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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

Thu, 15 Dec 2011 13:59:13 -0500

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/2011/Dec/15/t1323975578dx2eeju7e5ruzhw.htm/, Retrieved Mon, 29 Apr 2024 12:37:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155645, Retrieved Mon, 29 Apr 2024 12:37:23 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

123

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 19:35:21] [b98453cac15ba1066b407e146608df68]
-   PD    [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2011-12-15 18:59:13] [614dd89c388120cee0dd25886939832b] [Current]
-           [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2011-12-15 21:21:46] [15a5dd358825f04074b70fc847ec6454] 
-             [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2011-12-15 21:59:03] [15a5dd358825f04074b70fc847ec6454] 
- R PD      [Recursive Partitioning (Regression Trees)] [PAPER] [2011-12-17 14:55:35] [27e29806e0b1d1351a97bc4ee4116294] 
- RMPD      [Multiple Regression] [PAPER] [2011-12-17 15:10:41] [27e29806e0b1d1351a97bc4ee4116294] 
- RMPD      [Multiple Regression] [PAPER] [2011-12-17 15:15:20] [27e29806e0b1d1351a97bc4ee4116294] 

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Dataseries X:

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Histogram

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70	500	20	18	129404	22622	28
68	329	38	17	130358	73570	39
0	72	0	0	7215	1929	0
68	584	49	22	112861	36294	54
120	1100	76	30	219904	62378	80
120	1585	104	31	397355	167760	144
72	442	37	19	117604	52443	36
96	321	53	25	126737	57283	48
109	406	42	30	99729	36614	42
104	818	62	26	256310	93268	71
54	568	50	20	113066	35439	49
98	556	65	25	157228	72405	74
49	494	28	15	69952	24044	27
88	818	48	22	152673	55909	83
57	338	42	16	130642	44689	31
74	419	47	19	125769	49319	28
112	364	71	28	123467	62075	98
45	284	0	12	56232	2341	2
110	674	50	28	108330	40551	43
39	188	12	13	22762	11621	24
55	286	16	14	48554	18741	16
102	640	77	27	182081	84202	95
96	520	29	25	140857	15334	22
86	532	38	30	93773	28024	33
78	547	50	21	133398	53306	45
64	428	33	17	113933	37918	59
82	561	49	22	153851	54819	66
100	266	59	28	140711	89058	70
99	783	55	26	303804	103354	56
67	746	40	17	161651	70239	55
87	394	40	23	123344	33045	27
65	482	51	20	157640	63852	37
43	568	41	11	91279	30905	48
80	746	73	20	189374	24242	26
84	668	51	21	178768	78907	64
0	0	0	0	0	0	0
105	835	46	27	175403	36005	21
51	464	44	14	92342	31972	44
98	418	31	29	100023	35853	36
124	607	71	31	178277	115301	89
75	539	61	19	145062	47689	101
120	519	28	30	110980	34223	31
84	309	21	23	86039	43431	65
82	647	42	21	125481	52220	71
87	321	44	22	95535	33863	102
78	261	40	21	126456	46879	53
97	180	15	32	61554	23228	41
76	576	46	19	164752	42827	46
104	544	43	26	159121	65765	37
93	758	47	25	129362	38167	51
82	205	12	22	48188	14812	14
73	317	46	19	95461	32615	40
87	709	56	24	229864	82188	77
95	590	47	26	191094	51763	51
105	526	48	27	150640	59325	43
37	443	35	10	111388	48976	33
96	419	44	26	165098	43384	47
88	205	25	23	63205	26692	31
83	310	47	21	109102	53279	31
124	785	28	34	137303	20652	40
116	434	48	29	125304	38338	42
76	576	32	19	85332	36735	35
65	317	28	19	95808	42764	40
86	288	31	23	83419	44331	30
85	285	13	22	101723	41354	11
107	391	38	29	94982	47879	41
124	446	39	31	129700	103793	53
78	715	68	21	113325	52235	82
83	208	32	21	81518	49825	41
78	101	5	21	31970	4105	6
59	858	53	15	192268	58687	81
33	302	33	9	91086	40745	47
92	360	54	23	80820	33187	100
52	411	36	18	83261	14063	46
121	561	52	31	116290	37407	38
92	292	0	25	56544	7190	0
99	492	52	24	116173	49562	45
86	669	45	22	111488	76324	56
75	253	16	21	60138	21928	18
96	366	33	26	73422	27860	54
81	192	48	22	67751	28078	37
104	616	33	26	213351	49577	40
76	221	24	20	51185	28145	37
90	438	37	25	97181	36241	36
75	247	17	19	45100	10824	34
86	388	32	22	115801	46892	49
100	541	55	25	186310	61264	82
88	233	39	22	71960	22933	36
80	333	31	21	80105	20787	33
73	422	26	20	103613	43978	55
88	452	37	23	98707	51305	50
79	584	66	22	136234	55593	71
81	366	35	21	136781	51648	31
48	406	24	12	105863	30552	42
33	254	18	9	38775	23470	31
120	606	37	32	179997	77530	51
90	491	86	24	169406	57299	64
2	67	13	1	19349	9604	14
96	607	21	24	153069	34684	37
86	597	32	25	109510	41094	37
15	240	8	4	43803	3439	8
48	219	38	15	47062	25171	38
81	349	45	21	110845	23437	23
84	241	24	23	92517	34086	22
46	136	23	12	58660	24649	18
59	194	2	16	27676	2342	1
96	222	52	24	98550	45571	48
29	153	5	9	43646	3255	5
0	0	0	0	0	0	0
83	251	43	23	67312	30002	46
63	240	18	17	57359	19360	33
68	358	44	18	104330	43320	41
84	302	45	21	70369	35513	57
54	267	29	17	65494	23536	49
0	14	0	0	3616	0	0
0	0	0	0	0	0	0
75	287	32	20	143931	54438	45
87	476	65	26	117946	56812	78
104	509	26	26	131175	33838	46
80	243	24	20	84336	32366	25
3	292	7	1	43410	13	1
93	410	62	24	136250	55082	59
55	217	30	14	79015	31334	29
96	422	49	26	92937	16612	26
48	160	3	12	57586	5084	4
8	75	10	2	19764	9927	10
60	412	42	16	105757	47413	43
84	309	18	22	97213	27389	36
112	417	40	28	113402	30425	41
8	79	1	2	11796	0	0
0	25	0	0	7627	0	0
52	431	29	17	121085	33510	32
4	11	0	1	6836	0	0
57	564	46	17	139563	40389	53
0	6	5	0	5118	0	0
14	183	8	4	40248	6012	6
0	0	0	0	0	0	0
91	295	21	25	95079	22205	18
89	230	21	26	80763	17231	26
0	27	0	0	7131	0	0
0	14	0	0	4194	0	0
54	240	15	15	60378	11017	16
77	251	47	20	109173	46741	84
76	347	17	19	83484	39869	22

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	'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155645&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155645&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155645&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	'Gwilym Jenkins' @ jenkins.wessa.net

Goodness of Fit
Correlation	0.9791
R-squared	0.9586
RMSE	6.5812

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9791 \tabularnewline
R-squared & 0.9586 \tabularnewline
RMSE & 6.5812 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155645&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9791[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9586[/C][/ROW]
[ROW][C]RMSE[/C][C]6.5812[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155645&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155645&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	0.9791
R-squared	0.9586
RMSE	6.5812

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	70	60.8461538461538	9.15384615384615
2	68	60.8461538461538	7.15384615384615
3	0	3.17647058823529	-3.17647058823529
4	68	81.64	-13.64
5	120	111.047619047619	8.95238095238095
6	120	111.047619047619	8.95238095238095
7	72	73.0588235294118	-1.05882352941177
8	96	95.2692307692308	0.730769230769226
9	109	111.047619047619	-2.04761904761905
10	104	95.2692307692308	8.73076923076923
11	54	73.0588235294118	-19.0588235294118
12	98	95.2692307692308	2.73076923076923
13	49	45.4117647058824	3.58823529411764
14	88	81.64	6.36
15	57	60.8461538461538	-3.84615384615385
16	74	73.0588235294118	0.941176470588232
17	112	111.047619047619	0.952380952380949
18	45	45.4117647058824	-0.411764705882355
19	110	111.047619047619	-1.04761904761905
20	39	45.4117647058824	-6.41176470588236
21	55	45.4117647058824	9.58823529411764
22	102	111.047619047619	-9.04761904761905
23	96	95.2692307692308	0.730769230769226
24	86	111.047619047619	-25.0476190476191
25	78	81.64	-3.64
26	64	60.8461538461538	3.15384615384615
27	82	81.64	0.359999999999999
28	100	111.047619047619	-11.0476190476191
29	99	95.2692307692308	3.73076923076923
30	67	60.8461538461538	6.15384615384615
31	87	86.5	0.5
32	65	73.0588235294118	-8.05882352941177
33	43	45.4117647058824	-2.41176470588236
34	80	73.0588235294118	6.94117647058823
35	84	81.64	2.36
36	0	3.17647058823529	-3.17647058823529
37	105	111.047619047619	-6.04761904761905
38	51	45.4117647058824	5.58823529411764
39	98	111.047619047619	-13.0476190476191
40	124	111.047619047619	12.9523809523809
41	75	73.0588235294118	1.94117647058823
42	120	111.047619047619	8.95238095238095
43	84	86.5	-2.5
44	82	81.64	0.359999999999999
45	87	81.64	5.36
46	78	81.64	-3.64
47	97	111.047619047619	-14.0476190476191
48	76	73.0588235294118	2.94117647058823
49	104	95.2692307692308	8.73076923076923
50	93	95.2692307692308	-2.26923076923077
51	82	81.64	0.359999999999999
52	73	73.0588235294118	-0.058823529411768
53	87	95.2692307692308	-8.26923076923077
54	95	95.2692307692308	-0.269230769230774
55	105	111.047619047619	-6.04761904761905
56	37	45.4117647058824	-8.41176470588236
57	96	95.2692307692308	0.730769230769226
58	88	86.5	1.5
59	83	81.64	1.36
60	124	111.047619047619	12.9523809523809
61	116	111.047619047619	4.95238095238095
62	76	73.0588235294118	2.94117647058823
63	65	73.0588235294118	-8.05882352941177
64	86	86.5	-0.5
65	85	81.64	3.36
66	107	111.047619047619	-4.04761904761905
67	124	111.047619047619	12.9523809523809
68	78	81.64	-3.64
69	83	81.64	1.36
70	78	81.64	-3.64
71	59	45.4117647058824	13.5882352941176
72	33	45.4117647058824	-12.4117647058824
73	92	86.5	5.5
74	52	60.8461538461538	-8.84615384615385
75	121	111.047619047619	9.95238095238095
76	92	95.2692307692308	-3.26923076923077
77	99	95.2692307692308	3.73076923076923
78	86	81.64	4.36
79	75	81.64	-6.64
80	96	95.2692307692308	0.730769230769226
81	81	81.64	-0.640000000000001
82	104	95.2692307692308	8.73076923076923
83	76	73.0588235294118	2.94117647058823
84	90	95.2692307692308	-5.26923076923077
85	75	73.0588235294118	1.94117647058823
86	86	81.64	4.36
87	100	95.2692307692308	4.73076923076923
88	88	81.64	6.36
89	80	81.64	-1.64
90	73	73.0588235294118	-0.058823529411768
91	88	86.5	1.5
92	79	81.64	-2.64
93	81	81.64	-0.640000000000001
94	48	45.4117647058824	2.58823529411764
95	33	45.4117647058824	-12.4117647058824
96	120	111.047619047619	8.95238095238095
97	90	95.2692307692308	-5.26923076923077
98	2	3.17647058823529	-1.17647058823529
99	96	95.2692307692308	0.730769230769226
100	86	95.2692307692308	-9.26923076923077
101	15	3.17647058823529	11.8235294117647
102	48	45.4117647058824	2.58823529411764
103	81	81.64	-0.640000000000001
104	84	86.5	-2.5
105	46	45.4117647058824	0.588235294117645
106	59	60.8461538461538	-1.84615384615385
107	96	95.2692307692308	0.730769230769226
108	29	45.4117647058824	-16.4117647058824
109	0	3.17647058823529	-3.17647058823529
110	83	86.5	-3.5
111	63	60.8461538461538	2.15384615384615
112	68	60.8461538461538	7.15384615384615
113	84	81.64	2.36
114	54	60.8461538461538	-6.84615384615385
115	0	3.17647058823529	-3.17647058823529
116	0	3.17647058823529	-3.17647058823529
117	75	73.0588235294118	1.94117647058823
118	87	95.2692307692308	-8.26923076923077
119	104	95.2692307692308	8.73076923076923
120	80	73.0588235294118	6.94117647058823
121	3	3.17647058823529	-0.176470588235294
122	93	95.2692307692308	-2.26923076923077
123	55	45.4117647058824	9.58823529411764
124	96	95.2692307692308	0.730769230769226
125	48	45.4117647058824	2.58823529411764
126	8	3.17647058823529	4.82352941176471
127	60	60.8461538461538	-0.846153846153847
128	84	81.64	2.36
129	112	111.047619047619	0.952380952380949
130	8	3.17647058823529	4.82352941176471
131	0	3.17647058823529	-3.17647058823529
132	52	60.8461538461538	-8.84615384615385
133	4	3.17647058823529	0.823529411764706
134	57	60.8461538461538	-3.84615384615385
135	0	3.17647058823529	-3.17647058823529
136	14	3.17647058823529	10.8235294117647
137	0	3.17647058823529	-3.17647058823529
138	91	95.2692307692308	-4.26923076923077
139	89	95.2692307692308	-6.26923076923077
140	0	3.17647058823529	-3.17647058823529
141	0	3.17647058823529	-3.17647058823529
142	54	45.4117647058824	8.58823529411764
143	77	73.0588235294118	3.94117647058823
144	76	73.0588235294118	2.94117647058823

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 70 & 60.8461538461538 & 9.15384615384615 \tabularnewline
2 & 68 & 60.8461538461538 & 7.15384615384615 \tabularnewline
3 & 0 & 3.17647058823529 & -3.17647058823529 \tabularnewline
4 & 68 & 81.64 & -13.64 \tabularnewline
5 & 120 & 111.047619047619 & 8.95238095238095 \tabularnewline
6 & 120 & 111.047619047619 & 8.95238095238095 \tabularnewline
7 & 72 & 73.0588235294118 & -1.05882352941177 \tabularnewline
8 & 96 & 95.2692307692308 & 0.730769230769226 \tabularnewline
9 & 109 & 111.047619047619 & -2.04761904761905 \tabularnewline
10 & 104 & 95.2692307692308 & 8.73076923076923 \tabularnewline
11 & 54 & 73.0588235294118 & -19.0588235294118 \tabularnewline
12 & 98 & 95.2692307692308 & 2.73076923076923 \tabularnewline
13 & 49 & 45.4117647058824 & 3.58823529411764 \tabularnewline
14 & 88 & 81.64 & 6.36 \tabularnewline
15 & 57 & 60.8461538461538 & -3.84615384615385 \tabularnewline
16 & 74 & 73.0588235294118 & 0.941176470588232 \tabularnewline
17 & 112 & 111.047619047619 & 0.952380952380949 \tabularnewline
18 & 45 & 45.4117647058824 & -0.411764705882355 \tabularnewline
19 & 110 & 111.047619047619 & -1.04761904761905 \tabularnewline
20 & 39 & 45.4117647058824 & -6.41176470588236 \tabularnewline
21 & 55 & 45.4117647058824 & 9.58823529411764 \tabularnewline
22 & 102 & 111.047619047619 & -9.04761904761905 \tabularnewline
23 & 96 & 95.2692307692308 & 0.730769230769226 \tabularnewline
24 & 86 & 111.047619047619 & -25.0476190476191 \tabularnewline
25 & 78 & 81.64 & -3.64 \tabularnewline
26 & 64 & 60.8461538461538 & 3.15384615384615 \tabularnewline
27 & 82 & 81.64 & 0.359999999999999 \tabularnewline
28 & 100 & 111.047619047619 & -11.0476190476191 \tabularnewline
29 & 99 & 95.2692307692308 & 3.73076923076923 \tabularnewline
30 & 67 & 60.8461538461538 & 6.15384615384615 \tabularnewline
31 & 87 & 86.5 & 0.5 \tabularnewline
32 & 65 & 73.0588235294118 & -8.05882352941177 \tabularnewline
33 & 43 & 45.4117647058824 & -2.41176470588236 \tabularnewline
34 & 80 & 73.0588235294118 & 6.94117647058823 \tabularnewline
35 & 84 & 81.64 & 2.36 \tabularnewline
36 & 0 & 3.17647058823529 & -3.17647058823529 \tabularnewline
37 & 105 & 111.047619047619 & -6.04761904761905 \tabularnewline
38 & 51 & 45.4117647058824 & 5.58823529411764 \tabularnewline
39 & 98 & 111.047619047619 & -13.0476190476191 \tabularnewline
40 & 124 & 111.047619047619 & 12.9523809523809 \tabularnewline
41 & 75 & 73.0588235294118 & 1.94117647058823 \tabularnewline
42 & 120 & 111.047619047619 & 8.95238095238095 \tabularnewline
43 & 84 & 86.5 & -2.5 \tabularnewline
44 & 82 & 81.64 & 0.359999999999999 \tabularnewline
45 & 87 & 81.64 & 5.36 \tabularnewline
46 & 78 & 81.64 & -3.64 \tabularnewline
47 & 97 & 111.047619047619 & -14.0476190476191 \tabularnewline
48 & 76 & 73.0588235294118 & 2.94117647058823 \tabularnewline
49 & 104 & 95.2692307692308 & 8.73076923076923 \tabularnewline
50 & 93 & 95.2692307692308 & -2.26923076923077 \tabularnewline
51 & 82 & 81.64 & 0.359999999999999 \tabularnewline
52 & 73 & 73.0588235294118 & -0.058823529411768 \tabularnewline
53 & 87 & 95.2692307692308 & -8.26923076923077 \tabularnewline
54 & 95 & 95.2692307692308 & -0.269230769230774 \tabularnewline
55 & 105 & 111.047619047619 & -6.04761904761905 \tabularnewline
56 & 37 & 45.4117647058824 & -8.41176470588236 \tabularnewline
57 & 96 & 95.2692307692308 & 0.730769230769226 \tabularnewline
58 & 88 & 86.5 & 1.5 \tabularnewline
59 & 83 & 81.64 & 1.36 \tabularnewline
60 & 124 & 111.047619047619 & 12.9523809523809 \tabularnewline
61 & 116 & 111.047619047619 & 4.95238095238095 \tabularnewline
62 & 76 & 73.0588235294118 & 2.94117647058823 \tabularnewline
63 & 65 & 73.0588235294118 & -8.05882352941177 \tabularnewline
64 & 86 & 86.5 & -0.5 \tabularnewline
65 & 85 & 81.64 & 3.36 \tabularnewline
66 & 107 & 111.047619047619 & -4.04761904761905 \tabularnewline
67 & 124 & 111.047619047619 & 12.9523809523809 \tabularnewline
68 & 78 & 81.64 & -3.64 \tabularnewline
69 & 83 & 81.64 & 1.36 \tabularnewline
70 & 78 & 81.64 & -3.64 \tabularnewline
71 & 59 & 45.4117647058824 & 13.5882352941176 \tabularnewline
72 & 33 & 45.4117647058824 & -12.4117647058824 \tabularnewline
73 & 92 & 86.5 & 5.5 \tabularnewline
74 & 52 & 60.8461538461538 & -8.84615384615385 \tabularnewline
75 & 121 & 111.047619047619 & 9.95238095238095 \tabularnewline
76 & 92 & 95.2692307692308 & -3.26923076923077 \tabularnewline
77 & 99 & 95.2692307692308 & 3.73076923076923 \tabularnewline
78 & 86 & 81.64 & 4.36 \tabularnewline
79 & 75 & 81.64 & -6.64 \tabularnewline
80 & 96 & 95.2692307692308 & 0.730769230769226 \tabularnewline
81 & 81 & 81.64 & -0.640000000000001 \tabularnewline
82 & 104 & 95.2692307692308 & 8.73076923076923 \tabularnewline
83 & 76 & 73.0588235294118 & 2.94117647058823 \tabularnewline
84 & 90 & 95.2692307692308 & -5.26923076923077 \tabularnewline
85 & 75 & 73.0588235294118 & 1.94117647058823 \tabularnewline
86 & 86 & 81.64 & 4.36 \tabularnewline
87 & 100 & 95.2692307692308 & 4.73076923076923 \tabularnewline
88 & 88 & 81.64 & 6.36 \tabularnewline
89 & 80 & 81.64 & -1.64 \tabularnewline
90 & 73 & 73.0588235294118 & -0.058823529411768 \tabularnewline
91 & 88 & 86.5 & 1.5 \tabularnewline
92 & 79 & 81.64 & -2.64 \tabularnewline
93 & 81 & 81.64 & -0.640000000000001 \tabularnewline
94 & 48 & 45.4117647058824 & 2.58823529411764 \tabularnewline
95 & 33 & 45.4117647058824 & -12.4117647058824 \tabularnewline
96 & 120 & 111.047619047619 & 8.95238095238095 \tabularnewline
97 & 90 & 95.2692307692308 & -5.26923076923077 \tabularnewline
98 & 2 & 3.17647058823529 & -1.17647058823529 \tabularnewline
99 & 96 & 95.2692307692308 & 0.730769230769226 \tabularnewline
100 & 86 & 95.2692307692308 & -9.26923076923077 \tabularnewline
101 & 15 & 3.17647058823529 & 11.8235294117647 \tabularnewline
102 & 48 & 45.4117647058824 & 2.58823529411764 \tabularnewline
103 & 81 & 81.64 & -0.640000000000001 \tabularnewline
104 & 84 & 86.5 & -2.5 \tabularnewline
105 & 46 & 45.4117647058824 & 0.588235294117645 \tabularnewline
106 & 59 & 60.8461538461538 & -1.84615384615385 \tabularnewline
107 & 96 & 95.2692307692308 & 0.730769230769226 \tabularnewline
108 & 29 & 45.4117647058824 & -16.4117647058824 \tabularnewline
109 & 0 & 3.17647058823529 & -3.17647058823529 \tabularnewline
110 & 83 & 86.5 & -3.5 \tabularnewline
111 & 63 & 60.8461538461538 & 2.15384615384615 \tabularnewline
112 & 68 & 60.8461538461538 & 7.15384615384615 \tabularnewline
113 & 84 & 81.64 & 2.36 \tabularnewline
114 & 54 & 60.8461538461538 & -6.84615384615385 \tabularnewline
115 & 0 & 3.17647058823529 & -3.17647058823529 \tabularnewline
116 & 0 & 3.17647058823529 & -3.17647058823529 \tabularnewline
117 & 75 & 73.0588235294118 & 1.94117647058823 \tabularnewline
118 & 87 & 95.2692307692308 & -8.26923076923077 \tabularnewline
119 & 104 & 95.2692307692308 & 8.73076923076923 \tabularnewline
120 & 80 & 73.0588235294118 & 6.94117647058823 \tabularnewline
121 & 3 & 3.17647058823529 & -0.176470588235294 \tabularnewline
122 & 93 & 95.2692307692308 & -2.26923076923077 \tabularnewline
123 & 55 & 45.4117647058824 & 9.58823529411764 \tabularnewline
124 & 96 & 95.2692307692308 & 0.730769230769226 \tabularnewline
125 & 48 & 45.4117647058824 & 2.58823529411764 \tabularnewline
126 & 8 & 3.17647058823529 & 4.82352941176471 \tabularnewline
127 & 60 & 60.8461538461538 & -0.846153846153847 \tabularnewline
128 & 84 & 81.64 & 2.36 \tabularnewline
129 & 112 & 111.047619047619 & 0.952380952380949 \tabularnewline
130 & 8 & 3.17647058823529 & 4.82352941176471 \tabularnewline
131 & 0 & 3.17647058823529 & -3.17647058823529 \tabularnewline
132 & 52 & 60.8461538461538 & -8.84615384615385 \tabularnewline
133 & 4 & 3.17647058823529 & 0.823529411764706 \tabularnewline
134 & 57 & 60.8461538461538 & -3.84615384615385 \tabularnewline
135 & 0 & 3.17647058823529 & -3.17647058823529 \tabularnewline
136 & 14 & 3.17647058823529 & 10.8235294117647 \tabularnewline
137 & 0 & 3.17647058823529 & -3.17647058823529 \tabularnewline
138 & 91 & 95.2692307692308 & -4.26923076923077 \tabularnewline
139 & 89 & 95.2692307692308 & -6.26923076923077 \tabularnewline
140 & 0 & 3.17647058823529 & -3.17647058823529 \tabularnewline
141 & 0 & 3.17647058823529 & -3.17647058823529 \tabularnewline
142 & 54 & 45.4117647058824 & 8.58823529411764 \tabularnewline
143 & 77 & 73.0588235294118 & 3.94117647058823 \tabularnewline
144 & 76 & 73.0588235294118 & 2.94117647058823 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155645&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]70[/C][C]60.8461538461538[/C][C]9.15384615384615[/C][/ROW]
[ROW][C]2[/C][C]68[/C][C]60.8461538461538[/C][C]7.15384615384615[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]3.17647058823529[/C][C]-3.17647058823529[/C][/ROW]
[ROW][C]4[/C][C]68[/C][C]81.64[/C][C]-13.64[/C][/ROW]
[ROW][C]5[/C][C]120[/C][C]111.047619047619[/C][C]8.95238095238095[/C][/ROW]
[ROW][C]6[/C][C]120[/C][C]111.047619047619[/C][C]8.95238095238095[/C][/ROW]
[ROW][C]7[/C][C]72[/C][C]73.0588235294118[/C][C]-1.05882352941177[/C][/ROW]
[ROW][C]8[/C][C]96[/C][C]95.2692307692308[/C][C]0.730769230769226[/C][/ROW]
[ROW][C]9[/C][C]109[/C][C]111.047619047619[/C][C]-2.04761904761905[/C][/ROW]
[ROW][C]10[/C][C]104[/C][C]95.2692307692308[/C][C]8.73076923076923[/C][/ROW]
[ROW][C]11[/C][C]54[/C][C]73.0588235294118[/C][C]-19.0588235294118[/C][/ROW]
[ROW][C]12[/C][C]98[/C][C]95.2692307692308[/C][C]2.73076923076923[/C][/ROW]
[ROW][C]13[/C][C]49[/C][C]45.4117647058824[/C][C]3.58823529411764[/C][/ROW]
[ROW][C]14[/C][C]88[/C][C]81.64[/C][C]6.36[/C][/ROW]
[ROW][C]15[/C][C]57[/C][C]60.8461538461538[/C][C]-3.84615384615385[/C][/ROW]
[ROW][C]16[/C][C]74[/C][C]73.0588235294118[/C][C]0.941176470588232[/C][/ROW]
[ROW][C]17[/C][C]112[/C][C]111.047619047619[/C][C]0.952380952380949[/C][/ROW]
[ROW][C]18[/C][C]45[/C][C]45.4117647058824[/C][C]-0.411764705882355[/C][/ROW]
[ROW][C]19[/C][C]110[/C][C]111.047619047619[/C][C]-1.04761904761905[/C][/ROW]
[ROW][C]20[/C][C]39[/C][C]45.4117647058824[/C][C]-6.41176470588236[/C][/ROW]
[ROW][C]21[/C][C]55[/C][C]45.4117647058824[/C][C]9.58823529411764[/C][/ROW]
[ROW][C]22[/C][C]102[/C][C]111.047619047619[/C][C]-9.04761904761905[/C][/ROW]
[ROW][C]23[/C][C]96[/C][C]95.2692307692308[/C][C]0.730769230769226[/C][/ROW]
[ROW][C]24[/C][C]86[/C][C]111.047619047619[/C][C]-25.0476190476191[/C][/ROW]
[ROW][C]25[/C][C]78[/C][C]81.64[/C][C]-3.64[/C][/ROW]
[ROW][C]26[/C][C]64[/C][C]60.8461538461538[/C][C]3.15384615384615[/C][/ROW]
[ROW][C]27[/C][C]82[/C][C]81.64[/C][C]0.359999999999999[/C][/ROW]
[ROW][C]28[/C][C]100[/C][C]111.047619047619[/C][C]-11.0476190476191[/C][/ROW]
[ROW][C]29[/C][C]99[/C][C]95.2692307692308[/C][C]3.73076923076923[/C][/ROW]
[ROW][C]30[/C][C]67[/C][C]60.8461538461538[/C][C]6.15384615384615[/C][/ROW]
[ROW][C]31[/C][C]87[/C][C]86.5[/C][C]0.5[/C][/ROW]
[ROW][C]32[/C][C]65[/C][C]73.0588235294118[/C][C]-8.05882352941177[/C][/ROW]
[ROW][C]33[/C][C]43[/C][C]45.4117647058824[/C][C]-2.41176470588236[/C][/ROW]
[ROW][C]34[/C][C]80[/C][C]73.0588235294118[/C][C]6.94117647058823[/C][/ROW]
[ROW][C]35[/C][C]84[/C][C]81.64[/C][C]2.36[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]3.17647058823529[/C][C]-3.17647058823529[/C][/ROW]
[ROW][C]37[/C][C]105[/C][C]111.047619047619[/C][C]-6.04761904761905[/C][/ROW]
[ROW][C]38[/C][C]51[/C][C]45.4117647058824[/C][C]5.58823529411764[/C][/ROW]
[ROW][C]39[/C][C]98[/C][C]111.047619047619[/C][C]-13.0476190476191[/C][/ROW]
[ROW][C]40[/C][C]124[/C][C]111.047619047619[/C][C]12.9523809523809[/C][/ROW]
[ROW][C]41[/C][C]75[/C][C]73.0588235294118[/C][C]1.94117647058823[/C][/ROW]
[ROW][C]42[/C][C]120[/C][C]111.047619047619[/C][C]8.95238095238095[/C][/ROW]
[ROW][C]43[/C][C]84[/C][C]86.5[/C][C]-2.5[/C][/ROW]
[ROW][C]44[/C][C]82[/C][C]81.64[/C][C]0.359999999999999[/C][/ROW]
[ROW][C]45[/C][C]87[/C][C]81.64[/C][C]5.36[/C][/ROW]
[ROW][C]46[/C][C]78[/C][C]81.64[/C][C]-3.64[/C][/ROW]
[ROW][C]47[/C][C]97[/C][C]111.047619047619[/C][C]-14.0476190476191[/C][/ROW]
[ROW][C]48[/C][C]76[/C][C]73.0588235294118[/C][C]2.94117647058823[/C][/ROW]
[ROW][C]49[/C][C]104[/C][C]95.2692307692308[/C][C]8.73076923076923[/C][/ROW]
[ROW][C]50[/C][C]93[/C][C]95.2692307692308[/C][C]-2.26923076923077[/C][/ROW]
[ROW][C]51[/C][C]82[/C][C]81.64[/C][C]0.359999999999999[/C][/ROW]
[ROW][C]52[/C][C]73[/C][C]73.0588235294118[/C][C]-0.058823529411768[/C][/ROW]
[ROW][C]53[/C][C]87[/C][C]95.2692307692308[/C][C]-8.26923076923077[/C][/ROW]
[ROW][C]54[/C][C]95[/C][C]95.2692307692308[/C][C]-0.269230769230774[/C][/ROW]
[ROW][C]55[/C][C]105[/C][C]111.047619047619[/C][C]-6.04761904761905[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]45.4117647058824[/C][C]-8.41176470588236[/C][/ROW]
[ROW][C]57[/C][C]96[/C][C]95.2692307692308[/C][C]0.730769230769226[/C][/ROW]
[ROW][C]58[/C][C]88[/C][C]86.5[/C][C]1.5[/C][/ROW]
[ROW][C]59[/C][C]83[/C][C]81.64[/C][C]1.36[/C][/ROW]
[ROW][C]60[/C][C]124[/C][C]111.047619047619[/C][C]12.9523809523809[/C][/ROW]
[ROW][C]61[/C][C]116[/C][C]111.047619047619[/C][C]4.95238095238095[/C][/ROW]
[ROW][C]62[/C][C]76[/C][C]73.0588235294118[/C][C]2.94117647058823[/C][/ROW]
[ROW][C]63[/C][C]65[/C][C]73.0588235294118[/C][C]-8.05882352941177[/C][/ROW]
[ROW][C]64[/C][C]86[/C][C]86.5[/C][C]-0.5[/C][/ROW]
[ROW][C]65[/C][C]85[/C][C]81.64[/C][C]3.36[/C][/ROW]
[ROW][C]66[/C][C]107[/C][C]111.047619047619[/C][C]-4.04761904761905[/C][/ROW]
[ROW][C]67[/C][C]124[/C][C]111.047619047619[/C][C]12.9523809523809[/C][/ROW]
[ROW][C]68[/C][C]78[/C][C]81.64[/C][C]-3.64[/C][/ROW]
[ROW][C]69[/C][C]83[/C][C]81.64[/C][C]1.36[/C][/ROW]
[ROW][C]70[/C][C]78[/C][C]81.64[/C][C]-3.64[/C][/ROW]
[ROW][C]71[/C][C]59[/C][C]45.4117647058824[/C][C]13.5882352941176[/C][/ROW]
[ROW][C]72[/C][C]33[/C][C]45.4117647058824[/C][C]-12.4117647058824[/C][/ROW]
[ROW][C]73[/C][C]92[/C][C]86.5[/C][C]5.5[/C][/ROW]
[ROW][C]74[/C][C]52[/C][C]60.8461538461538[/C][C]-8.84615384615385[/C][/ROW]
[ROW][C]75[/C][C]121[/C][C]111.047619047619[/C][C]9.95238095238095[/C][/ROW]
[ROW][C]76[/C][C]92[/C][C]95.2692307692308[/C][C]-3.26923076923077[/C][/ROW]
[ROW][C]77[/C][C]99[/C][C]95.2692307692308[/C][C]3.73076923076923[/C][/ROW]
[ROW][C]78[/C][C]86[/C][C]81.64[/C][C]4.36[/C][/ROW]
[ROW][C]79[/C][C]75[/C][C]81.64[/C][C]-6.64[/C][/ROW]
[ROW][C]80[/C][C]96[/C][C]95.2692307692308[/C][C]0.730769230769226[/C][/ROW]
[ROW][C]81[/C][C]81[/C][C]81.64[/C][C]-0.640000000000001[/C][/ROW]
[ROW][C]82[/C][C]104[/C][C]95.2692307692308[/C][C]8.73076923076923[/C][/ROW]
[ROW][C]83[/C][C]76[/C][C]73.0588235294118[/C][C]2.94117647058823[/C][/ROW]
[ROW][C]84[/C][C]90[/C][C]95.2692307692308[/C][C]-5.26923076923077[/C][/ROW]
[ROW][C]85[/C][C]75[/C][C]73.0588235294118[/C][C]1.94117647058823[/C][/ROW]
[ROW][C]86[/C][C]86[/C][C]81.64[/C][C]4.36[/C][/ROW]
[ROW][C]87[/C][C]100[/C][C]95.2692307692308[/C][C]4.73076923076923[/C][/ROW]
[ROW][C]88[/C][C]88[/C][C]81.64[/C][C]6.36[/C][/ROW]
[ROW][C]89[/C][C]80[/C][C]81.64[/C][C]-1.64[/C][/ROW]
[ROW][C]90[/C][C]73[/C][C]73.0588235294118[/C][C]-0.058823529411768[/C][/ROW]
[ROW][C]91[/C][C]88[/C][C]86.5[/C][C]1.5[/C][/ROW]
[ROW][C]92[/C][C]79[/C][C]81.64[/C][C]-2.64[/C][/ROW]
[ROW][C]93[/C][C]81[/C][C]81.64[/C][C]-0.640000000000001[/C][/ROW]
[ROW][C]94[/C][C]48[/C][C]45.4117647058824[/C][C]2.58823529411764[/C][/ROW]
[ROW][C]95[/C][C]33[/C][C]45.4117647058824[/C][C]-12.4117647058824[/C][/ROW]
[ROW][C]96[/C][C]120[/C][C]111.047619047619[/C][C]8.95238095238095[/C][/ROW]
[ROW][C]97[/C][C]90[/C][C]95.2692307692308[/C][C]-5.26923076923077[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]3.17647058823529[/C][C]-1.17647058823529[/C][/ROW]
[ROW][C]99[/C][C]96[/C][C]95.2692307692308[/C][C]0.730769230769226[/C][/ROW]
[ROW][C]100[/C][C]86[/C][C]95.2692307692308[/C][C]-9.26923076923077[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]3.17647058823529[/C][C]11.8235294117647[/C][/ROW]
[ROW][C]102[/C][C]48[/C][C]45.4117647058824[/C][C]2.58823529411764[/C][/ROW]
[ROW][C]103[/C][C]81[/C][C]81.64[/C][C]-0.640000000000001[/C][/ROW]
[ROW][C]104[/C][C]84[/C][C]86.5[/C][C]-2.5[/C][/ROW]
[ROW][C]105[/C][C]46[/C][C]45.4117647058824[/C][C]0.588235294117645[/C][/ROW]
[ROW][C]106[/C][C]59[/C][C]60.8461538461538[/C][C]-1.84615384615385[/C][/ROW]
[ROW][C]107[/C][C]96[/C][C]95.2692307692308[/C][C]0.730769230769226[/C][/ROW]
[ROW][C]108[/C][C]29[/C][C]45.4117647058824[/C][C]-16.4117647058824[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]3.17647058823529[/C][C]-3.17647058823529[/C][/ROW]
[ROW][C]110[/C][C]83[/C][C]86.5[/C][C]-3.5[/C][/ROW]
[ROW][C]111[/C][C]63[/C][C]60.8461538461538[/C][C]2.15384615384615[/C][/ROW]
[ROW][C]112[/C][C]68[/C][C]60.8461538461538[/C][C]7.15384615384615[/C][/ROW]
[ROW][C]113[/C][C]84[/C][C]81.64[/C][C]2.36[/C][/ROW]
[ROW][C]114[/C][C]54[/C][C]60.8461538461538[/C][C]-6.84615384615385[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]3.17647058823529[/C][C]-3.17647058823529[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]3.17647058823529[/C][C]-3.17647058823529[/C][/ROW]
[ROW][C]117[/C][C]75[/C][C]73.0588235294118[/C][C]1.94117647058823[/C][/ROW]
[ROW][C]118[/C][C]87[/C][C]95.2692307692308[/C][C]-8.26923076923077[/C][/ROW]
[ROW][C]119[/C][C]104[/C][C]95.2692307692308[/C][C]8.73076923076923[/C][/ROW]
[ROW][C]120[/C][C]80[/C][C]73.0588235294118[/C][C]6.94117647058823[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]3.17647058823529[/C][C]-0.176470588235294[/C][/ROW]
[ROW][C]122[/C][C]93[/C][C]95.2692307692308[/C][C]-2.26923076923077[/C][/ROW]
[ROW][C]123[/C][C]55[/C][C]45.4117647058824[/C][C]9.58823529411764[/C][/ROW]
[ROW][C]124[/C][C]96[/C][C]95.2692307692308[/C][C]0.730769230769226[/C][/ROW]
[ROW][C]125[/C][C]48[/C][C]45.4117647058824[/C][C]2.58823529411764[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]3.17647058823529[/C][C]4.82352941176471[/C][/ROW]
[ROW][C]127[/C][C]60[/C][C]60.8461538461538[/C][C]-0.846153846153847[/C][/ROW]
[ROW][C]128[/C][C]84[/C][C]81.64[/C][C]2.36[/C][/ROW]
[ROW][C]129[/C][C]112[/C][C]111.047619047619[/C][C]0.952380952380949[/C][/ROW]
[ROW][C]130[/C][C]8[/C][C]3.17647058823529[/C][C]4.82352941176471[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]3.17647058823529[/C][C]-3.17647058823529[/C][/ROW]
[ROW][C]132[/C][C]52[/C][C]60.8461538461538[/C][C]-8.84615384615385[/C][/ROW]
[ROW][C]133[/C][C]4[/C][C]3.17647058823529[/C][C]0.823529411764706[/C][/ROW]
[ROW][C]134[/C][C]57[/C][C]60.8461538461538[/C][C]-3.84615384615385[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]3.17647058823529[/C][C]-3.17647058823529[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]3.17647058823529[/C][C]10.8235294117647[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]3.17647058823529[/C][C]-3.17647058823529[/C][/ROW]
[ROW][C]138[/C][C]91[/C][C]95.2692307692308[/C][C]-4.26923076923077[/C][/ROW]
[ROW][C]139[/C][C]89[/C][C]95.2692307692308[/C][C]-6.26923076923077[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]3.17647058823529[/C][C]-3.17647058823529[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]3.17647058823529[/C][C]-3.17647058823529[/C][/ROW]
[ROW][C]142[/C][C]54[/C][C]45.4117647058824[/C][C]8.58823529411764[/C][/ROW]
[ROW][C]143[/C][C]77[/C][C]73.0588235294118[/C][C]3.94117647058823[/C][/ROW]
[ROW][C]144[/C][C]76[/C][C]73.0588235294118[/C][C]2.94117647058823[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155645&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155645&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	70	60.8461538461538	9.15384615384615
2	68	60.8461538461538	7.15384615384615
3	0	3.17647058823529	-3.17647058823529
4	68	81.64	-13.64
5	120	111.047619047619	8.95238095238095
6	120	111.047619047619	8.95238095238095
7	72	73.0588235294118	-1.05882352941177
8	96	95.2692307692308	0.730769230769226
9	109	111.047619047619	-2.04761904761905
10	104	95.2692307692308	8.73076923076923
11	54	73.0588235294118	-19.0588235294118
12	98	95.2692307692308	2.73076923076923
13	49	45.4117647058824	3.58823529411764
14	88	81.64	6.36
15	57	60.8461538461538	-3.84615384615385
16	74	73.0588235294118	0.941176470588232
17	112	111.047619047619	0.952380952380949
18	45	45.4117647058824	-0.411764705882355
19	110	111.047619047619	-1.04761904761905
20	39	45.4117647058824	-6.41176470588236
21	55	45.4117647058824	9.58823529411764
22	102	111.047619047619	-9.04761904761905
23	96	95.2692307692308	0.730769230769226
24	86	111.047619047619	-25.0476190476191
25	78	81.64	-3.64
26	64	60.8461538461538	3.15384615384615
27	82	81.64	0.359999999999999
28	100	111.047619047619	-11.0476190476191
29	99	95.2692307692308	3.73076923076923
30	67	60.8461538461538	6.15384615384615
31	87	86.5	0.5
32	65	73.0588235294118	-8.05882352941177
33	43	45.4117647058824	-2.41176470588236
34	80	73.0588235294118	6.94117647058823
35	84	81.64	2.36
36	0	3.17647058823529	-3.17647058823529
37	105	111.047619047619	-6.04761904761905
38	51	45.4117647058824	5.58823529411764
39	98	111.047619047619	-13.0476190476191
40	124	111.047619047619	12.9523809523809
41	75	73.0588235294118	1.94117647058823
42	120	111.047619047619	8.95238095238095
43	84	86.5	-2.5
44	82	81.64	0.359999999999999
45	87	81.64	5.36
46	78	81.64	-3.64
47	97	111.047619047619	-14.0476190476191
48	76	73.0588235294118	2.94117647058823
49	104	95.2692307692308	8.73076923076923
50	93	95.2692307692308	-2.26923076923077
51	82	81.64	0.359999999999999
52	73	73.0588235294118	-0.058823529411768
53	87	95.2692307692308	-8.26923076923077
54	95	95.2692307692308	-0.269230769230774
55	105	111.047619047619	-6.04761904761905
56	37	45.4117647058824	-8.41176470588236
57	96	95.2692307692308	0.730769230769226
58	88	86.5	1.5
59	83	81.64	1.36
60	124	111.047619047619	12.9523809523809
61	116	111.047619047619	4.95238095238095
62	76	73.0588235294118	2.94117647058823
63	65	73.0588235294118	-8.05882352941177
64	86	86.5	-0.5
65	85	81.64	3.36
66	107	111.047619047619	-4.04761904761905
67	124	111.047619047619	12.9523809523809
68	78	81.64	-3.64
69	83	81.64	1.36
70	78	81.64	-3.64
71	59	45.4117647058824	13.5882352941176
72	33	45.4117647058824	-12.4117647058824
73	92	86.5	5.5
74	52	60.8461538461538	-8.84615384615385
75	121	111.047619047619	9.95238095238095
76	92	95.2692307692308	-3.26923076923077
77	99	95.2692307692308	3.73076923076923
78	86	81.64	4.36
79	75	81.64	-6.64
80	96	95.2692307692308	0.730769230769226
81	81	81.64	-0.640000000000001
82	104	95.2692307692308	8.73076923076923
83	76	73.0588235294118	2.94117647058823
84	90	95.2692307692308	-5.26923076923077
85	75	73.0588235294118	1.94117647058823
86	86	81.64	4.36
87	100	95.2692307692308	4.73076923076923
88	88	81.64	6.36
89	80	81.64	-1.64
90	73	73.0588235294118	-0.058823529411768
91	88	86.5	1.5
92	79	81.64	-2.64
93	81	81.64	-0.640000000000001
94	48	45.4117647058824	2.58823529411764
95	33	45.4117647058824	-12.4117647058824
96	120	111.047619047619	8.95238095238095
97	90	95.2692307692308	-5.26923076923077
98	2	3.17647058823529	-1.17647058823529
99	96	95.2692307692308	0.730769230769226
100	86	95.2692307692308	-9.26923076923077
101	15	3.17647058823529	11.8235294117647
102	48	45.4117647058824	2.58823529411764
103	81	81.64	-0.640000000000001
104	84	86.5	-2.5
105	46	45.4117647058824	0.588235294117645
106	59	60.8461538461538	-1.84615384615385
107	96	95.2692307692308	0.730769230769226
108	29	45.4117647058824	-16.4117647058824
109	0	3.17647058823529	-3.17647058823529
110	83	86.5	-3.5
111	63	60.8461538461538	2.15384615384615
112	68	60.8461538461538	7.15384615384615
113	84	81.64	2.36
114	54	60.8461538461538	-6.84615384615385
115	0	3.17647058823529	-3.17647058823529
116	0	3.17647058823529	-3.17647058823529
117	75	73.0588235294118	1.94117647058823
118	87	95.2692307692308	-8.26923076923077
119	104	95.2692307692308	8.73076923076923
120	80	73.0588235294118	6.94117647058823
121	3	3.17647058823529	-0.176470588235294
122	93	95.2692307692308	-2.26923076923077
123	55	45.4117647058824	9.58823529411764
124	96	95.2692307692308	0.730769230769226
125	48	45.4117647058824	2.58823529411764
126	8	3.17647058823529	4.82352941176471
127	60	60.8461538461538	-0.846153846153847
128	84	81.64	2.36
129	112	111.047619047619	0.952380952380949
130	8	3.17647058823529	4.82352941176471
131	0	3.17647058823529	-3.17647058823529
132	52	60.8461538461538	-8.84615384615385
133	4	3.17647058823529	0.823529411764706
134	57	60.8461538461538	-3.84615384615385
135	0	3.17647058823529	-3.17647058823529
136	14	3.17647058823529	10.8235294117647
137	0	3.17647058823529	-3.17647058823529
138	91	95.2692307692308	-4.26923076923077
139	89	95.2692307692308	-6.26923076923077
140	0	3.17647058823529	-3.17647058823529
141	0	3.17647058823529	-3.17647058823529
142	54	45.4117647058824	8.58823529411764
143	77	73.0588235294118	3.94117647058823
144	76	73.0588235294118	2.94117647058823

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):

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = none ; 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