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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Thu, 15 Dec 2011 14:58:07 -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/t1323979121th6ps1rok07potg.htm/, Retrieved Wed, 08 May 2024 06:16:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155697, Retrieved Wed, 08 May 2024 06:16:51 +0000

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User-defined keywords

Estimated Impact

101

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)] [Recursive Partiti...] [2011-12-15 19:58:07] [ccdbcd1f4b80805a70032cb1a2c4c931] [Current]
-           [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-15 20:11:10] [298b545ca29b1a60cbb481c5dea313ae] 
-   PD        [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-22 20:50:11] [298b545ca29b1a60cbb481c5dea313ae] 
-   PD          [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-23 15:58:21] [298b545ca29b1a60cbb481c5dea313ae] 
-   P             [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-23 16:48:21] [298b545ca29b1a60cbb481c5dea313ae] 

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Dataseries X:

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Histogram

Boxplots

129404	20	18158	5636	22622	30	28
130358	38	30461	9079	73570	42	39
7215	0	1423	603	1929	0	0
112861	49	25629	8874	36294	54	54
219904	76	48758	17988	62378	86	80
396382	104	129230	21325	167760	157	144
117604	37	27376	8325	52443	36	36
126737	53	26706	7117	57283	48	48
99729	42	26505	7996	36614	45	42
256310	62	49801	14218	93268	77	71
113066	50	46580	6321	35439	49	49
157228	65	48352	19690	72405	77	74
69952	28	13899	5659	24044	28	27
152673	48	39342	11370	55909	84	83
130642	42	27465	4778	44689	31	31
125769	47	55211	5954	49319	28	28
123467	71	74098	22924	62075	99	98
56232	0	13497	70	2341	2	2
108330	50	38338	14369	40551	41	43
22762	12	52505	3706	11621	25	24
48554	16	10663	3147	18741	16	16
182081	77	74484	16801	84202	96	95
140857	29	28895	2162	15334	23	22
93773	38	32827	4721	28024	33	33
133398	50	36188	5290	53306	46	45
113933	33	28173	6446	37918	59	59
153851	49	54926	14711	54819	72	66
140711	59	38900	13311	89058	72	70
303804	55	88530	13577	103354	62	56
161651	40	35482	14634	70239	55	55
123344	40	26730	6931	33045	27	27
157640	51	29806	9992	63852	41	37
91279	41	41799	6185	30905	51	48
189374	73	54289	3445	24242	26	26
178768	51	36805	12327	78907	65	64
0	0	0	0	0	0	0
175403	46	33146	9898	36005	28	21
92342	44	23333	8022	31972	44	44
100023	31	47686	10765	35853	36	36
178277	71	77783	22717	115301	100	89
145062	61	36042	10090	47689	104	101
110980	28	34541	12385	34223	35	31
86039	21	75620	8513	43431	69	65
125481	42	60610	5508	52220	73	71
95535	44	55041	9628	33863	106	102
126456	40	32087	11872	46879	53	53
61554	15	16356	4186	23228	43	41
164752	46	40161	10877	42827	49	46
159121	43	55459	17066	65765	38	37
129362	47	36679	9175	38167	51	51
48188	12	22346	2102	14812	14	14
95461	46	27377	10807	32615	40	40
229864	56	50273	13662	82188	79	77
191094	47	32104	9224	51763	52	51
150640	48	27016	9001	59325	44	43
111388	35	19715	7204	48976	34	33
165098	44	33629	6572	43384	47	47
63205	25	27084	7509	26692	32	31
109102	47	32352	12920	53279	31	31
137303	28	51845	5438	20652	40	40
125304	48	26591	11489	38338	42	42
85332	32	29677	6661	36735	34	35
95808	28	54237	7941	42764	40	40
83419	31	20284	6173	44331	35	30
101723	13	22741	5562	41354	11	11
94982	38	34178	9492	47879	43	41
129700	39	69551	17456	103793	53	53
113325	68	29653	9422	52235	82	82
81518	32	38071	10913	49825	41	41
31970	5	4157	1283	4105	6	6
192268	53	28321	6198	58687	82	81
91086	33	40195	4501	40745	47	47
80820	54	48158	9560	33187	108	100
83261	36	13310	3394	14063	46	46
116290	52	78474	9871	37407	38	38
56544	0	6386	2419	7190	0	0
116173	52	31588	10630	49562	45	45
111488	45	61254	8536	76324	57	56
60138	16	21152	4911	21928	20	18
73422	33	41272	9775	27860	56	54
67751	48	34165	11227	28078	38	37
213351	33	37054	6916	49577	42	40
51185	24	12368	3424	28145	37	37
97181	37	23168	8637	36241	36	36
45100	17	16380	3189	10824	34	34
115801	32	41242	8178	46892	53	49
186310	55	48450	16739	61264	85	82
71960	39	20790	6094	22933	36	36
80105	31	34585	7237	20787	33	33
103613	26	35672	7355	43978	57	55
98707	37	52168	9734	51305	50	50
136234	66	53933	11225	55593	71	71
136781	35	34474	6213	51648	32	31
105863	24	43753	4875	30552	45	42
38775	18	36456	8159	23470	33	31
179997	37	51183	11893	77530	53	51
169406	86	52742	10754	57299	64	64
19349	13	3895	786	9604	14	14
153069	21	37076	9706	34684	38	37
109510	32	24079	7796	41094	39	37
43803	8	2325	593	3439	8	8
47062	38	29354	5600	25171	38	38
110845	45	30341	7245	23437	24	23
92517	24	18992	7360	34086	22	22
58660	23	15292	4574	24649	18	18
27676	2	5842	522	2342	3	1
98550	52	28918	10905	45571	49	48
43646	5	3738	999	3255	5	5
0	0	0	0	0	0	0
67312	43	95352	9016	30002	47	46
57359	18	37478	5134	19360	33	33
104330	44	26839	6608	43320	44	41
70369	45	26783	8577	35513	56	57
65494	29	33392	1543	23536	49	49
3616	0	0	0	0	0	0
0	0	0	0	0	0	0
143931	32	25446	9803	54438	45	45
117946	65	59847	12140	56812	78	78
131175	26	28162	6678	33838	51	46
84336	24	33298	6420	32366	25	25
43410	7	2781	4	13	1	1
136250	62	37121	7979	55082	62	59
79015	30	22698	5141	31334	29	29
92937	49	27615	1311	16612	26	26
57586	3	32689	443	5084	4	4
19764	10	5752	2416	9927	10	10
105757	42	23164	8396	47413	43	43
97213	18	20304	5462	27389	36	36
113402	40	34409	7271	30425	43	41
11796	1	0	0	0	0	0
7627	0	0	0	0	0	0
121085	29	92538	4423	33510	33	32
6836	0	0	0	0	0	0
139563	46	46037	5331	40389	53	53
5118	5	0	0	0	0	0
40248	8	5444	775	6012	6	6
0	0	0	0	0	0	0
95079	21	23924	6676	22205	19	18
80763	21	52230	1489	17231	26	26
7131	0	0	0	0	0	0
4194	0	0	0	0	0	0
60378	15	8019	3080	11017	16	16
109173	47	34542	11409	46741	84	84
83484	17	21157	6769	39869	28	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=155697&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=155697&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155697&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	0.8788
R-squared	0.7722
RMSE	12463.0627

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8788 \tabularnewline
R-squared & 0.7722 \tabularnewline
RMSE & 12463.0627 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155697&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8788[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7722[/C][/ROW]
[ROW][C]RMSE[/C][C]12463.0627[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155697&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155697&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	0.8788
R-squared	0.7722
RMSE	12463.0627

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	22622	36964.3103448276	-14342.3103448276
2	73570	56385.0714285714	17184.9285714286
3	1929	891.058823529412	1037.94117647059
4	36294	44911.16	-8617.16
5	62378	83026.25	-20648.25
6	167760	83026.25	84733.75
7	52443	44911.16	7531.84
8	57283	49929.2857142857	7353.71428571428
9	36614	36964.3103448276	-350.310344827587
10	93268	83026.25	10241.75
11	35439	49929.2857142857	-14490.2857142857
12	72405	83026.25	-10621.25
13	24044	26826.4615384615	-2782.46153846154
14	55909	56385.0714285714	-476.071428571428
15	44689	36964.3103448276	7724.68965517241
16	49319	49929.2857142857	-610.285714285717
17	62075	83026.25	-20951.25
18	2341	891.058823529412	1449.94117647059
19	40551	83026.25	-42475.25
20	11621	8315.75	3305.25
21	18741	18224.875	516.125
22	84202	83026.25	1175.75
23	15334	24647.1428571429	-9313.14285714286
24	28024	24647.1428571429	3376.85714285714
25	53306	49929.2857142857	3376.71428571428
26	37918	36964.3103448276	953.689655172413
27	54819	83026.25	-28207.25
28	89058	83026.25	6031.75
29	103354	83026.25	20327.75
30	70239	83026.25	-12787.25
31	33045	36964.3103448276	-3919.31034482759
32	63852	56385.0714285714	7466.92857142857
33	30905	36964.3103448276	-6059.31034482759
34	24242	24647.1428571429	-405.142857142859
35	78907	56385.0714285714	22521.9285714286
36	0	891.058823529412	-891.058823529412
37	36005	56385.0714285714	-20380.0714285714
38	31972	36964.3103448276	-4992.31034482759
39	35853	44911.16	-9058.16
40	115301	83026.25	32274.75
41	47689	56385.0714285714	-8696.07142857143
42	34223	44911.16	-10688.16
43	43431	44911.16	-1480.16
44	52220	36964.3103448276	15255.6896551724
45	33863	44911.16	-11048.16
46	46879	44911.16	1967.84
47	23228	18224.875	5003.125
48	42827	56385.0714285714	-13558.0714285714
49	65765	83026.25	-17261.25
50	38167	44911.16	-6744.16
51	14812	8315.75	6496.25
52	32615	44911.16	-12296.16
53	82188	83026.25	-838.25
54	51763	56385.0714285714	-4622.07142857143
55	59325	56385.0714285714	2939.92857142857
56	48976	36964.3103448276	12011.6896551724
57	43384	36964.3103448276	6419.68965517241
58	26692	26826.4615384615	-134.461538461539
59	53279	44911.16	8367.84
60	20652	36964.3103448276	-16312.3103448276
61	38338	44911.16	-6573.16
62	36735	36964.3103448276	-229.310344827587
63	42764	36964.3103448276	5799.68965517241
64	44331	36964.3103448276	7366.68965517241
65	41354	36964.3103448276	4389.68965517241
66	47879	44911.16	2967.84
67	103793	83026.25	20766.75
68	52235	44911.16	7323.84
69	49825	44911.16	4913.84
70	4105	8315.75	-4210.75
71	58687	49929.2857142857	8757.71428571428
72	40745	24647.1428571429	16097.8571428571
73	33187	44911.16	-11724.16
74	14063	24647.1428571429	-10584.1428571429
75	37407	44911.16	-7504.16
76	7190	8315.75	-1125.75
77	49562	44911.16	4650.84
78	76324	44911.16	31412.84
79	21928	18224.875	3703.125
80	27860	26826.4615384615	1033.53846153846
81	28078	26826.4615384615	1251.53846153846
82	49577	36964.3103448276	12612.6896551724
83	28145	26826.4615384615	1318.53846153846
84	36241	44911.16	-8670.16
85	10824	18224.875	-7400.875
86	46892	44911.16	1980.84
87	61264	83026.25	-21762.25
88	22933	26826.4615384615	-3893.46153846154
89	20787	26826.4615384615	-6039.46153846154
90	43978	36964.3103448276	7013.68965517241
91	51305	44911.16	6393.84
92	55593	56385.0714285714	-792.071428571428
93	51648	36964.3103448276	14683.6896551724
94	30552	36964.3103448276	-6412.31034482759
95	23470	18224.875	5245.125
96	77530	56385.0714285714	21144.9285714286
97	57299	56385.0714285714	913.928571428572
98	9604	8315.75	1288.25
99	34684	56385.0714285714	-21701.0714285714
100	41094	36964.3103448276	4129.68965517241
101	3439	891.058823529412	2547.94117647059
102	25171	26826.4615384615	-1655.46153846154
103	23437	36964.3103448276	-13527.3103448276
104	34086	36964.3103448276	-2878.31034482759
105	24649	26826.4615384615	-2177.46153846154
106	2342	891.058823529412	1450.94117647059
107	45571	44911.16	659.839999999997
108	3255	8315.75	-5060.75
109	0	891.058823529412	-891.058823529412
110	30002	26826.4615384615	3175.53846153846
111	19360	18224.875	1135.125
112	43320	36964.3103448276	6355.68965517241
113	35513	26826.4615384615	8686.53846153846
114	23536	26826.4615384615	-3290.46153846154
115	0	891.058823529412	-891.058823529412
116	0	891.058823529412	-891.058823529412
117	54438	56385.0714285714	-1947.07142857143
118	56812	44911.16	11900.84
119	33838	36964.3103448276	-3126.31034482759
120	32366	36964.3103448276	-4598.31034482759
121	13	891.058823529412	-878.058823529412
122	55082	49929.2857142857	5152.71428571428
123	31334	26826.4615384615	4507.53846153846
124	16612	24647.1428571429	-8035.14285714286
125	5084	891.058823529412	4192.94117647059
126	9927	8315.75	1611.25
127	47413	44911.16	2501.84
128	27389	36964.3103448276	-9575.31034482759
129	30425	36964.3103448276	-6539.31034482759
130	0	891.058823529412	-891.058823529412
131	0	891.058823529412	-891.058823529412
132	33510	24647.1428571429	8862.85714285714
133	0	891.058823529412	-891.058823529412
134	40389	49929.2857142857	-9540.28571428572
135	0	891.058823529412	-891.058823529412
136	6012	8315.75	-2303.75
137	0	891.058823529412	-891.058823529412
138	22205	36964.3103448276	-14759.3103448276
139	17231	18224.875	-993.875
140	0	891.058823529412	-891.058823529412
141	0	891.058823529412	-891.058823529412
142	11017	18224.875	-7207.875
143	46741	44911.16	1829.84
144	39869	36964.3103448276	2904.68965517241

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 22622 & 36964.3103448276 & -14342.3103448276 \tabularnewline
2 & 73570 & 56385.0714285714 & 17184.9285714286 \tabularnewline
3 & 1929 & 891.058823529412 & 1037.94117647059 \tabularnewline
4 & 36294 & 44911.16 & -8617.16 \tabularnewline
5 & 62378 & 83026.25 & -20648.25 \tabularnewline
6 & 167760 & 83026.25 & 84733.75 \tabularnewline
7 & 52443 & 44911.16 & 7531.84 \tabularnewline
8 & 57283 & 49929.2857142857 & 7353.71428571428 \tabularnewline
9 & 36614 & 36964.3103448276 & -350.310344827587 \tabularnewline
10 & 93268 & 83026.25 & 10241.75 \tabularnewline
11 & 35439 & 49929.2857142857 & -14490.2857142857 \tabularnewline
12 & 72405 & 83026.25 & -10621.25 \tabularnewline
13 & 24044 & 26826.4615384615 & -2782.46153846154 \tabularnewline
14 & 55909 & 56385.0714285714 & -476.071428571428 \tabularnewline
15 & 44689 & 36964.3103448276 & 7724.68965517241 \tabularnewline
16 & 49319 & 49929.2857142857 & -610.285714285717 \tabularnewline
17 & 62075 & 83026.25 & -20951.25 \tabularnewline
18 & 2341 & 891.058823529412 & 1449.94117647059 \tabularnewline
19 & 40551 & 83026.25 & -42475.25 \tabularnewline
20 & 11621 & 8315.75 & 3305.25 \tabularnewline
21 & 18741 & 18224.875 & 516.125 \tabularnewline
22 & 84202 & 83026.25 & 1175.75 \tabularnewline
23 & 15334 & 24647.1428571429 & -9313.14285714286 \tabularnewline
24 & 28024 & 24647.1428571429 & 3376.85714285714 \tabularnewline
25 & 53306 & 49929.2857142857 & 3376.71428571428 \tabularnewline
26 & 37918 & 36964.3103448276 & 953.689655172413 \tabularnewline
27 & 54819 & 83026.25 & -28207.25 \tabularnewline
28 & 89058 & 83026.25 & 6031.75 \tabularnewline
29 & 103354 & 83026.25 & 20327.75 \tabularnewline
30 & 70239 & 83026.25 & -12787.25 \tabularnewline
31 & 33045 & 36964.3103448276 & -3919.31034482759 \tabularnewline
32 & 63852 & 56385.0714285714 & 7466.92857142857 \tabularnewline
33 & 30905 & 36964.3103448276 & -6059.31034482759 \tabularnewline
34 & 24242 & 24647.1428571429 & -405.142857142859 \tabularnewline
35 & 78907 & 56385.0714285714 & 22521.9285714286 \tabularnewline
36 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
37 & 36005 & 56385.0714285714 & -20380.0714285714 \tabularnewline
38 & 31972 & 36964.3103448276 & -4992.31034482759 \tabularnewline
39 & 35853 & 44911.16 & -9058.16 \tabularnewline
40 & 115301 & 83026.25 & 32274.75 \tabularnewline
41 & 47689 & 56385.0714285714 & -8696.07142857143 \tabularnewline
42 & 34223 & 44911.16 & -10688.16 \tabularnewline
43 & 43431 & 44911.16 & -1480.16 \tabularnewline
44 & 52220 & 36964.3103448276 & 15255.6896551724 \tabularnewline
45 & 33863 & 44911.16 & -11048.16 \tabularnewline
46 & 46879 & 44911.16 & 1967.84 \tabularnewline
47 & 23228 & 18224.875 & 5003.125 \tabularnewline
48 & 42827 & 56385.0714285714 & -13558.0714285714 \tabularnewline
49 & 65765 & 83026.25 & -17261.25 \tabularnewline
50 & 38167 & 44911.16 & -6744.16 \tabularnewline
51 & 14812 & 8315.75 & 6496.25 \tabularnewline
52 & 32615 & 44911.16 & -12296.16 \tabularnewline
53 & 82188 & 83026.25 & -838.25 \tabularnewline
54 & 51763 & 56385.0714285714 & -4622.07142857143 \tabularnewline
55 & 59325 & 56385.0714285714 & 2939.92857142857 \tabularnewline
56 & 48976 & 36964.3103448276 & 12011.6896551724 \tabularnewline
57 & 43384 & 36964.3103448276 & 6419.68965517241 \tabularnewline
58 & 26692 & 26826.4615384615 & -134.461538461539 \tabularnewline
59 & 53279 & 44911.16 & 8367.84 \tabularnewline
60 & 20652 & 36964.3103448276 & -16312.3103448276 \tabularnewline
61 & 38338 & 44911.16 & -6573.16 \tabularnewline
62 & 36735 & 36964.3103448276 & -229.310344827587 \tabularnewline
63 & 42764 & 36964.3103448276 & 5799.68965517241 \tabularnewline
64 & 44331 & 36964.3103448276 & 7366.68965517241 \tabularnewline
65 & 41354 & 36964.3103448276 & 4389.68965517241 \tabularnewline
66 & 47879 & 44911.16 & 2967.84 \tabularnewline
67 & 103793 & 83026.25 & 20766.75 \tabularnewline
68 & 52235 & 44911.16 & 7323.84 \tabularnewline
69 & 49825 & 44911.16 & 4913.84 \tabularnewline
70 & 4105 & 8315.75 & -4210.75 \tabularnewline
71 & 58687 & 49929.2857142857 & 8757.71428571428 \tabularnewline
72 & 40745 & 24647.1428571429 & 16097.8571428571 \tabularnewline
73 & 33187 & 44911.16 & -11724.16 \tabularnewline
74 & 14063 & 24647.1428571429 & -10584.1428571429 \tabularnewline
75 & 37407 & 44911.16 & -7504.16 \tabularnewline
76 & 7190 & 8315.75 & -1125.75 \tabularnewline
77 & 49562 & 44911.16 & 4650.84 \tabularnewline
78 & 76324 & 44911.16 & 31412.84 \tabularnewline
79 & 21928 & 18224.875 & 3703.125 \tabularnewline
80 & 27860 & 26826.4615384615 & 1033.53846153846 \tabularnewline
81 & 28078 & 26826.4615384615 & 1251.53846153846 \tabularnewline
82 & 49577 & 36964.3103448276 & 12612.6896551724 \tabularnewline
83 & 28145 & 26826.4615384615 & 1318.53846153846 \tabularnewline
84 & 36241 & 44911.16 & -8670.16 \tabularnewline
85 & 10824 & 18224.875 & -7400.875 \tabularnewline
86 & 46892 & 44911.16 & 1980.84 \tabularnewline
87 & 61264 & 83026.25 & -21762.25 \tabularnewline
88 & 22933 & 26826.4615384615 & -3893.46153846154 \tabularnewline
89 & 20787 & 26826.4615384615 & -6039.46153846154 \tabularnewline
90 & 43978 & 36964.3103448276 & 7013.68965517241 \tabularnewline
91 & 51305 & 44911.16 & 6393.84 \tabularnewline
92 & 55593 & 56385.0714285714 & -792.071428571428 \tabularnewline
93 & 51648 & 36964.3103448276 & 14683.6896551724 \tabularnewline
94 & 30552 & 36964.3103448276 & -6412.31034482759 \tabularnewline
95 & 23470 & 18224.875 & 5245.125 \tabularnewline
96 & 77530 & 56385.0714285714 & 21144.9285714286 \tabularnewline
97 & 57299 & 56385.0714285714 & 913.928571428572 \tabularnewline
98 & 9604 & 8315.75 & 1288.25 \tabularnewline
99 & 34684 & 56385.0714285714 & -21701.0714285714 \tabularnewline
100 & 41094 & 36964.3103448276 & 4129.68965517241 \tabularnewline
101 & 3439 & 891.058823529412 & 2547.94117647059 \tabularnewline
102 & 25171 & 26826.4615384615 & -1655.46153846154 \tabularnewline
103 & 23437 & 36964.3103448276 & -13527.3103448276 \tabularnewline
104 & 34086 & 36964.3103448276 & -2878.31034482759 \tabularnewline
105 & 24649 & 26826.4615384615 & -2177.46153846154 \tabularnewline
106 & 2342 & 891.058823529412 & 1450.94117647059 \tabularnewline
107 & 45571 & 44911.16 & 659.839999999997 \tabularnewline
108 & 3255 & 8315.75 & -5060.75 \tabularnewline
109 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
110 & 30002 & 26826.4615384615 & 3175.53846153846 \tabularnewline
111 & 19360 & 18224.875 & 1135.125 \tabularnewline
112 & 43320 & 36964.3103448276 & 6355.68965517241 \tabularnewline
113 & 35513 & 26826.4615384615 & 8686.53846153846 \tabularnewline
114 & 23536 & 26826.4615384615 & -3290.46153846154 \tabularnewline
115 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
116 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
117 & 54438 & 56385.0714285714 & -1947.07142857143 \tabularnewline
118 & 56812 & 44911.16 & 11900.84 \tabularnewline
119 & 33838 & 36964.3103448276 & -3126.31034482759 \tabularnewline
120 & 32366 & 36964.3103448276 & -4598.31034482759 \tabularnewline
121 & 13 & 891.058823529412 & -878.058823529412 \tabularnewline
122 & 55082 & 49929.2857142857 & 5152.71428571428 \tabularnewline
123 & 31334 & 26826.4615384615 & 4507.53846153846 \tabularnewline
124 & 16612 & 24647.1428571429 & -8035.14285714286 \tabularnewline
125 & 5084 & 891.058823529412 & 4192.94117647059 \tabularnewline
126 & 9927 & 8315.75 & 1611.25 \tabularnewline
127 & 47413 & 44911.16 & 2501.84 \tabularnewline
128 & 27389 & 36964.3103448276 & -9575.31034482759 \tabularnewline
129 & 30425 & 36964.3103448276 & -6539.31034482759 \tabularnewline
130 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
131 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
132 & 33510 & 24647.1428571429 & 8862.85714285714 \tabularnewline
133 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
134 & 40389 & 49929.2857142857 & -9540.28571428572 \tabularnewline
135 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
136 & 6012 & 8315.75 & -2303.75 \tabularnewline
137 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
138 & 22205 & 36964.3103448276 & -14759.3103448276 \tabularnewline
139 & 17231 & 18224.875 & -993.875 \tabularnewline
140 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
141 & 0 & 891.058823529412 & -891.058823529412 \tabularnewline
142 & 11017 & 18224.875 & -7207.875 \tabularnewline
143 & 46741 & 44911.16 & 1829.84 \tabularnewline
144 & 39869 & 36964.3103448276 & 2904.68965517241 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155697&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]22622[/C][C]36964.3103448276[/C][C]-14342.3103448276[/C][/ROW]
[ROW][C]2[/C][C]73570[/C][C]56385.0714285714[/C][C]17184.9285714286[/C][/ROW]
[ROW][C]3[/C][C]1929[/C][C]891.058823529412[/C][C]1037.94117647059[/C][/ROW]
[ROW][C]4[/C][C]36294[/C][C]44911.16[/C][C]-8617.16[/C][/ROW]
[ROW][C]5[/C][C]62378[/C][C]83026.25[/C][C]-20648.25[/C][/ROW]
[ROW][C]6[/C][C]167760[/C][C]83026.25[/C][C]84733.75[/C][/ROW]
[ROW][C]7[/C][C]52443[/C][C]44911.16[/C][C]7531.84[/C][/ROW]
[ROW][C]8[/C][C]57283[/C][C]49929.2857142857[/C][C]7353.71428571428[/C][/ROW]
[ROW][C]9[/C][C]36614[/C][C]36964.3103448276[/C][C]-350.310344827587[/C][/ROW]
[ROW][C]10[/C][C]93268[/C][C]83026.25[/C][C]10241.75[/C][/ROW]
[ROW][C]11[/C][C]35439[/C][C]49929.2857142857[/C][C]-14490.2857142857[/C][/ROW]
[ROW][C]12[/C][C]72405[/C][C]83026.25[/C][C]-10621.25[/C][/ROW]
[ROW][C]13[/C][C]24044[/C][C]26826.4615384615[/C][C]-2782.46153846154[/C][/ROW]
[ROW][C]14[/C][C]55909[/C][C]56385.0714285714[/C][C]-476.071428571428[/C][/ROW]
[ROW][C]15[/C][C]44689[/C][C]36964.3103448276[/C][C]7724.68965517241[/C][/ROW]
[ROW][C]16[/C][C]49319[/C][C]49929.2857142857[/C][C]-610.285714285717[/C][/ROW]
[ROW][C]17[/C][C]62075[/C][C]83026.25[/C][C]-20951.25[/C][/ROW]
[ROW][C]18[/C][C]2341[/C][C]891.058823529412[/C][C]1449.94117647059[/C][/ROW]
[ROW][C]19[/C][C]40551[/C][C]83026.25[/C][C]-42475.25[/C][/ROW]
[ROW][C]20[/C][C]11621[/C][C]8315.75[/C][C]3305.25[/C][/ROW]
[ROW][C]21[/C][C]18741[/C][C]18224.875[/C][C]516.125[/C][/ROW]
[ROW][C]22[/C][C]84202[/C][C]83026.25[/C][C]1175.75[/C][/ROW]
[ROW][C]23[/C][C]15334[/C][C]24647.1428571429[/C][C]-9313.14285714286[/C][/ROW]
[ROW][C]24[/C][C]28024[/C][C]24647.1428571429[/C][C]3376.85714285714[/C][/ROW]
[ROW][C]25[/C][C]53306[/C][C]49929.2857142857[/C][C]3376.71428571428[/C][/ROW]
[ROW][C]26[/C][C]37918[/C][C]36964.3103448276[/C][C]953.689655172413[/C][/ROW]
[ROW][C]27[/C][C]54819[/C][C]83026.25[/C][C]-28207.25[/C][/ROW]
[ROW][C]28[/C][C]89058[/C][C]83026.25[/C][C]6031.75[/C][/ROW]
[ROW][C]29[/C][C]103354[/C][C]83026.25[/C][C]20327.75[/C][/ROW]
[ROW][C]30[/C][C]70239[/C][C]83026.25[/C][C]-12787.25[/C][/ROW]
[ROW][C]31[/C][C]33045[/C][C]36964.3103448276[/C][C]-3919.31034482759[/C][/ROW]
[ROW][C]32[/C][C]63852[/C][C]56385.0714285714[/C][C]7466.92857142857[/C][/ROW]
[ROW][C]33[/C][C]30905[/C][C]36964.3103448276[/C][C]-6059.31034482759[/C][/ROW]
[ROW][C]34[/C][C]24242[/C][C]24647.1428571429[/C][C]-405.142857142859[/C][/ROW]
[ROW][C]35[/C][C]78907[/C][C]56385.0714285714[/C][C]22521.9285714286[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]37[/C][C]36005[/C][C]56385.0714285714[/C][C]-20380.0714285714[/C][/ROW]
[ROW][C]38[/C][C]31972[/C][C]36964.3103448276[/C][C]-4992.31034482759[/C][/ROW]
[ROW][C]39[/C][C]35853[/C][C]44911.16[/C][C]-9058.16[/C][/ROW]
[ROW][C]40[/C][C]115301[/C][C]83026.25[/C][C]32274.75[/C][/ROW]
[ROW][C]41[/C][C]47689[/C][C]56385.0714285714[/C][C]-8696.07142857143[/C][/ROW]
[ROW][C]42[/C][C]34223[/C][C]44911.16[/C][C]-10688.16[/C][/ROW]
[ROW][C]43[/C][C]43431[/C][C]44911.16[/C][C]-1480.16[/C][/ROW]
[ROW][C]44[/C][C]52220[/C][C]36964.3103448276[/C][C]15255.6896551724[/C][/ROW]
[ROW][C]45[/C][C]33863[/C][C]44911.16[/C][C]-11048.16[/C][/ROW]
[ROW][C]46[/C][C]46879[/C][C]44911.16[/C][C]1967.84[/C][/ROW]
[ROW][C]47[/C][C]23228[/C][C]18224.875[/C][C]5003.125[/C][/ROW]
[ROW][C]48[/C][C]42827[/C][C]56385.0714285714[/C][C]-13558.0714285714[/C][/ROW]
[ROW][C]49[/C][C]65765[/C][C]83026.25[/C][C]-17261.25[/C][/ROW]
[ROW][C]50[/C][C]38167[/C][C]44911.16[/C][C]-6744.16[/C][/ROW]
[ROW][C]51[/C][C]14812[/C][C]8315.75[/C][C]6496.25[/C][/ROW]
[ROW][C]52[/C][C]32615[/C][C]44911.16[/C][C]-12296.16[/C][/ROW]
[ROW][C]53[/C][C]82188[/C][C]83026.25[/C][C]-838.25[/C][/ROW]
[ROW][C]54[/C][C]51763[/C][C]56385.0714285714[/C][C]-4622.07142857143[/C][/ROW]
[ROW][C]55[/C][C]59325[/C][C]56385.0714285714[/C][C]2939.92857142857[/C][/ROW]
[ROW][C]56[/C][C]48976[/C][C]36964.3103448276[/C][C]12011.6896551724[/C][/ROW]
[ROW][C]57[/C][C]43384[/C][C]36964.3103448276[/C][C]6419.68965517241[/C][/ROW]
[ROW][C]58[/C][C]26692[/C][C]26826.4615384615[/C][C]-134.461538461539[/C][/ROW]
[ROW][C]59[/C][C]53279[/C][C]44911.16[/C][C]8367.84[/C][/ROW]
[ROW][C]60[/C][C]20652[/C][C]36964.3103448276[/C][C]-16312.3103448276[/C][/ROW]
[ROW][C]61[/C][C]38338[/C][C]44911.16[/C][C]-6573.16[/C][/ROW]
[ROW][C]62[/C][C]36735[/C][C]36964.3103448276[/C][C]-229.310344827587[/C][/ROW]
[ROW][C]63[/C][C]42764[/C][C]36964.3103448276[/C][C]5799.68965517241[/C][/ROW]
[ROW][C]64[/C][C]44331[/C][C]36964.3103448276[/C][C]7366.68965517241[/C][/ROW]
[ROW][C]65[/C][C]41354[/C][C]36964.3103448276[/C][C]4389.68965517241[/C][/ROW]
[ROW][C]66[/C][C]47879[/C][C]44911.16[/C][C]2967.84[/C][/ROW]
[ROW][C]67[/C][C]103793[/C][C]83026.25[/C][C]20766.75[/C][/ROW]
[ROW][C]68[/C][C]52235[/C][C]44911.16[/C][C]7323.84[/C][/ROW]
[ROW][C]69[/C][C]49825[/C][C]44911.16[/C][C]4913.84[/C][/ROW]
[ROW][C]70[/C][C]4105[/C][C]8315.75[/C][C]-4210.75[/C][/ROW]
[ROW][C]71[/C][C]58687[/C][C]49929.2857142857[/C][C]8757.71428571428[/C][/ROW]
[ROW][C]72[/C][C]40745[/C][C]24647.1428571429[/C][C]16097.8571428571[/C][/ROW]
[ROW][C]73[/C][C]33187[/C][C]44911.16[/C][C]-11724.16[/C][/ROW]
[ROW][C]74[/C][C]14063[/C][C]24647.1428571429[/C][C]-10584.1428571429[/C][/ROW]
[ROW][C]75[/C][C]37407[/C][C]44911.16[/C][C]-7504.16[/C][/ROW]
[ROW][C]76[/C][C]7190[/C][C]8315.75[/C][C]-1125.75[/C][/ROW]
[ROW][C]77[/C][C]49562[/C][C]44911.16[/C][C]4650.84[/C][/ROW]
[ROW][C]78[/C][C]76324[/C][C]44911.16[/C][C]31412.84[/C][/ROW]
[ROW][C]79[/C][C]21928[/C][C]18224.875[/C][C]3703.125[/C][/ROW]
[ROW][C]80[/C][C]27860[/C][C]26826.4615384615[/C][C]1033.53846153846[/C][/ROW]
[ROW][C]81[/C][C]28078[/C][C]26826.4615384615[/C][C]1251.53846153846[/C][/ROW]
[ROW][C]82[/C][C]49577[/C][C]36964.3103448276[/C][C]12612.6896551724[/C][/ROW]
[ROW][C]83[/C][C]28145[/C][C]26826.4615384615[/C][C]1318.53846153846[/C][/ROW]
[ROW][C]84[/C][C]36241[/C][C]44911.16[/C][C]-8670.16[/C][/ROW]
[ROW][C]85[/C][C]10824[/C][C]18224.875[/C][C]-7400.875[/C][/ROW]
[ROW][C]86[/C][C]46892[/C][C]44911.16[/C][C]1980.84[/C][/ROW]
[ROW][C]87[/C][C]61264[/C][C]83026.25[/C][C]-21762.25[/C][/ROW]
[ROW][C]88[/C][C]22933[/C][C]26826.4615384615[/C][C]-3893.46153846154[/C][/ROW]
[ROW][C]89[/C][C]20787[/C][C]26826.4615384615[/C][C]-6039.46153846154[/C][/ROW]
[ROW][C]90[/C][C]43978[/C][C]36964.3103448276[/C][C]7013.68965517241[/C][/ROW]
[ROW][C]91[/C][C]51305[/C][C]44911.16[/C][C]6393.84[/C][/ROW]
[ROW][C]92[/C][C]55593[/C][C]56385.0714285714[/C][C]-792.071428571428[/C][/ROW]
[ROW][C]93[/C][C]51648[/C][C]36964.3103448276[/C][C]14683.6896551724[/C][/ROW]
[ROW][C]94[/C][C]30552[/C][C]36964.3103448276[/C][C]-6412.31034482759[/C][/ROW]
[ROW][C]95[/C][C]23470[/C][C]18224.875[/C][C]5245.125[/C][/ROW]
[ROW][C]96[/C][C]77530[/C][C]56385.0714285714[/C][C]21144.9285714286[/C][/ROW]
[ROW][C]97[/C][C]57299[/C][C]56385.0714285714[/C][C]913.928571428572[/C][/ROW]
[ROW][C]98[/C][C]9604[/C][C]8315.75[/C][C]1288.25[/C][/ROW]
[ROW][C]99[/C][C]34684[/C][C]56385.0714285714[/C][C]-21701.0714285714[/C][/ROW]
[ROW][C]100[/C][C]41094[/C][C]36964.3103448276[/C][C]4129.68965517241[/C][/ROW]
[ROW][C]101[/C][C]3439[/C][C]891.058823529412[/C][C]2547.94117647059[/C][/ROW]
[ROW][C]102[/C][C]25171[/C][C]26826.4615384615[/C][C]-1655.46153846154[/C][/ROW]
[ROW][C]103[/C][C]23437[/C][C]36964.3103448276[/C][C]-13527.3103448276[/C][/ROW]
[ROW][C]104[/C][C]34086[/C][C]36964.3103448276[/C][C]-2878.31034482759[/C][/ROW]
[ROW][C]105[/C][C]24649[/C][C]26826.4615384615[/C][C]-2177.46153846154[/C][/ROW]
[ROW][C]106[/C][C]2342[/C][C]891.058823529412[/C][C]1450.94117647059[/C][/ROW]
[ROW][C]107[/C][C]45571[/C][C]44911.16[/C][C]659.839999999997[/C][/ROW]
[ROW][C]108[/C][C]3255[/C][C]8315.75[/C][C]-5060.75[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]110[/C][C]30002[/C][C]26826.4615384615[/C][C]3175.53846153846[/C][/ROW]
[ROW][C]111[/C][C]19360[/C][C]18224.875[/C][C]1135.125[/C][/ROW]
[ROW][C]112[/C][C]43320[/C][C]36964.3103448276[/C][C]6355.68965517241[/C][/ROW]
[ROW][C]113[/C][C]35513[/C][C]26826.4615384615[/C][C]8686.53846153846[/C][/ROW]
[ROW][C]114[/C][C]23536[/C][C]26826.4615384615[/C][C]-3290.46153846154[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]117[/C][C]54438[/C][C]56385.0714285714[/C][C]-1947.07142857143[/C][/ROW]
[ROW][C]118[/C][C]56812[/C][C]44911.16[/C][C]11900.84[/C][/ROW]
[ROW][C]119[/C][C]33838[/C][C]36964.3103448276[/C][C]-3126.31034482759[/C][/ROW]
[ROW][C]120[/C][C]32366[/C][C]36964.3103448276[/C][C]-4598.31034482759[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]891.058823529412[/C][C]-878.058823529412[/C][/ROW]
[ROW][C]122[/C][C]55082[/C][C]49929.2857142857[/C][C]5152.71428571428[/C][/ROW]
[ROW][C]123[/C][C]31334[/C][C]26826.4615384615[/C][C]4507.53846153846[/C][/ROW]
[ROW][C]124[/C][C]16612[/C][C]24647.1428571429[/C][C]-8035.14285714286[/C][/ROW]
[ROW][C]125[/C][C]5084[/C][C]891.058823529412[/C][C]4192.94117647059[/C][/ROW]
[ROW][C]126[/C][C]9927[/C][C]8315.75[/C][C]1611.25[/C][/ROW]
[ROW][C]127[/C][C]47413[/C][C]44911.16[/C][C]2501.84[/C][/ROW]
[ROW][C]128[/C][C]27389[/C][C]36964.3103448276[/C][C]-9575.31034482759[/C][/ROW]
[ROW][C]129[/C][C]30425[/C][C]36964.3103448276[/C][C]-6539.31034482759[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]132[/C][C]33510[/C][C]24647.1428571429[/C][C]8862.85714285714[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]134[/C][C]40389[/C][C]49929.2857142857[/C][C]-9540.28571428572[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]136[/C][C]6012[/C][C]8315.75[/C][C]-2303.75[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]138[/C][C]22205[/C][C]36964.3103448276[/C][C]-14759.3103448276[/C][/ROW]
[ROW][C]139[/C][C]17231[/C][C]18224.875[/C][C]-993.875[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]891.058823529412[/C][C]-891.058823529412[/C][/ROW]
[ROW][C]142[/C][C]11017[/C][C]18224.875[/C][C]-7207.875[/C][/ROW]
[ROW][C]143[/C][C]46741[/C][C]44911.16[/C][C]1829.84[/C][/ROW]
[ROW][C]144[/C][C]39869[/C][C]36964.3103448276[/C][C]2904.68965517241[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155697&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155697&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	22622	36964.3103448276	-14342.3103448276
2	73570	56385.0714285714	17184.9285714286
3	1929	891.058823529412	1037.94117647059
4	36294	44911.16	-8617.16
5	62378	83026.25	-20648.25
6	167760	83026.25	84733.75
7	52443	44911.16	7531.84
8	57283	49929.2857142857	7353.71428571428
9	36614	36964.3103448276	-350.310344827587
10	93268	83026.25	10241.75
11	35439	49929.2857142857	-14490.2857142857
12	72405	83026.25	-10621.25
13	24044	26826.4615384615	-2782.46153846154
14	55909	56385.0714285714	-476.071428571428
15	44689	36964.3103448276	7724.68965517241
16	49319	49929.2857142857	-610.285714285717
17	62075	83026.25	-20951.25
18	2341	891.058823529412	1449.94117647059
19	40551	83026.25	-42475.25
20	11621	8315.75	3305.25
21	18741	18224.875	516.125
22	84202	83026.25	1175.75
23	15334	24647.1428571429	-9313.14285714286
24	28024	24647.1428571429	3376.85714285714
25	53306	49929.2857142857	3376.71428571428
26	37918	36964.3103448276	953.689655172413
27	54819	83026.25	-28207.25
28	89058	83026.25	6031.75
29	103354	83026.25	20327.75
30	70239	83026.25	-12787.25
31	33045	36964.3103448276	-3919.31034482759
32	63852	56385.0714285714	7466.92857142857
33	30905	36964.3103448276	-6059.31034482759
34	24242	24647.1428571429	-405.142857142859
35	78907	56385.0714285714	22521.9285714286
36	0	891.058823529412	-891.058823529412
37	36005	56385.0714285714	-20380.0714285714
38	31972	36964.3103448276	-4992.31034482759
39	35853	44911.16	-9058.16
40	115301	83026.25	32274.75
41	47689	56385.0714285714	-8696.07142857143
42	34223	44911.16	-10688.16
43	43431	44911.16	-1480.16
44	52220	36964.3103448276	15255.6896551724
45	33863	44911.16	-11048.16
46	46879	44911.16	1967.84
47	23228	18224.875	5003.125
48	42827	56385.0714285714	-13558.0714285714
49	65765	83026.25	-17261.25
50	38167	44911.16	-6744.16
51	14812	8315.75	6496.25
52	32615	44911.16	-12296.16
53	82188	83026.25	-838.25
54	51763	56385.0714285714	-4622.07142857143
55	59325	56385.0714285714	2939.92857142857
56	48976	36964.3103448276	12011.6896551724
57	43384	36964.3103448276	6419.68965517241
58	26692	26826.4615384615	-134.461538461539
59	53279	44911.16	8367.84
60	20652	36964.3103448276	-16312.3103448276
61	38338	44911.16	-6573.16
62	36735	36964.3103448276	-229.310344827587
63	42764	36964.3103448276	5799.68965517241
64	44331	36964.3103448276	7366.68965517241
65	41354	36964.3103448276	4389.68965517241
66	47879	44911.16	2967.84
67	103793	83026.25	20766.75
68	52235	44911.16	7323.84
69	49825	44911.16	4913.84
70	4105	8315.75	-4210.75
71	58687	49929.2857142857	8757.71428571428
72	40745	24647.1428571429	16097.8571428571
73	33187	44911.16	-11724.16
74	14063	24647.1428571429	-10584.1428571429
75	37407	44911.16	-7504.16
76	7190	8315.75	-1125.75
77	49562	44911.16	4650.84
78	76324	44911.16	31412.84
79	21928	18224.875	3703.125
80	27860	26826.4615384615	1033.53846153846
81	28078	26826.4615384615	1251.53846153846
82	49577	36964.3103448276	12612.6896551724
83	28145	26826.4615384615	1318.53846153846
84	36241	44911.16	-8670.16
85	10824	18224.875	-7400.875
86	46892	44911.16	1980.84
87	61264	83026.25	-21762.25
88	22933	26826.4615384615	-3893.46153846154
89	20787	26826.4615384615	-6039.46153846154
90	43978	36964.3103448276	7013.68965517241
91	51305	44911.16	6393.84
92	55593	56385.0714285714	-792.071428571428
93	51648	36964.3103448276	14683.6896551724
94	30552	36964.3103448276	-6412.31034482759
95	23470	18224.875	5245.125
96	77530	56385.0714285714	21144.9285714286
97	57299	56385.0714285714	913.928571428572
98	9604	8315.75	1288.25
99	34684	56385.0714285714	-21701.0714285714
100	41094	36964.3103448276	4129.68965517241
101	3439	891.058823529412	2547.94117647059
102	25171	26826.4615384615	-1655.46153846154
103	23437	36964.3103448276	-13527.3103448276
104	34086	36964.3103448276	-2878.31034482759
105	24649	26826.4615384615	-2177.46153846154
106	2342	891.058823529412	1450.94117647059
107	45571	44911.16	659.839999999997
108	3255	8315.75	-5060.75
109	0	891.058823529412	-891.058823529412
110	30002	26826.4615384615	3175.53846153846
111	19360	18224.875	1135.125
112	43320	36964.3103448276	6355.68965517241
113	35513	26826.4615384615	8686.53846153846
114	23536	26826.4615384615	-3290.46153846154
115	0	891.058823529412	-891.058823529412
116	0	891.058823529412	-891.058823529412
117	54438	56385.0714285714	-1947.07142857143
118	56812	44911.16	11900.84
119	33838	36964.3103448276	-3126.31034482759
120	32366	36964.3103448276	-4598.31034482759
121	13	891.058823529412	-878.058823529412
122	55082	49929.2857142857	5152.71428571428
123	31334	26826.4615384615	4507.53846153846
124	16612	24647.1428571429	-8035.14285714286
125	5084	891.058823529412	4192.94117647059
126	9927	8315.75	1611.25
127	47413	44911.16	2501.84
128	27389	36964.3103448276	-9575.31034482759
129	30425	36964.3103448276	-6539.31034482759
130	0	891.058823529412	-891.058823529412
131	0	891.058823529412	-891.058823529412
132	33510	24647.1428571429	8862.85714285714
133	0	891.058823529412	-891.058823529412
134	40389	49929.2857142857	-9540.28571428572
135	0	891.058823529412	-891.058823529412
136	6012	8315.75	-2303.75
137	0	891.058823529412	-891.058823529412
138	22205	36964.3103448276	-14759.3103448276
139	17231	18224.875	-993.875
140	0	891.058823529412	-891.058823529412
141	0	891.058823529412	-891.058823529412
142	11017	18224.875	-7207.875
143	46741	44911.16	1829.84
144	39869	36964.3103448276	2904.68965517241

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 = kendall ; par2 = equal ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 5 ; 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