	-9.5
131	12	7.73333333333333	4.26666666666667
132	12	7.73333333333333	4.26666666666667
133	9	7.73333333333333	1.26666666666667
134	9	7.73333333333333	1.26666666666667
135	13	7.73333333333333	5.26666666666667
136	18	7.73333333333333	10.2666666666667
137	4	7.73333333333333	-3.73333333333333
138	3	7.73333333333333	-4.73333333333333
139	3	7.73333333333333	-4.73333333333333
140	5	7.73333333333333	-2.73333333333333
141	1	7.73333333333333	-6.73333333333333
142	0	7.73333333333333	-7.73333333333333
143	0	7.73333333333333	-7.73333333333333
144	0	7.73333333333333	-7.73333333333333

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