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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Thu, 22 Dec 2011 08:51:44 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t1324562239i9dsmnb0yu7eozr.htm/, Retrieved Fri, 03 May 2024 12:44:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159463, Retrieved Fri, 03 May 2024 12:44:54 +0000

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Estimated Impact

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

-     [Kendall tau Correlation Matrix] [] [2011-12-21 14:12:22] [247ecd499838c320214983d11495b1c2]
- R  D  [Kendall tau Correlation Matrix] [Pearson correlati...] [2011-12-22 13:04:45] [74be16979710d4c4e7c6647856088456]
- RMP       [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-22 13:51:44] [02062aee8449a97abc7c7e765b9155b5] [Current]
- R P         [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-22 14:08:50] [baac05fe722f73c103cc2d713fa5bd78] 
-               [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-22 14:11:35] [baac05fe722f73c103cc2d713fa5bd78] 
-   P             [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-22 15:32:38] [74be16979710d4c4e7c6647856088456] 
-   P               [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-22 18:08:00] [74be16979710d4c4e7c6647856088456] 

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Dataseries X:

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Histogram

Boxplots

1845	162687	48	21	73	20465	23975
1796	201906	58	20	56	33629	85634
192	7215	0	0	0	1423	1929
2444	146367	67	27	63	25629	36294
3567	257045	83	31	116	54002	72255
6917	524450	136	36	138	151036	189748
1840	188294	65	23	71	33287	61834
1740	195674	86	30	107	31172	68167
2078	177020	62	30	50	28113	38462
3118	330194	72	27	81	57803	101219
1946	121844	50	24	58	49830	43270
2370	203938	88	30	91	52143	76183
1944	113213	61	22	41	21055	31476
3198	220751	79	28	100	47007	62157
1491	172905	56	18	61	28735	46261
1573	156326	54	22	74	59147	50063
1807	145178	81	37	147	78950	64483
1309	89171	13	15	45	13497	2341
2820	172624	74	34	110	46154	48149
776	39790	18	18	41	53249	12743
1162	87927	31	15	37	10726	18743
2818	241285	99	30	84	83700	97057
1770	198587	38	25	67	40400	17675
2315	146946	59	34	69	33797	33106
1994	159763	54	21	58	36205	53311
1806	207078	63	21	60	30165	42754
2152	212394	66	25	88	58534	59056
1457	201536	90	31	75	44663	101621
3000	394662	72	31	98	92556	118120
2236	217892	61	20	67	40078	79572
1685	182286	61	28	84	34711	42744
1626	181740	61	22	62	31076	65931
2257	137978	53	17	35	74608	38575
3373	255929	118	25	74	58092	28795
2571	236489	73	25	93	42009	94440
1	0	0	0	0	0	0
2142	230761	54	31	87	36022	38229
1878	132807	54	14	39	23333	31972
2190	157118	46	35	101	53349	40071
2186	253254	83	34	135	92596	132480
2533	269329	106	22	76	49598	62797
1823	161273	44	34	118	44093	40429
1095	107181	27	23	76	84205	45545
2162	195891	64	24	65	63369	57568
1365	139667	71	26	97	60132	39019
1245	171101	44	23	70	37403	53866
756	81407	23	35	63	24460	38345
2417	247563	78	24	96	46456	50210
2327	239807	60	31	112	66616	80947
2786	172743	73	30	82	41554	43461
658	48188	12	22	39	22346	14812
2012	169355	104	23	69	30874	37819
2616	325322	86	27	93	68701	102738
2071	241518	57	30	76	35728	54509
1911	195583	67	33	117	29010	62956
1775	159913	44	12	31	23110	55411
1919	220241	53	26	65	38844	50611
1047	101694	26	26	78	27084	26692
1190	157258	67	23	87	35139	60056
2890	202536	36	38	85	57476	25155
1836	173505	56	32	119	33277	42840
2254	150518	52	21	65	31141	39358
1392	141491	54	22	60	61281	47241
1326	125612	57	26	67	25820	49611
1317	166049	27	28	94	23284	41833
1525	124197	58	33	100	35378	48930
2335	195043	76	36	135	74990	110600
2897	138708	93	25	71	29653	52235
1118	116552	59	25	78	64622	53986
340	31970	5	21	42	4157	4105
2977	258158	57	19	42	29245	59331
1452	151194	42	12	8	50008	47796
1550	135926	88	30	86	52338	38302
1685	119629	53	21	41	13310	14063
2728	171518	81	39	131	92901	54414
1574	108949	35	32	91	10956	9903
2413	183471	102	28	102	34241	53987
2563	159966	71	29	91	75043	88937
1079	93786	28	21	46	21152	21928
1235	84971	34	31	60	42249	29487
980	88882	54	26	69	42005	35334
2246	304603	49	29	95	41152	57596
1076	75101	30	23	17	14399	29750
1637	145043	57	25	61	28263	41029
1208	95827	54	22	55	17215	12416
1865	173924	38	26	55	48140	51158
2726	241957	63	33	124	62897	79935
1208	115367	58	24	73	22883	26552
1419	118408	46	24	73	41622	25807
1609	164078	46	21	67	40715	50620
1864	158931	51	28	66	65897	61467
2412	184139	87	28	77	76542	65292
1238	152856	39	25	83	37477	55516
1463	144014	28	15	55	53216	42006
973	62535	26	13	27	40911	26273
2319	245196	52	36	115	57021	90248
1890	199841	96	27	85	73116	61476
223	19349	13	1	0	3895	9604
2526	247280	43	24	83	46609	45108
2072	159408	42	31	90	29351	47232
778	72128	30	4	4	2325	3439
1194	104253	59	21	60	31747	30553
1424	151090	73	27	74	32665	24751
1328	137382	39	23	52	19249	34458
839	87448	36	12	24	15292	24649
596	27676	2	16	17	5842	2342
1671	165507	102	29	105	33994	52739
1168	132148	30	26	20	13018	6245
0	0	0	0	0	0	0
1106	95778	46	25	51	98177	35381
1148	109001	25	21	76	37941	19595
1485	158833	59	24	61	31032	50848
1526	147690	60	21	70	32683	39443
962	89887	36	21	38	34545	27023
78	3616	0	0	0	0	0
0	0	0	0	0	0	0
1184	199005	45	23	81	27525	61022
1671	160930	79	33	78	66856	63528
2142	177948	30	32	76	28549	34835
1015	136061	43	23	89	38610	37172
778	43410	7	1	3	2781	13
1856	184277	80	29	87	41211	62548
1056	108858	32	20	55	22698	31334
2297	151030	84	33	73	41194	20839
731	60493	3	12	32	32689	5084
285	19764	10	2	4	5752	9927
1872	177559	47	21	70	26757	53229
1181	140281	35	28	102	22527	29877
1725	164249	54	35	109	44810	37310
256	11796	1	2	1	0	0
98	10674	0	0	0	0	0
1435	151322	46	18	39	100674	50067
41	6836	0	1	0	0	0
1930	174712	51	21	45	57786	47708
42	5118	5	0	0	0	0
528	40248	8	4	7	5444	6012
0	0	0	0	0	0	0
1121	127628	38	29	86	28470	27749
1305	88837	21	26	52	61849	47555
81	7131	0	0	0	0	0
262	9056	0	4	1	2179	1336
1100	87957	18	19	49	8019	11017
1290	144470	53	22	72	39644	55184
1248	111408	17	22	56	23494	43485

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	'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159463&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159463&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159463&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	'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.8696
R-squared	0.7563
RMSE	445.6349

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8696 \tabularnewline
R-squared & 0.7563 \tabularnewline
RMSE & 445.6349 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159463&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8696[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7563[/C][/ROW]
[ROW][C]RMSE[/C][C]445.6349[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159463&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159463&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	0.8696
R-squared	0.7563
RMSE	445.6349

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1845	1655.47826086957	189.521739130435
2	1796	1655.47826086957	140.521739130435
3	192	155.9375	36.0625
4	2444	2034.48484848485	409.515151515152
5	3567	3371.88888888889	195.111111111111
6	6917	3371.88888888889	3545.11111111111
7	1840	2034.48484848485	-194.484848484848
8	1740	2034.48484848485	-294.484848484848
9	2078	2034.48484848485	43.5151515151515
10	3118	3371.88888888889	-253.888888888889
11	1946	1430.58823529412	515.411764705882
12	2370	2034.48484848485	335.515151515152
13	1944	2034.48484848485	-90.4848484848485
14	3198	2481.90909090909	716.090909090909
15	1491	1655.47826086957	-164.478260869565
16	1573	1655.47826086957	-82.4782608695652
17	1807	2034.48484848485	-227.484848484848
18	1309	1147.05555555556	161.944444444444
19	2820	2034.48484848485	785.515151515152
20	776	783.777777777778	-7.77777777777783
21	1162	1147.05555555556	14.9444444444443
22	2818	2481.90909090909	336.090909090909
23	1770	1655.47826086957	114.521739130435
24	2315	2124.125	190.875
25	1994	1655.47826086957	338.521739130435
26	1806	2034.48484848485	-228.484848484848
27	2152	2034.48484848485	117.515151515152
28	1457	2034.48484848485	-577.484848484848
29	3000	3371.88888888889	-371.888888888889
30	2236	2034.48484848485	201.515151515152
31	1685	2034.48484848485	-349.484848484848
32	1626	2034.48484848485	-408.484848484848
33	2257	1430.58823529412	826.411764705882
34	3373	3371.88888888889	1.11111111111131
35	2571	2481.90909090909	89.090909090909
36	1	155.9375	-154.9375
37	2142	2481.90909090909	-339.909090909091
38	1878	1430.58823529412	447.411764705882
39	2190	2124.125	65.875
40	2186	2481.90909090909	-295.909090909091
41	2533	3371.88888888889	-838.888888888889
42	1823	2124.125	-301.125
43	1095	1147.05555555556	-52.0555555555557
44	2162	2034.48484848485	127.515151515152
45	1365	2034.48484848485	-669.484848484848
46	1245	1655.47826086957	-410.478260869565
47	756	783.777777777778	-27.7777777777778
48	2417	2481.90909090909	-64.909090909091
49	2327	2481.90909090909	-154.909090909091
50	2786	2034.48484848485	751.515151515152
51	658	783.777777777778	-125.777777777778
52	2012	2034.48484848485	-22.4848484848485
53	2616	3371.88888888889	-755.888888888889
54	2071	2481.90909090909	-410.909090909091
55	1911	2034.48484848485	-123.484848484848
56	1775	1655.47826086957	119.521739130435
57	1919	1655.47826086957	263.521739130435
58	1047	1147.05555555556	-100.055555555556
59	1190	2034.48484848485	-844.484848484848
60	2890	2124.125	765.875
61	1836	2124.125	-288.125
62	2254	1655.47826086957	598.521739130435
63	1392	1430.58823529412	-38.5882352941176
64	1326	1430.58823529412	-104.588235294118
65	1317	1655.47826086957	-338.478260869565
66	1525	1430.58823529412	94.4117647058824
67	2335	2034.48484848485	300.515151515152
68	2897	2034.48484848485	862.515151515152
69	1118	1430.58823529412	-312.588235294118
70	340	155.9375	184.0625
71	2977	3371.88888888889	-394.888888888889
72	1452	1655.47826086957	-203.478260869565
73	1550	2034.48484848485	-484.484848484848
74	1685	1430.58823529412	254.411764705882
75	2728	2034.48484848485	693.515151515152
76	1574	1147.05555555556	426.944444444444
77	2413	2034.48484848485	378.515151515152
78	2563	2034.48484848485	528.515151515152
79	1079	1147.05555555556	-68.0555555555557
80	1235	1147.05555555556	87.9444444444443
81	980	1147.05555555556	-167.055555555556
82	2246	3371.88888888889	-1125.88888888889
83	1076	783.777777777778	292.222222222222
84	1637	1655.47826086957	-18.4782608695652
85	1208	1147.05555555556	60.9444444444443
86	1865	1655.47826086957	209.521739130435
87	2726	2481.90909090909	244.090909090909
88	1208	1430.58823529412	-222.588235294118
89	1419	1430.58823529412	-11.5882352941176
90	1609	1655.47826086957	-46.4782608695652
91	1864	1655.47826086957	208.521739130435
92	2412	2034.48484848485	377.515151515152
93	1238	1655.47826086957	-417.478260869565
94	1463	1430.58823529412	32.4117647058824
95	973	783.777777777778	189.222222222222
96	2319	2481.90909090909	-162.909090909091
97	1890	2034.48484848485	-144.484848484848
98	223	155.9375	67.0625
99	2526	2481.90909090909	44.090909090909
100	2072	2124.125	-52.125
101	778	783.777777777778	-5.77777777777783
102	1194	1147.05555555556	46.9444444444443
103	1424	2034.48484848485	-610.484848484848
104	1328	1430.58823529412	-102.588235294118
105	839	1147.05555555556	-308.055555555556
106	596	155.9375	440.0625
107	1671	2034.48484848485	-363.484848484848
108	1168	1430.58823529412	-262.588235294118
109	0	155.9375	-155.9375
110	1106	1147.05555555556	-41.0555555555557
111	1148	1147.05555555556	0.944444444444343
112	1485	1655.47826086957	-170.478260869565
113	1526	1655.47826086957	-129.478260869565
114	962	1147.05555555556	-185.055555555556
115	78	155.9375	-77.9375
116	0	155.9375	-155.9375
117	1184	1655.47826086957	-471.478260869565
118	1671	2034.48484848485	-363.484848484848
119	2142	2124.125	17.875
120	1015	1430.58823529412	-415.588235294118
121	778	783.777777777778	-5.77777777777783
122	1856	2034.48484848485	-178.484848484848
123	1056	1147.05555555556	-91.0555555555557
124	2297	2034.48484848485	262.515151515152
125	731	783.777777777778	-52.7777777777778
126	285	155.9375	129.0625
127	1872	1655.47826086957	216.521739130435
128	1181	1430.58823529412	-249.588235294118
129	1725	2124.125	-399.125
130	256	155.9375	100.0625
131	98	155.9375	-57.9375
132	1435	1655.47826086957	-220.478260869565
133	41	155.9375	-114.9375
134	1930	1655.47826086957	274.521739130435
135	42	155.9375	-113.9375
136	528	783.777777777778	-255.777777777778
137	0	155.9375	-155.9375
138	1121	1430.58823529412	-309.588235294118
139	1305	1147.05555555556	157.944444444444
140	81	155.9375	-74.9375
141	262	155.9375	106.0625
142	1100	1147.05555555556	-47.0555555555557
143	1290	1430.58823529412	-140.588235294118
144	1248	1147.05555555556	100.944444444444

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1845 & 1655.47826086957 & 189.521739130435 \tabularnewline
2 & 1796 & 1655.47826086957 & 140.521739130435 \tabularnewline
3 & 192 & 155.9375 & 36.0625 \tabularnewline
4 & 2444 & 2034.48484848485 & 409.515151515152 \tabularnewline
5 & 3567 & 3371.88888888889 & 195.111111111111 \tabularnewline
6 & 6917 & 3371.88888888889 & 3545.11111111111 \tabularnewline
7 & 1840 & 2034.48484848485 & -194.484848484848 \tabularnewline
8 & 1740 & 2034.48484848485 & -294.484848484848 \tabularnewline
9 & 2078 & 2034.48484848485 & 43.5151515151515 \tabularnewline
10 & 3118 & 3371.88888888889 & -253.888888888889 \tabularnewline
11 & 1946 & 1430.58823529412 & 515.411764705882 \tabularnewline
12 & 2370 & 2034.48484848485 & 335.515151515152 \tabularnewline
13 & 1944 & 2034.48484848485 & -90.4848484848485 \tabularnewline
14 & 3198 & 2481.90909090909 & 716.090909090909 \tabularnewline
15 & 1491 & 1655.47826086957 & -164.478260869565 \tabularnewline
16 & 1573 & 1655.47826086957 & -82.4782608695652 \tabularnewline
17 & 1807 & 2034.48484848485 & -227.484848484848 \tabularnewline
18 & 1309 & 1147.05555555556 & 161.944444444444 \tabularnewline
19 & 2820 & 2034.48484848485 & 785.515151515152 \tabularnewline
20 & 776 & 783.777777777778 & -7.77777777777783 \tabularnewline
21 & 1162 & 1147.05555555556 & 14.9444444444443 \tabularnewline
22 & 2818 & 2481.90909090909 & 336.090909090909 \tabularnewline
23 & 1770 & 1655.47826086957 & 114.521739130435 \tabularnewline
24 & 2315 & 2124.125 & 190.875 \tabularnewline
25 & 1994 & 1655.47826086957 & 338.521739130435 \tabularnewline
26 & 1806 & 2034.48484848485 & -228.484848484848 \tabularnewline
27 & 2152 & 2034.48484848485 & 117.515151515152 \tabularnewline
28 & 1457 & 2034.48484848485 & -577.484848484848 \tabularnewline
29 & 3000 & 3371.88888888889 & -371.888888888889 \tabularnewline
30 & 2236 & 2034.48484848485 & 201.515151515152 \tabularnewline
31 & 1685 & 2034.48484848485 & -349.484848484848 \tabularnewline
32 & 1626 & 2034.48484848485 & -408.484848484848 \tabularnewline
33 & 2257 & 1430.58823529412 & 826.411764705882 \tabularnewline
34 & 3373 & 3371.88888888889 & 1.11111111111131 \tabularnewline
35 & 2571 & 2481.90909090909 & 89.090909090909 \tabularnewline
36 & 1 & 155.9375 & -154.9375 \tabularnewline
37 & 2142 & 2481.90909090909 & -339.909090909091 \tabularnewline
38 & 1878 & 1430.58823529412 & 447.411764705882 \tabularnewline
39 & 2190 & 2124.125 & 65.875 \tabularnewline
40 & 2186 & 2481.90909090909 & -295.909090909091 \tabularnewline
41 & 2533 & 3371.88888888889 & -838.888888888889 \tabularnewline
42 & 1823 & 2124.125 & -301.125 \tabularnewline
43 & 1095 & 1147.05555555556 & -52.0555555555557 \tabularnewline
44 & 2162 & 2034.48484848485 & 127.515151515152 \tabularnewline
45 & 1365 & 2034.48484848485 & -669.484848484848 \tabularnewline
46 & 1245 & 1655.47826086957 & -410.478260869565 \tabularnewline
47 & 756 & 783.777777777778 & -27.7777777777778 \tabularnewline
48 & 2417 & 2481.90909090909 & -64.909090909091 \tabularnewline
49 & 2327 & 2481.90909090909 & -154.909090909091 \tabularnewline
50 & 2786 & 2034.48484848485 & 751.515151515152 \tabularnewline
51 & 658 & 783.777777777778 & -125.777777777778 \tabularnewline
52 & 2012 & 2034.48484848485 & -22.4848484848485 \tabularnewline
53 & 2616 & 3371.88888888889 & -755.888888888889 \tabularnewline
54 & 2071 & 2481.90909090909 & -410.909090909091 \tabularnewline
55 & 1911 & 2034.48484848485 & -123.484848484848 \tabularnewline
56 & 1775 & 1655.47826086957 & 119.521739130435 \tabularnewline
57 & 1919 & 1655.47826086957 & 263.521739130435 \tabularnewline
58 & 1047 & 1147.05555555556 & -100.055555555556 \tabularnewline
59 & 1190 & 2034.48484848485 & -844.484848484848 \tabularnewline
60 & 2890 & 2124.125 & 765.875 \tabularnewline
61 & 1836 & 2124.125 & -288.125 \tabularnewline
62 & 2254 & 1655.47826086957 & 598.521739130435 \tabularnewline
63 & 1392 & 1430.58823529412 & -38.5882352941176 \tabularnewline
64 & 1326 & 1430.58823529412 & -104.588235294118 \tabularnewline
65 & 1317 & 1655.47826086957 & -338.478260869565 \tabularnewline
66 & 1525 & 1430.58823529412 & 94.4117647058824 \tabularnewline
67 & 2335 & 2034.48484848485 & 300.515151515152 \tabularnewline
68 & 2897 & 2034.48484848485 & 862.515151515152 \tabularnewline
69 & 1118 & 1430.58823529412 & -312.588235294118 \tabularnewline
70 & 340 & 155.9375 & 184.0625 \tabularnewline
71 & 2977 & 3371.88888888889 & -394.888888888889 \tabularnewline
72 & 1452 & 1655.47826086957 & -203.478260869565 \tabularnewline
73 & 1550 & 2034.48484848485 & -484.484848484848 \tabularnewline
74 & 1685 & 1430.58823529412 & 254.411764705882 \tabularnewline
75 & 2728 & 2034.48484848485 & 693.515151515152 \tabularnewline
76 & 1574 & 1147.05555555556 & 426.944444444444 \tabularnewline
77 & 2413 & 2034.48484848485 & 378.515151515152 \tabularnewline
78 & 2563 & 2034.48484848485 & 528.515151515152 \tabularnewline
79 & 1079 & 1147.05555555556 & -68.0555555555557 \tabularnewline
80 & 1235 & 1147.05555555556 & 87.9444444444443 \tabularnewline
81 & 980 & 1147.05555555556 & -167.055555555556 \tabularnewline
82 & 2246 & 3371.88888888889 & -1125.88888888889 \tabularnewline
83 & 1076 & 783.777777777778 & 292.222222222222 \tabularnewline
84 & 1637 & 1655.47826086957 & -18.4782608695652 \tabularnewline
85 & 1208 & 1147.05555555556 & 60.9444444444443 \tabularnewline
86 & 1865 & 1655.47826086957 & 209.521739130435 \tabularnewline
87 & 2726 & 2481.90909090909 & 244.090909090909 \tabularnewline
88 & 1208 & 1430.58823529412 & -222.588235294118 \tabularnewline
89 & 1419 & 1430.58823529412 & -11.5882352941176 \tabularnewline
90 & 1609 & 1655.47826086957 & -46.4782608695652 \tabularnewline
91 & 1864 & 1655.47826086957 & 208.521739130435 \tabularnewline
92 & 2412 & 2034.48484848485 & 377.515151515152 \tabularnewline
93 & 1238 & 1655.47826086957 & -417.478260869565 \tabularnewline
94 & 1463 & 1430.58823529412 & 32.4117647058824 \tabularnewline
95 & 973 & 783.777777777778 & 189.222222222222 \tabularnewline
96 & 2319 & 2481.90909090909 & -162.909090909091 \tabularnewline
97 & 1890 & 2034.48484848485 & -144.484848484848 \tabularnewline
98 & 223 & 155.9375 & 67.0625 \tabularnewline
99 & 2526 & 2481.90909090909 & 44.090909090909 \tabularnewline
100 & 2072 & 2124.125 & -52.125 \tabularnewline
101 & 778 & 783.777777777778 & -5.77777777777783 \tabularnewline
102 & 1194 & 1147.05555555556 & 46.9444444444443 \tabularnewline
103 & 1424 & 2034.48484848485 & -610.484848484848 \tabularnewline
104 & 1328 & 1430.58823529412 & -102.588235294118 \tabularnewline
105 & 839 & 1147.05555555556 & -308.055555555556 \tabularnewline
106 & 596 & 155.9375 & 440.0625 \tabularnewline
107 & 1671 & 2034.48484848485 & -363.484848484848 \tabularnewline
108 & 1168 & 1430.58823529412 & -262.588235294118 \tabularnewline
109 & 0 & 155.9375 & -155.9375 \tabularnewline
110 & 1106 & 1147.05555555556 & -41.0555555555557 \tabularnewline
111 & 1148 & 1147.05555555556 & 0.944444444444343 \tabularnewline
112 & 1485 & 1655.47826086957 & -170.478260869565 \tabularnewline
113 & 1526 & 1655.47826086957 & -129.478260869565 \tabularnewline
114 & 962 & 1147.05555555556 & -185.055555555556 \tabularnewline
115 & 78 & 155.9375 & -77.9375 \tabularnewline
116 & 0 & 155.9375 & -155.9375 \tabularnewline
117 & 1184 & 1655.47826086957 & -471.478260869565 \tabularnewline
118 & 1671 & 2034.48484848485 & -363.484848484848 \tabularnewline
119 & 2142 & 2124.125 & 17.875 \tabularnewline
120 & 1015 & 1430.58823529412 & -415.588235294118 \tabularnewline
121 & 778 & 783.777777777778 & -5.77777777777783 \tabularnewline
122 & 1856 & 2034.48484848485 & -178.484848484848 \tabularnewline
123 & 1056 & 1147.05555555556 & -91.0555555555557 \tabularnewline
124 & 2297 & 2034.48484848485 & 262.515151515152 \tabularnewline
125 & 731 & 783.777777777778 & -52.7777777777778 \tabularnewline
126 & 285 & 155.9375 & 129.0625 \tabularnewline
127 & 1872 & 1655.47826086957 & 216.521739130435 \tabularnewline
128 & 1181 & 1430.58823529412 & -249.588235294118 \tabularnewline
129 & 1725 & 2124.125 & -399.125 \tabularnewline
130 & 256 & 155.9375 & 100.0625 \tabularnewline
131 & 98 & 155.9375 & -57.9375 \tabularnewline
132 & 1435 & 1655.47826086957 & -220.478260869565 \tabularnewline
133 & 41 & 155.9375 & -114.9375 \tabularnewline
134 & 1930 & 1655.47826086957 & 274.521739130435 \tabularnewline
135 & 42 & 155.9375 & -113.9375 \tabularnewline
136 & 528 & 783.777777777778 & -255.777777777778 \tabularnewline
137 & 0 & 155.9375 & -155.9375 \tabularnewline
138 & 1121 & 1430.58823529412 & -309.588235294118 \tabularnewline
139 & 1305 & 1147.05555555556 & 157.944444444444 \tabularnewline
140 & 81 & 155.9375 & -74.9375 \tabularnewline
141 & 262 & 155.9375 & 106.0625 \tabularnewline
142 & 1100 & 1147.05555555556 & -47.0555555555557 \tabularnewline
143 & 1290 & 1430.58823529412 & -140.588235294118 \tabularnewline
144 & 1248 & 1147.05555555556 & 100.944444444444 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159463&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]1845[/C][C]1655.47826086957[/C][C]189.521739130435[/C][/ROW]
[ROW][C]2[/C][C]1796[/C][C]1655.47826086957[/C][C]140.521739130435[/C][/ROW]
[ROW][C]3[/C][C]192[/C][C]155.9375[/C][C]36.0625[/C][/ROW]
[ROW][C]4[/C][C]2444[/C][C]2034.48484848485[/C][C]409.515151515152[/C][/ROW]
[ROW][C]5[/C][C]3567[/C][C]3371.88888888889[/C][C]195.111111111111[/C][/ROW]
[ROW][C]6[/C][C]6917[/C][C]3371.88888888889[/C][C]3545.11111111111[/C][/ROW]
[ROW][C]7[/C][C]1840[/C][C]2034.48484848485[/C][C]-194.484848484848[/C][/ROW]
[ROW][C]8[/C][C]1740[/C][C]2034.48484848485[/C][C]-294.484848484848[/C][/ROW]
[ROW][C]9[/C][C]2078[/C][C]2034.48484848485[/C][C]43.5151515151515[/C][/ROW]
[ROW][C]10[/C][C]3118[/C][C]3371.88888888889[/C][C]-253.888888888889[/C][/ROW]
[ROW][C]11[/C][C]1946[/C][C]1430.58823529412[/C][C]515.411764705882[/C][/ROW]
[ROW][C]12[/C][C]2370[/C][C]2034.48484848485[/C][C]335.515151515152[/C][/ROW]
[ROW][C]13[/C][C]1944[/C][C]2034.48484848485[/C][C]-90.4848484848485[/C][/ROW]
[ROW][C]14[/C][C]3198[/C][C]2481.90909090909[/C][C]716.090909090909[/C][/ROW]
[ROW][C]15[/C][C]1491[/C][C]1655.47826086957[/C][C]-164.478260869565[/C][/ROW]
[ROW][C]16[/C][C]1573[/C][C]1655.47826086957[/C][C]-82.4782608695652[/C][/ROW]
[ROW][C]17[/C][C]1807[/C][C]2034.48484848485[/C][C]-227.484848484848[/C][/ROW]
[ROW][C]18[/C][C]1309[/C][C]1147.05555555556[/C][C]161.944444444444[/C][/ROW]
[ROW][C]19[/C][C]2820[/C][C]2034.48484848485[/C][C]785.515151515152[/C][/ROW]
[ROW][C]20[/C][C]776[/C][C]783.777777777778[/C][C]-7.77777777777783[/C][/ROW]
[ROW][C]21[/C][C]1162[/C][C]1147.05555555556[/C][C]14.9444444444443[/C][/ROW]
[ROW][C]22[/C][C]2818[/C][C]2481.90909090909[/C][C]336.090909090909[/C][/ROW]
[ROW][C]23[/C][C]1770[/C][C]1655.47826086957[/C][C]114.521739130435[/C][/ROW]
[ROW][C]24[/C][C]2315[/C][C]2124.125[/C][C]190.875[/C][/ROW]
[ROW][C]25[/C][C]1994[/C][C]1655.47826086957[/C][C]338.521739130435[/C][/ROW]
[ROW][C]26[/C][C]1806[/C][C]2034.48484848485[/C][C]-228.484848484848[/C][/ROW]
[ROW][C]27[/C][C]2152[/C][C]2034.48484848485[/C][C]117.515151515152[/C][/ROW]
[ROW][C]28[/C][C]1457[/C][C]2034.48484848485[/C][C]-577.484848484848[/C][/ROW]
[ROW][C]29[/C][C]3000[/C][C]3371.88888888889[/C][C]-371.888888888889[/C][/ROW]
[ROW][C]30[/C][C]2236[/C][C]2034.48484848485[/C][C]201.515151515152[/C][/ROW]
[ROW][C]31[/C][C]1685[/C][C]2034.48484848485[/C][C]-349.484848484848[/C][/ROW]
[ROW][C]32[/C][C]1626[/C][C]2034.48484848485[/C][C]-408.484848484848[/C][/ROW]
[ROW][C]33[/C][C]2257[/C][C]1430.58823529412[/C][C]826.411764705882[/C][/ROW]
[ROW][C]34[/C][C]3373[/C][C]3371.88888888889[/C][C]1.11111111111131[/C][/ROW]
[ROW][C]35[/C][C]2571[/C][C]2481.90909090909[/C][C]89.090909090909[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]155.9375[/C][C]-154.9375[/C][/ROW]
[ROW][C]37[/C][C]2142[/C][C]2481.90909090909[/C][C]-339.909090909091[/C][/ROW]
[ROW][C]38[/C][C]1878[/C][C]1430.58823529412[/C][C]447.411764705882[/C][/ROW]
[ROW][C]39[/C][C]2190[/C][C]2124.125[/C][C]65.875[/C][/ROW]
[ROW][C]40[/C][C]2186[/C][C]2481.90909090909[/C][C]-295.909090909091[/C][/ROW]
[ROW][C]41[/C][C]2533[/C][C]3371.88888888889[/C][C]-838.888888888889[/C][/ROW]
[ROW][C]42[/C][C]1823[/C][C]2124.125[/C][C]-301.125[/C][/ROW]
[ROW][C]43[/C][C]1095[/C][C]1147.05555555556[/C][C]-52.0555555555557[/C][/ROW]
[ROW][C]44[/C][C]2162[/C][C]2034.48484848485[/C][C]127.515151515152[/C][/ROW]
[ROW][C]45[/C][C]1365[/C][C]2034.48484848485[/C][C]-669.484848484848[/C][/ROW]
[ROW][C]46[/C][C]1245[/C][C]1655.47826086957[/C][C]-410.478260869565[/C][/ROW]
[ROW][C]47[/C][C]756[/C][C]783.777777777778[/C][C]-27.7777777777778[/C][/ROW]
[ROW][C]48[/C][C]2417[/C][C]2481.90909090909[/C][C]-64.909090909091[/C][/ROW]
[ROW][C]49[/C][C]2327[/C][C]2481.90909090909[/C][C]-154.909090909091[/C][/ROW]
[ROW][C]50[/C][C]2786[/C][C]2034.48484848485[/C][C]751.515151515152[/C][/ROW]
[ROW][C]51[/C][C]658[/C][C]783.777777777778[/C][C]-125.777777777778[/C][/ROW]
[ROW][C]52[/C][C]2012[/C][C]2034.48484848485[/C][C]-22.4848484848485[/C][/ROW]
[ROW][C]53[/C][C]2616[/C][C]3371.88888888889[/C][C]-755.888888888889[/C][/ROW]
[ROW][C]54[/C][C]2071[/C][C]2481.90909090909[/C][C]-410.909090909091[/C][/ROW]
[ROW][C]55[/C][C]1911[/C][C]2034.48484848485[/C][C]-123.484848484848[/C][/ROW]
[ROW][C]56[/C][C]1775[/C][C]1655.47826086957[/C][C]119.521739130435[/C][/ROW]
[ROW][C]57[/C][C]1919[/C][C]1655.47826086957[/C][C]263.521739130435[/C][/ROW]
[ROW][C]58[/C][C]1047[/C][C]1147.05555555556[/C][C]-100.055555555556[/C][/ROW]
[ROW][C]59[/C][C]1190[/C][C]2034.48484848485[/C][C]-844.484848484848[/C][/ROW]
[ROW][C]60[/C][C]2890[/C][C]2124.125[/C][C]765.875[/C][/ROW]
[ROW][C]61[/C][C]1836[/C][C]2124.125[/C][C]-288.125[/C][/ROW]
[ROW][C]62[/C][C]2254[/C][C]1655.47826086957[/C][C]598.521739130435[/C][/ROW]
[ROW][C]63[/C][C]1392[/C][C]1430.58823529412[/C][C]-38.5882352941176[/C][/ROW]
[ROW][C]64[/C][C]1326[/C][C]1430.58823529412[/C][C]-104.588235294118[/C][/ROW]
[ROW][C]65[/C][C]1317[/C][C]1655.47826086957[/C][C]-338.478260869565[/C][/ROW]
[ROW][C]66[/C][C]1525[/C][C]1430.58823529412[/C][C]94.4117647058824[/C][/ROW]
[ROW][C]67[/C][C]2335[/C][C]2034.48484848485[/C][C]300.515151515152[/C][/ROW]
[ROW][C]68[/C][C]2897[/C][C]2034.48484848485[/C][C]862.515151515152[/C][/ROW]
[ROW][C]69[/C][C]1118[/C][C]1430.58823529412[/C][C]-312.588235294118[/C][/ROW]
[ROW][C]70[/C][C]340[/C][C]155.9375[/C][C]184.0625[/C][/ROW]
[ROW][C]71[/C][C]2977[/C][C]3371.88888888889[/C][C]-394.888888888889[/C][/ROW]
[ROW][C]72[/C][C]1452[/C][C]1655.47826086957[/C][C]-203.478260869565[/C][/ROW]
[ROW][C]73[/C][C]1550[/C][C]2034.48484848485[/C][C]-484.484848484848[/C][/ROW]
[ROW][C]74[/C][C]1685[/C][C]1430.58823529412[/C][C]254.411764705882[/C][/ROW]
[ROW][C]75[/C][C]2728[/C][C]2034.48484848485[/C][C]693.515151515152[/C][/ROW]
[ROW][C]76[/C][C]1574[/C][C]1147.05555555556[/C][C]426.944444444444[/C][/ROW]
[ROW][C]77[/C][C]2413[/C][C]2034.48484848485[/C][C]378.515151515152[/C][/ROW]
[ROW][C]78[/C][C]2563[/C][C]2034.48484848485[/C][C]528.515151515152[/C][/ROW]
[ROW][C]79[/C][C]1079[/C][C]1147.05555555556[/C][C]-68.0555555555557[/C][/ROW]
[ROW][C]80[/C][C]1235[/C][C]1147.05555555556[/C][C]87.9444444444443[/C][/ROW]
[ROW][C]81[/C][C]980[/C][C]1147.05555555556[/C][C]-167.055555555556[/C][/ROW]
[ROW][C]82[/C][C]2246[/C][C]3371.88888888889[/C][C]-1125.88888888889[/C][/ROW]
[ROW][C]83[/C][C]1076[/C][C]783.777777777778[/C][C]292.222222222222[/C][/ROW]
[ROW][C]84[/C][C]1637[/C][C]1655.47826086957[/C][C]-18.4782608695652[/C][/ROW]
[ROW][C]85[/C][C]1208[/C][C]1147.05555555556[/C][C]60.9444444444443[/C][/ROW]
[ROW][C]86[/C][C]1865[/C][C]1655.47826086957[/C][C]209.521739130435[/C][/ROW]
[ROW][C]87[/C][C]2726[/C][C]2481.90909090909[/C][C]244.090909090909[/C][/ROW]
[ROW][C]88[/C][C]1208[/C][C]1430.58823529412[/C][C]-222.588235294118[/C][/ROW]
[ROW][C]89[/C][C]1419[/C][C]1430.58823529412[/C][C]-11.5882352941176[/C][/ROW]
[ROW][C]90[/C][C]1609[/C][C]1655.47826086957[/C][C]-46.4782608695652[/C][/ROW]
[ROW][C]91[/C][C]1864[/C][C]1655.47826086957[/C][C]208.521739130435[/C][/ROW]
[ROW][C]92[/C][C]2412[/C][C]2034.48484848485[/C][C]377.515151515152[/C][/ROW]
[ROW][C]93[/C][C]1238[/C][C]1655.47826086957[/C][C]-417.478260869565[/C][/ROW]
[ROW][C]94[/C][C]1463[/C][C]1430.58823529412[/C][C]32.4117647058824[/C][/ROW]
[ROW][C]95[/C][C]973[/C][C]783.777777777778[/C][C]189.222222222222[/C][/ROW]
[ROW][C]96[/C][C]2319[/C][C]2481.90909090909[/C][C]-162.909090909091[/C][/ROW]
[ROW][C]97[/C][C]1890[/C][C]2034.48484848485[/C][C]-144.484848484848[/C][/ROW]
[ROW][C]98[/C][C]223[/C][C]155.9375[/C][C]67.0625[/C][/ROW]
[ROW][C]99[/C][C]2526[/C][C]2481.90909090909[/C][C]44.090909090909[/C][/ROW]
[ROW][C]100[/C][C]2072[/C][C]2124.125[/C][C]-52.125[/C][/ROW]
[ROW][C]101[/C][C]778[/C][C]783.777777777778[/C][C]-5.77777777777783[/C][/ROW]
[ROW][C]102[/C][C]1194[/C][C]1147.05555555556[/C][C]46.9444444444443[/C][/ROW]
[ROW][C]103[/C][C]1424[/C][C]2034.48484848485[/C][C]-610.484848484848[/C][/ROW]
[ROW][C]104[/C][C]1328[/C][C]1430.58823529412[/C][C]-102.588235294118[/C][/ROW]
[ROW][C]105[/C][C]839[/C][C]1147.05555555556[/C][C]-308.055555555556[/C][/ROW]
[ROW][C]106[/C][C]596[/C][C]155.9375[/C][C]440.0625[/C][/ROW]
[ROW][C]107[/C][C]1671[/C][C]2034.48484848485[/C][C]-363.484848484848[/C][/ROW]
[ROW][C]108[/C][C]1168[/C][C]1430.58823529412[/C][C]-262.588235294118[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]155.9375[/C][C]-155.9375[/C][/ROW]
[ROW][C]110[/C][C]1106[/C][C]1147.05555555556[/C][C]-41.0555555555557[/C][/ROW]
[ROW][C]111[/C][C]1148[/C][C]1147.05555555556[/C][C]0.944444444444343[/C][/ROW]
[ROW][C]112[/C][C]1485[/C][C]1655.47826086957[/C][C]-170.478260869565[/C][/ROW]
[ROW][C]113[/C][C]1526[/C][C]1655.47826086957[/C][C]-129.478260869565[/C][/ROW]
[ROW][C]114[/C][C]962[/C][C]1147.05555555556[/C][C]-185.055555555556[/C][/ROW]
[ROW][C]115[/C][C]78[/C][C]155.9375[/C][C]-77.9375[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]155.9375[/C][C]-155.9375[/C][/ROW]
[ROW][C]117[/C][C]1184[/C][C]1655.47826086957[/C][C]-471.478260869565[/C][/ROW]
[ROW][C]118[/C][C]1671[/C][C]2034.48484848485[/C][C]-363.484848484848[/C][/ROW]
[ROW][C]119[/C][C]2142[/C][C]2124.125[/C][C]17.875[/C][/ROW]
[ROW][C]120[/C][C]1015[/C][C]1430.58823529412[/C][C]-415.588235294118[/C][/ROW]
[ROW][C]121[/C][C]778[/C][C]783.777777777778[/C][C]-5.77777777777783[/C][/ROW]
[ROW][C]122[/C][C]1856[/C][C]2034.48484848485[/C][C]-178.484848484848[/C][/ROW]
[ROW][C]123[/C][C]1056[/C][C]1147.05555555556[/C][C]-91.0555555555557[/C][/ROW]
[ROW][C]124[/C][C]2297[/C][C]2034.48484848485[/C][C]262.515151515152[/C][/ROW]
[ROW][C]125[/C][C]731[/C][C]783.777777777778[/C][C]-52.7777777777778[/C][/ROW]
[ROW][C]126[/C][C]285[/C][C]155.9375[/C][C]129.0625[/C][/ROW]
[ROW][C]127[/C][C]1872[/C][C]1655.47826086957[/C][C]216.521739130435[/C][/ROW]
[ROW][C]128[/C][C]1181[/C][C]1430.58823529412[/C][C]-249.588235294118[/C][/ROW]
[ROW][C]129[/C][C]1725[/C][C]2124.125[/C][C]-399.125[/C][/ROW]
[ROW][C]130[/C][C]256[/C][C]155.9375[/C][C]100.0625[/C][/ROW]
[ROW][C]131[/C][C]98[/C][C]155.9375[/C][C]-57.9375[/C][/ROW]
[ROW][C]132[/C][C]1435[/C][C]1655.47826086957[/C][C]-220.478260869565[/C][/ROW]
[ROW][C]133[/C][C]41[/C][C]155.9375[/C][C]-114.9375[/C][/ROW]
[ROW][C]134[/C][C]1930[/C][C]1655.47826086957[/C][C]274.521739130435[/C][/ROW]
[ROW][C]135[/C][C]42[/C][C]155.9375[/C][C]-113.9375[/C][/ROW]
[ROW][C]136[/C][C]528[/C][C]783.777777777778[/C][C]-255.777777777778[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]155.9375[/C][C]-155.9375[/C][/ROW]
[ROW][C]138[/C][C]1121[/C][C]1430.58823529412[/C][C]-309.588235294118[/C][/ROW]
[ROW][C]139[/C][C]1305[/C][C]1147.05555555556[/C][C]157.944444444444[/C][/ROW]
[ROW][C]140[/C][C]81[/C][C]155.9375[/C][C]-74.9375[/C][/ROW]
[ROW][C]141[/C][C]262[/C][C]155.9375[/C][C]106.0625[/C][/ROW]
[ROW][C]142[/C][C]1100[/C][C]1147.05555555556[/C][C]-47.0555555555557[/C][/ROW]
[ROW][C]143[/C][C]1290[/C][C]1430.58823529412[/C][C]-140.588235294118[/C][/ROW]
[ROW][C]144[/C][C]1248[/C][C]1147.05555555556[/C][C]100.944444444444[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159463&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159463&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	1845	1655.47826086957	189.521739130435
2	1796	1655.47826086957	140.521739130435
3	192	155.9375	36.0625
4	2444	2034.48484848485	409.515151515152
5	3567	3371.88888888889	195.111111111111
6	6917	3371.88888888889	3545.11111111111
7	1840	2034.48484848485	-194.484848484848
8	1740	2034.48484848485	-294.484848484848
9	2078	2034.48484848485	43.5151515151515
10	3118	3371.88888888889	-253.888888888889
11	1946	1430.58823529412	515.411764705882
12	2370	2034.48484848485	335.515151515152
13	1944	2034.48484848485	-90.4848484848485
14	3198	2481.90909090909	716.090909090909
15	1491	1655.47826086957	-164.478260869565
16	1573	1655.47826086957	-82.4782608695652
17	1807	2034.48484848485	-227.484848484848
18	1309	1147.05555555556	161.944444444444
19	2820	2034.48484848485	785.515151515152
20	776	783.777777777778	-7.77777777777783
21	1162	1147.05555555556	14.9444444444443
22	2818	2481.90909090909	336.090909090909
23	1770	1655.47826086957	114.521739130435
24	2315	2124.125	190.875
25	1994	1655.47826086957	338.521739130435
26	1806	2034.48484848485	-228.484848484848
27	2152	2034.48484848485	117.515151515152
28	1457	2034.48484848485	-577.484848484848
29	3000	3371.88888888889	-371.888888888889
30	2236	2034.48484848485	201.515151515152
31	1685	2034.48484848485	-349.484848484848
32	1626	2034.48484848485	-408.484848484848
33	2257	1430.58823529412	826.411764705882
34	3373	3371.88888888889	1.11111111111131
35	2571	2481.90909090909	89.090909090909
36	1	155.9375	-154.9375
37	2142	2481.90909090909	-339.909090909091
38	1878	1430.58823529412	447.411764705882
39	2190	2124.125	65.875
40	2186	2481.90909090909	-295.909090909091
41	2533	3371.88888888889	-838.888888888889
42	1823	2124.125	-301.125
43	1095	1147.05555555556	-52.0555555555557
44	2162	2034.48484848485	127.515151515152
45	1365	2034.48484848485	-669.484848484848
46	1245	1655.47826086957	-410.478260869565
47	756	783.777777777778	-27.7777777777778
48	2417	2481.90909090909	-64.909090909091
49	2327	2481.90909090909	-154.909090909091
50	2786	2034.48484848485	751.515151515152
51	658	783.777777777778	-125.777777777778
52	2012	2034.48484848485	-22.4848484848485
53	2616	3371.88888888889	-755.888888888889
54	2071	2481.90909090909	-410.909090909091
55	1911	2034.48484848485	-123.484848484848
56	1775	1655.47826086957	119.521739130435
57	1919	1655.47826086957	263.521739130435
58	1047	1147.05555555556	-100.055555555556
59	1190	2034.48484848485	-844.484848484848
60	2890	2124.125	765.875
61	1836	2124.125	-288.125
62	2254	1655.47826086957	598.521739130435
63	1392	1430.58823529412	-38.5882352941176
64	1326	1430.58823529412	-104.588235294118
65	1317	1655.47826086957	-338.478260869565
66	1525	1430.58823529412	94.4117647058824
67	2335	2034.48484848485	300.515151515152
68	2897	2034.48484848485	862.515151515152
69	1118	1430.58823529412	-312.588235294118
70	340	155.9375	184.0625
71	2977	3371.88888888889	-394.888888888889
72	1452	1655.47826086957	-203.478260869565
73	1550	2034.48484848485	-484.484848484848
74	1685	1430.58823529412	254.411764705882
75	2728	2034.48484848485	693.515151515152
76	1574	1147.05555555556	426.944444444444
77	2413	2034.48484848485	378.515151515152
78	2563	2034.48484848485	528.515151515152
79	1079	1147.05555555556	-68.0555555555557
80	1235	1147.05555555556	87.9444444444443
81	980	1147.05555555556	-167.055555555556
82	2246	3371.88888888889	-1125.88888888889
83	1076	783.777777777778	292.222222222222
84	1637	1655.47826086957	-18.4782608695652
85	1208	1147.05555555556	60.9444444444443
86	1865	1655.47826086957	209.521739130435
87	2726	2481.90909090909	244.090909090909
88	1208	1430.58823529412	-222.588235294118
89	1419	1430.58823529412	-11.5882352941176
90	1609	1655.47826086957	-46.4782608695652
91	1864	1655.47826086957	208.521739130435
92	2412	2034.48484848485	377.515151515152
93	1238	1655.47826086957	-417.478260869565
94	1463	1430.58823529412	32.4117647058824
95	973	783.777777777778	189.222222222222
96	2319	2481.90909090909	-162.909090909091
97	1890	2034.48484848485	-144.484848484848
98	223	155.9375	67.0625
99	2526	2481.90909090909	44.090909090909
100	2072	2124.125	-52.125
101	778	783.777777777778	-5.77777777777783
102	1194	1147.05555555556	46.9444444444443
103	1424	2034.48484848485	-610.484848484848
104	1328	1430.58823529412	-102.588235294118
105	839	1147.05555555556	-308.055555555556
106	596	155.9375	440.0625
107	1671	2034.48484848485	-363.484848484848
108	1168	1430.58823529412	-262.588235294118
109	0	155.9375	-155.9375
110	1106	1147.05555555556	-41.0555555555557
111	1148	1147.05555555556	0.944444444444343
112	1485	1655.47826086957	-170.478260869565
113	1526	1655.47826086957	-129.478260869565
114	962	1147.05555555556	-185.055555555556
115	78	155.9375	-77.9375
116	0	155.9375	-155.9375
117	1184	1655.47826086957	-471.478260869565
118	1671	2034.48484848485	-363.484848484848
119	2142	2124.125	17.875
120	1015	1430.58823529412	-415.588235294118
121	778	783.777777777778	-5.77777777777783
122	1856	2034.48484848485	-178.484848484848
123	1056	1147.05555555556	-91.0555555555557
124	2297	2034.48484848485	262.515151515152
125	731	783.777777777778	-52.7777777777778
126	285	155.9375	129.0625
127	1872	1655.47826086957	216.521739130435
128	1181	1430.58823529412	-249.588235294118
129	1725	2124.125	-399.125
130	256	155.9375	100.0625
131	98	155.9375	-57.9375
132	1435	1655.47826086957	-220.478260869565
133	41	155.9375	-114.9375
134	1930	1655.47826086957	274.521739130435
135	42	155.9375	-113.9375
136	528	783.777777777778	-255.777777777778
137	0	155.9375	-155.9375
138	1121	1430.58823529412	-309.588235294118
139	1305	1147.05555555556	157.944444444444
140	81	155.9375	-74.9375
141	262	155.9375	106.0625
142	1100	1147.05555555556	-47.0555555555557
143	1290	1430.58823529412	-140.588235294118
144	1248	1147.05555555556	100.944444444444

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