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Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Thu, 22 Dec 2011 09:08:50 -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/22/t1324562988b895eax8oxo6z9m.htm/, Retrieved Fri, 03 May 2024 07:41:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159482, Retrieved Fri, 03 May 2024 07:41:59 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

107

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] [baac05fe722f73c103cc2d713fa5bd78]
- R P         [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-22 14:08:50] [02062aee8449a97abc7c7e765b9155b5] [Current]
-               [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	'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.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=159482&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]'AstonUniversity' @ aston.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=159482&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159482&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	'AstonUniversity' @ aston.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	0.9088
R-squared	0.826
RMSE	33707.8647

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9088 \tabularnewline
R-squared & 0.826 \tabularnewline
RMSE & 33707.8647 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159482&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9088[/C][/ROW]
[ROW][C]R-squared[/C][C]0.826[/C][/ROW]
[ROW][C]RMSE[/C][C]33707.8647[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159482&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159482&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	0.9088
R-squared	0.826
RMSE	33707.8647

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	162687	171507.7	-8820.70000000001
2	201906	185229.2	16676.8
3	7215	13813.1666666667	-6598.16666666667
4	146367	171507.7	-25140.7
5	257045	320169.857142857	-63124.8571428572
6	524450	320169.857142857	204280.142857143
7	188294	185229.2	3064.79999999999
8	195674	185229.2	10444.8
9	177020	171507.7	5512.29999999999
10	330194	320169.857142857	10024.1428571428
11	121844	171507.7	-49663.7
12	203938	237015.714285714	-33077.7142857143
13	113213	171507.7	-58294.7
14	220751	320169.857142857	-99418.8571428572
15	172905	143225.263157895	29679.7368421053
16	156326	143225.263157895	13100.7368421053
17	145178	185229.2	-40051.2
18	89171	114046.5	-24875.5
19	172624	171507.7	1116.29999999999
20	39790	68746.5555555556	-28956.5555555556
21	87927	68746.5555555556	19180.4444444444
22	241285	237015.714285714	4269.28571428571
23	198587	171507.7	27079.3
24	146946	171507.7	-24561.7
25	159763	171507.7	-11744.7
26	207078	171507.7	35570.3
27	212394	185229.2	27164.8
28	201536	185229.2	16306.8
29	394662	320169.857142857	74492.1428571428
30	217892	237015.714285714	-19123.7142857143
31	182286	171507.7	10778.3
32	181740	185229.2	-3489.20000000001
33	137978	171507.7	-33529.7
34	255929	320169.857142857	-64240.8571428572
35	236489	237015.714285714	-526.71428571429
36	0	13813.1666666667	-13813.1666666667
37	230761	171507.7	59253.3
38	132807	171507.7	-38700.7
39	157118	171507.7	-14389.7
40	253254	237015.714285714	16238.2857142857
41	269329	237015.714285714	32313.2857142857
42	161273	171507.7	-10234.7
43	107181	104565.416666667	2615.58333333333
44	195891	185229.2	10661.8
45	139667	143225.263157895	-3558.26315789475
46	171101	143225.263157895	27875.7368421053
47	81407	104565.416666667	-23158.4166666667
48	247563	171507.7	76055.3
49	239807	237015.714285714	2791.28571428571
50	172743	171507.7	1235.29999999999
51	48188	13813.1666666667	34374.8333333333
52	169355	171507.7	-2152.70000000001
53	325322	237015.714285714	88306.2857142857
54	241518	171507.7	70010.3
55	195583	185229.2	10353.8
56	159913	171507.7	-11594.7
57	220241	171507.7	48733.3
58	101694	104565.416666667	-2871.41666666667
59	157258	185229.2	-27971.2
60	202536	171507.7	31028.3
61	173505	171507.7	1997.29999999999
62	150518	171507.7	-20989.7
63	141491	143225.263157895	-1734.26315789475
64	125612	143225.263157895	-17613.2631578947
65	166049	143225.263157895	22823.7368421053
66	124197	143225.263157895	-19028.2631578947
67	195043	237015.714285714	-41972.7142857143
68	138708	171507.7	-32799.7
69	116552	104565.416666667	11986.5833333333
70	31970	13813.1666666667	18156.8333333333
71	258158	320169.857142857	-62011.8571428572
72	151194	143225.263157895	7968.73684210525
73	135926	143225.263157895	-7299.26315789475
74	119629	171507.7	-51878.7
75	171518	171507.7	10.2999999999884
76	108949	114046.5	-5097.5
77	183471	171507.7	11963.3
78	159966	237015.714285714	-77049.7142857143
79	93786	104565.416666667	-10779.4166666667
80	84971	114046.5	-29075.5
81	88882	104565.416666667	-15683.4166666667
82	304603	237015.714285714	67587.2857142857
83	75101	68746.5555555556	6354.44444444444
84	145043	171507.7	-26464.7
85	95827	114046.5	-18219.5
86	173924	171507.7	2416.29999999999
87	241957	237015.714285714	4941.28571428571
88	115367	114046.5	1320.5
89	118408	114046.5	4361.5
90	164078	171507.7	-7429.70000000001
91	158931	185229.2	-26298.2
92	184139	237015.714285714	-52876.7142857143
93	152856	143225.263157895	9630.73684210525
94	144014	143225.263157895	788.736842105252
95	62535	68746.5555555556	-6211.55555555556
96	245196	237015.714285714	8180.2857142857
97	199841	185229.2	14611.8
98	19349	13813.1666666667	5535.83333333333
99	247280	171507.7	75772.3
100	159408	171507.7	-12099.7
101	72128	68746.5555555556	3381.44444444444
102	104253	114046.5	-9793.5
103	151090	114046.5	37043.5
104	137382	143225.263157895	-5843.26315789475
105	87448	68746.5555555556	18701.4444444444
106	27676	13813.1666666667	13862.8333333333
107	165507	171507.7	-6000.70000000001
108	132148	114046.5	18101.5
109	0	13813.1666666667	-13813.1666666667
110	95778	104565.416666667	-8787.41666666667
111	109001	104565.416666667	4435.58333333333
112	158833	143225.263157895	15607.7368421053
113	147690	143225.263157895	4464.73684210525
114	89887	68746.5555555556	21140.4444444444
115	3616	13813.1666666667	-10197.1666666667
116	0	13813.1666666667	-13813.1666666667
117	199005	185229.2	13775.8
118	160930	185229.2	-24299.2
119	177948	171507.7	6440.29999999999
120	136061	104565.416666667	31495.5833333333
121	43410	68746.5555555556	-25336.5555555556
122	184277	185229.2	-952.200000000012
123	108858	104565.416666667	4292.58333333333
124	151030	171507.7	-20477.7
125	60493	68746.5555555556	-8253.55555555556
126	19764	13813.1666666667	5950.83333333333
127	177559	171507.7	6051.29999999999
128	140281	114046.5	26234.5
129	164249	171507.7	-7258.70000000001
130	11796	13813.1666666667	-2017.16666666667
131	10674	13813.1666666667	-3139.16666666667
132	151322	143225.263157895	8096.73684210525
133	6836	13813.1666666667	-6977.16666666667
134	174712	171507.7	3204.29999999999
135	5118	13813.1666666667	-8695.16666666667
136	40248	13813.1666666667	26434.8333333333
137	0	13813.1666666667	-13813.1666666667
138	127628	104565.416666667	23062.5833333333
139	88837	143225.263157895	-54388.2631578947
140	7131	13813.1666666667	-6682.16666666667
141	9056	13813.1666666667	-4757.16666666667
142	87957	104565.416666667	-16608.4166666667
143	144470	143225.263157895	1244.73684210525
144	111408	143225.263157895	-31817.2631578947

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 162687 & 171507.7 & -8820.70000000001 \tabularnewline
2 & 201906 & 185229.2 & 16676.8 \tabularnewline
3 & 7215 & 13813.1666666667 & -6598.16666666667 \tabularnewline
4 & 146367 & 171507.7 & -25140.7 \tabularnewline
5 & 257045 & 320169.857142857 & -63124.8571428572 \tabularnewline
6 & 524450 & 320169.857142857 & 204280.142857143 \tabularnewline
7 & 188294 & 185229.2 & 3064.79999999999 \tabularnewline
8 & 195674 & 185229.2 & 10444.8 \tabularnewline
9 & 177020 & 171507.7 & 5512.29999999999 \tabularnewline
10 & 330194 & 320169.857142857 & 10024.1428571428 \tabularnewline
11 & 121844 & 171507.7 & -49663.7 \tabularnewline
12 & 203938 & 237015.714285714 & -33077.7142857143 \tabularnewline
13 & 113213 & 171507.7 & -58294.7 \tabularnewline
14 & 220751 & 320169.857142857 & -99418.8571428572 \tabularnewline
15 & 172905 & 143225.263157895 & 29679.7368421053 \tabularnewline
16 & 156326 & 143225.263157895 & 13100.7368421053 \tabularnewline
17 & 145178 & 185229.2 & -40051.2 \tabularnewline
18 & 89171 & 114046.5 & -24875.5 \tabularnewline
19 & 172624 & 171507.7 & 1116.29999999999 \tabularnewline
20 & 39790 & 68746.5555555556 & -28956.5555555556 \tabularnewline
21 & 87927 & 68746.5555555556 & 19180.4444444444 \tabularnewline
22 & 241285 & 237015.714285714 & 4269.28571428571 \tabularnewline
23 & 198587 & 171507.7 & 27079.3 \tabularnewline
24 & 146946 & 171507.7 & -24561.7 \tabularnewline
25 & 159763 & 171507.7 & -11744.7 \tabularnewline
26 & 207078 & 171507.7 & 35570.3 \tabularnewline
27 & 212394 & 185229.2 & 27164.8 \tabularnewline
28 & 201536 & 185229.2 & 16306.8 \tabularnewline
29 & 394662 & 320169.857142857 & 74492.1428571428 \tabularnewline
30 & 217892 & 237015.714285714 & -19123.7142857143 \tabularnewline
31 & 182286 & 171507.7 & 10778.3 \tabularnewline
32 & 181740 & 185229.2 & -3489.20000000001 \tabularnewline
33 & 137978 & 171507.7 & -33529.7 \tabularnewline
34 & 255929 & 320169.857142857 & -64240.8571428572 \tabularnewline
35 & 236489 & 237015.714285714 & -526.71428571429 \tabularnewline
36 & 0 & 13813.1666666667 & -13813.1666666667 \tabularnewline
37 & 230761 & 171507.7 & 59253.3 \tabularnewline
38 & 132807 & 171507.7 & -38700.7 \tabularnewline
39 & 157118 & 171507.7 & -14389.7 \tabularnewline
40 & 253254 & 237015.714285714 & 16238.2857142857 \tabularnewline
41 & 269329 & 237015.714285714 & 32313.2857142857 \tabularnewline
42 & 161273 & 171507.7 & -10234.7 \tabularnewline
43 & 107181 & 104565.416666667 & 2615.58333333333 \tabularnewline
44 & 195891 & 185229.2 & 10661.8 \tabularnewline
45 & 139667 & 143225.263157895 & -3558.26315789475 \tabularnewline
46 & 171101 & 143225.263157895 & 27875.7368421053 \tabularnewline
47 & 81407 & 104565.416666667 & -23158.4166666667 \tabularnewline
48 & 247563 & 171507.7 & 76055.3 \tabularnewline
49 & 239807 & 237015.714285714 & 2791.28571428571 \tabularnewline
50 & 172743 & 171507.7 & 1235.29999999999 \tabularnewline
51 & 48188 & 13813.1666666667 & 34374.8333333333 \tabularnewline
52 & 169355 & 171507.7 & -2152.70000000001 \tabularnewline
53 & 325322 & 237015.714285714 & 88306.2857142857 \tabularnewline
54 & 241518 & 171507.7 & 70010.3 \tabularnewline
55 & 195583 & 185229.2 & 10353.8 \tabularnewline
56 & 159913 & 171507.7 & -11594.7 \tabularnewline
57 & 220241 & 171507.7 & 48733.3 \tabularnewline
58 & 101694 & 104565.416666667 & -2871.41666666667 \tabularnewline
59 & 157258 & 185229.2 & -27971.2 \tabularnewline
60 & 202536 & 171507.7 & 31028.3 \tabularnewline
61 & 173505 & 171507.7 & 1997.29999999999 \tabularnewline
62 & 150518 & 171507.7 & -20989.7 \tabularnewline
63 & 141491 & 143225.263157895 & -1734.26315789475 \tabularnewline
64 & 125612 & 143225.263157895 & -17613.2631578947 \tabularnewline
65 & 166049 & 143225.263157895 & 22823.7368421053 \tabularnewline
66 & 124197 & 143225.263157895 & -19028.2631578947 \tabularnewline
67 & 195043 & 237015.714285714 & -41972.7142857143 \tabularnewline
68 & 138708 & 171507.7 & -32799.7 \tabularnewline
69 & 116552 & 104565.416666667 & 11986.5833333333 \tabularnewline
70 & 31970 & 13813.1666666667 & 18156.8333333333 \tabularnewline
71 & 258158 & 320169.857142857 & -62011.8571428572 \tabularnewline
72 & 151194 & 143225.263157895 & 7968.73684210525 \tabularnewline
73 & 135926 & 143225.263157895 & -7299.26315789475 \tabularnewline
74 & 119629 & 171507.7 & -51878.7 \tabularnewline
75 & 171518 & 171507.7 & 10.2999999999884 \tabularnewline
76 & 108949 & 114046.5 & -5097.5 \tabularnewline
77 & 183471 & 171507.7 & 11963.3 \tabularnewline
78 & 159966 & 237015.714285714 & -77049.7142857143 \tabularnewline
79 & 93786 & 104565.416666667 & -10779.4166666667 \tabularnewline
80 & 84971 & 114046.5 & -29075.5 \tabularnewline
81 & 88882 & 104565.416666667 & -15683.4166666667 \tabularnewline
82 & 304603 & 237015.714285714 & 67587.2857142857 \tabularnewline
83 & 75101 & 68746.5555555556 & 6354.44444444444 \tabularnewline
84 & 145043 & 171507.7 & -26464.7 \tabularnewline
85 & 95827 & 114046.5 & -18219.5 \tabularnewline
86 & 173924 & 171507.7 & 2416.29999999999 \tabularnewline
87 & 241957 & 237015.714285714 & 4941.28571428571 \tabularnewline
88 & 115367 & 114046.5 & 1320.5 \tabularnewline
89 & 118408 & 114046.5 & 4361.5 \tabularnewline
90 & 164078 & 171507.7 & -7429.70000000001 \tabularnewline
91 & 158931 & 185229.2 & -26298.2 \tabularnewline
92 & 184139 & 237015.714285714 & -52876.7142857143 \tabularnewline
93 & 152856 & 143225.263157895 & 9630.73684210525 \tabularnewline
94 & 144014 & 143225.263157895 & 788.736842105252 \tabularnewline
95 & 62535 & 68746.5555555556 & -6211.55555555556 \tabularnewline
96 & 245196 & 237015.714285714 & 8180.2857142857 \tabularnewline
97 & 199841 & 185229.2 & 14611.8 \tabularnewline
98 & 19349 & 13813.1666666667 & 5535.83333333333 \tabularnewline
99 & 247280 & 171507.7 & 75772.3 \tabularnewline
100 & 159408 & 171507.7 & -12099.7 \tabularnewline
101 & 72128 & 68746.5555555556 & 3381.44444444444 \tabularnewline
102 & 104253 & 114046.5 & -9793.5 \tabularnewline
103 & 151090 & 114046.5 & 37043.5 \tabularnewline
104 & 137382 & 143225.263157895 & -5843.26315789475 \tabularnewline
105 & 87448 & 68746.5555555556 & 18701.4444444444 \tabularnewline
106 & 27676 & 13813.1666666667 & 13862.8333333333 \tabularnewline
107 & 165507 & 171507.7 & -6000.70000000001 \tabularnewline
108 & 132148 & 114046.5 & 18101.5 \tabularnewline
109 & 0 & 13813.1666666667 & -13813.1666666667 \tabularnewline
110 & 95778 & 104565.416666667 & -8787.41666666667 \tabularnewline
111 & 109001 & 104565.416666667 & 4435.58333333333 \tabularnewline
112 & 158833 & 143225.263157895 & 15607.7368421053 \tabularnewline
113 & 147690 & 143225.263157895 & 4464.73684210525 \tabularnewline
114 & 89887 & 68746.5555555556 & 21140.4444444444 \tabularnewline
115 & 3616 & 13813.1666666667 & -10197.1666666667 \tabularnewline
116 & 0 & 13813.1666666667 & -13813.1666666667 \tabularnewline
117 & 199005 & 185229.2 & 13775.8 \tabularnewline
118 & 160930 & 185229.2 & -24299.2 \tabularnewline
119 & 177948 & 171507.7 & 6440.29999999999 \tabularnewline
120 & 136061 & 104565.416666667 & 31495.5833333333 \tabularnewline
121 & 43410 & 68746.5555555556 & -25336.5555555556 \tabularnewline
122 & 184277 & 185229.2 & -952.200000000012 \tabularnewline
123 & 108858 & 104565.416666667 & 4292.58333333333 \tabularnewline
124 & 151030 & 171507.7 & -20477.7 \tabularnewline
125 & 60493 & 68746.5555555556 & -8253.55555555556 \tabularnewline
126 & 19764 & 13813.1666666667 & 5950.83333333333 \tabularnewline
127 & 177559 & 171507.7 & 6051.29999999999 \tabularnewline
128 & 140281 & 114046.5 & 26234.5 \tabularnewline
129 & 164249 & 171507.7 & -7258.70000000001 \tabularnewline
130 & 11796 & 13813.1666666667 & -2017.16666666667 \tabularnewline
131 & 10674 & 13813.1666666667 & -3139.16666666667 \tabularnewline
132 & 151322 & 143225.263157895 & 8096.73684210525 \tabularnewline
133 & 6836 & 13813.1666666667 & -6977.16666666667 \tabularnewline
134 & 174712 & 171507.7 & 3204.29999999999 \tabularnewline
135 & 5118 & 13813.1666666667 & -8695.16666666667 \tabularnewline
136 & 40248 & 13813.1666666667 & 26434.8333333333 \tabularnewline
137 & 0 & 13813.1666666667 & -13813.1666666667 \tabularnewline
138 & 127628 & 104565.416666667 & 23062.5833333333 \tabularnewline
139 & 88837 & 143225.263157895 & -54388.2631578947 \tabularnewline
140 & 7131 & 13813.1666666667 & -6682.16666666667 \tabularnewline
141 & 9056 & 13813.1666666667 & -4757.16666666667 \tabularnewline
142 & 87957 & 104565.416666667 & -16608.4166666667 \tabularnewline
143 & 144470 & 143225.263157895 & 1244.73684210525 \tabularnewline
144 & 111408 & 143225.263157895 & -31817.2631578947 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159482&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]162687[/C][C]171507.7[/C][C]-8820.70000000001[/C][/ROW]
[ROW][C]2[/C][C]201906[/C][C]185229.2[/C][C]16676.8[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]13813.1666666667[/C][C]-6598.16666666667[/C][/ROW]
[ROW][C]4[/C][C]146367[/C][C]171507.7[/C][C]-25140.7[/C][/ROW]
[ROW][C]5[/C][C]257045[/C][C]320169.857142857[/C][C]-63124.8571428572[/C][/ROW]
[ROW][C]6[/C][C]524450[/C][C]320169.857142857[/C][C]204280.142857143[/C][/ROW]
[ROW][C]7[/C][C]188294[/C][C]185229.2[/C][C]3064.79999999999[/C][/ROW]
[ROW][C]8[/C][C]195674[/C][C]185229.2[/C][C]10444.8[/C][/ROW]
[ROW][C]9[/C][C]177020[/C][C]171507.7[/C][C]5512.29999999999[/C][/ROW]
[ROW][C]10[/C][C]330194[/C][C]320169.857142857[/C][C]10024.1428571428[/C][/ROW]
[ROW][C]11[/C][C]121844[/C][C]171507.7[/C][C]-49663.7[/C][/ROW]
[ROW][C]12[/C][C]203938[/C][C]237015.714285714[/C][C]-33077.7142857143[/C][/ROW]
[ROW][C]13[/C][C]113213[/C][C]171507.7[/C][C]-58294.7[/C][/ROW]
[ROW][C]14[/C][C]220751[/C][C]320169.857142857[/C][C]-99418.8571428572[/C][/ROW]
[ROW][C]15[/C][C]172905[/C][C]143225.263157895[/C][C]29679.7368421053[/C][/ROW]
[ROW][C]16[/C][C]156326[/C][C]143225.263157895[/C][C]13100.7368421053[/C][/ROW]
[ROW][C]17[/C][C]145178[/C][C]185229.2[/C][C]-40051.2[/C][/ROW]
[ROW][C]18[/C][C]89171[/C][C]114046.5[/C][C]-24875.5[/C][/ROW]
[ROW][C]19[/C][C]172624[/C][C]171507.7[/C][C]1116.29999999999[/C][/ROW]
[ROW][C]20[/C][C]39790[/C][C]68746.5555555556[/C][C]-28956.5555555556[/C][/ROW]
[ROW][C]21[/C][C]87927[/C][C]68746.5555555556[/C][C]19180.4444444444[/C][/ROW]
[ROW][C]22[/C][C]241285[/C][C]237015.714285714[/C][C]4269.28571428571[/C][/ROW]
[ROW][C]23[/C][C]198587[/C][C]171507.7[/C][C]27079.3[/C][/ROW]
[ROW][C]24[/C][C]146946[/C][C]171507.7[/C][C]-24561.7[/C][/ROW]
[ROW][C]25[/C][C]159763[/C][C]171507.7[/C][C]-11744.7[/C][/ROW]
[ROW][C]26[/C][C]207078[/C][C]171507.7[/C][C]35570.3[/C][/ROW]
[ROW][C]27[/C][C]212394[/C][C]185229.2[/C][C]27164.8[/C][/ROW]
[ROW][C]28[/C][C]201536[/C][C]185229.2[/C][C]16306.8[/C][/ROW]
[ROW][C]29[/C][C]394662[/C][C]320169.857142857[/C][C]74492.1428571428[/C][/ROW]
[ROW][C]30[/C][C]217892[/C][C]237015.714285714[/C][C]-19123.7142857143[/C][/ROW]
[ROW][C]31[/C][C]182286[/C][C]171507.7[/C][C]10778.3[/C][/ROW]
[ROW][C]32[/C][C]181740[/C][C]185229.2[/C][C]-3489.20000000001[/C][/ROW]
[ROW][C]33[/C][C]137978[/C][C]171507.7[/C][C]-33529.7[/C][/ROW]
[ROW][C]34[/C][C]255929[/C][C]320169.857142857[/C][C]-64240.8571428572[/C][/ROW]
[ROW][C]35[/C][C]236489[/C][C]237015.714285714[/C][C]-526.71428571429[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]13813.1666666667[/C][C]-13813.1666666667[/C][/ROW]
[ROW][C]37[/C][C]230761[/C][C]171507.7[/C][C]59253.3[/C][/ROW]
[ROW][C]38[/C][C]132807[/C][C]171507.7[/C][C]-38700.7[/C][/ROW]
[ROW][C]39[/C][C]157118[/C][C]171507.7[/C][C]-14389.7[/C][/ROW]
[ROW][C]40[/C][C]253254[/C][C]237015.714285714[/C][C]16238.2857142857[/C][/ROW]
[ROW][C]41[/C][C]269329[/C][C]237015.714285714[/C][C]32313.2857142857[/C][/ROW]
[ROW][C]42[/C][C]161273[/C][C]171507.7[/C][C]-10234.7[/C][/ROW]
[ROW][C]43[/C][C]107181[/C][C]104565.416666667[/C][C]2615.58333333333[/C][/ROW]
[ROW][C]44[/C][C]195891[/C][C]185229.2[/C][C]10661.8[/C][/ROW]
[ROW][C]45[/C][C]139667[/C][C]143225.263157895[/C][C]-3558.26315789475[/C][/ROW]
[ROW][C]46[/C][C]171101[/C][C]143225.263157895[/C][C]27875.7368421053[/C][/ROW]
[ROW][C]47[/C][C]81407[/C][C]104565.416666667[/C][C]-23158.4166666667[/C][/ROW]
[ROW][C]48[/C][C]247563[/C][C]171507.7[/C][C]76055.3[/C][/ROW]
[ROW][C]49[/C][C]239807[/C][C]237015.714285714[/C][C]2791.28571428571[/C][/ROW]
[ROW][C]50[/C][C]172743[/C][C]171507.7[/C][C]1235.29999999999[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]13813.1666666667[/C][C]34374.8333333333[/C][/ROW]
[ROW][C]52[/C][C]169355[/C][C]171507.7[/C][C]-2152.70000000001[/C][/ROW]
[ROW][C]53[/C][C]325322[/C][C]237015.714285714[/C][C]88306.2857142857[/C][/ROW]
[ROW][C]54[/C][C]241518[/C][C]171507.7[/C][C]70010.3[/C][/ROW]
[ROW][C]55[/C][C]195583[/C][C]185229.2[/C][C]10353.8[/C][/ROW]
[ROW][C]56[/C][C]159913[/C][C]171507.7[/C][C]-11594.7[/C][/ROW]
[ROW][C]57[/C][C]220241[/C][C]171507.7[/C][C]48733.3[/C][/ROW]
[ROW][C]58[/C][C]101694[/C][C]104565.416666667[/C][C]-2871.41666666667[/C][/ROW]
[ROW][C]59[/C][C]157258[/C][C]185229.2[/C][C]-27971.2[/C][/ROW]
[ROW][C]60[/C][C]202536[/C][C]171507.7[/C][C]31028.3[/C][/ROW]
[ROW][C]61[/C][C]173505[/C][C]171507.7[/C][C]1997.29999999999[/C][/ROW]
[ROW][C]62[/C][C]150518[/C][C]171507.7[/C][C]-20989.7[/C][/ROW]
[ROW][C]63[/C][C]141491[/C][C]143225.263157895[/C][C]-1734.26315789475[/C][/ROW]
[ROW][C]64[/C][C]125612[/C][C]143225.263157895[/C][C]-17613.2631578947[/C][/ROW]
[ROW][C]65[/C][C]166049[/C][C]143225.263157895[/C][C]22823.7368421053[/C][/ROW]
[ROW][C]66[/C][C]124197[/C][C]143225.263157895[/C][C]-19028.2631578947[/C][/ROW]
[ROW][C]67[/C][C]195043[/C][C]237015.714285714[/C][C]-41972.7142857143[/C][/ROW]
[ROW][C]68[/C][C]138708[/C][C]171507.7[/C][C]-32799.7[/C][/ROW]
[ROW][C]69[/C][C]116552[/C][C]104565.416666667[/C][C]11986.5833333333[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]13813.1666666667[/C][C]18156.8333333333[/C][/ROW]
[ROW][C]71[/C][C]258158[/C][C]320169.857142857[/C][C]-62011.8571428572[/C][/ROW]
[ROW][C]72[/C][C]151194[/C][C]143225.263157895[/C][C]7968.73684210525[/C][/ROW]
[ROW][C]73[/C][C]135926[/C][C]143225.263157895[/C][C]-7299.26315789475[/C][/ROW]
[ROW][C]74[/C][C]119629[/C][C]171507.7[/C][C]-51878.7[/C][/ROW]
[ROW][C]75[/C][C]171518[/C][C]171507.7[/C][C]10.2999999999884[/C][/ROW]
[ROW][C]76[/C][C]108949[/C][C]114046.5[/C][C]-5097.5[/C][/ROW]
[ROW][C]77[/C][C]183471[/C][C]171507.7[/C][C]11963.3[/C][/ROW]
[ROW][C]78[/C][C]159966[/C][C]237015.714285714[/C][C]-77049.7142857143[/C][/ROW]
[ROW][C]79[/C][C]93786[/C][C]104565.416666667[/C][C]-10779.4166666667[/C][/ROW]
[ROW][C]80[/C][C]84971[/C][C]114046.5[/C][C]-29075.5[/C][/ROW]
[ROW][C]81[/C][C]88882[/C][C]104565.416666667[/C][C]-15683.4166666667[/C][/ROW]
[ROW][C]82[/C][C]304603[/C][C]237015.714285714[/C][C]67587.2857142857[/C][/ROW]
[ROW][C]83[/C][C]75101[/C][C]68746.5555555556[/C][C]6354.44444444444[/C][/ROW]
[ROW][C]84[/C][C]145043[/C][C]171507.7[/C][C]-26464.7[/C][/ROW]
[ROW][C]85[/C][C]95827[/C][C]114046.5[/C][C]-18219.5[/C][/ROW]
[ROW][C]86[/C][C]173924[/C][C]171507.7[/C][C]2416.29999999999[/C][/ROW]
[ROW][C]87[/C][C]241957[/C][C]237015.714285714[/C][C]4941.28571428571[/C][/ROW]
[ROW][C]88[/C][C]115367[/C][C]114046.5[/C][C]1320.5[/C][/ROW]
[ROW][C]89[/C][C]118408[/C][C]114046.5[/C][C]4361.5[/C][/ROW]
[ROW][C]90[/C][C]164078[/C][C]171507.7[/C][C]-7429.70000000001[/C][/ROW]
[ROW][C]91[/C][C]158931[/C][C]185229.2[/C][C]-26298.2[/C][/ROW]
[ROW][C]92[/C][C]184139[/C][C]237015.714285714[/C][C]-52876.7142857143[/C][/ROW]
[ROW][C]93[/C][C]152856[/C][C]143225.263157895[/C][C]9630.73684210525[/C][/ROW]
[ROW][C]94[/C][C]144014[/C][C]143225.263157895[/C][C]788.736842105252[/C][/ROW]
[ROW][C]95[/C][C]62535[/C][C]68746.5555555556[/C][C]-6211.55555555556[/C][/ROW]
[ROW][C]96[/C][C]245196[/C][C]237015.714285714[/C][C]8180.2857142857[/C][/ROW]
[ROW][C]97[/C][C]199841[/C][C]185229.2[/C][C]14611.8[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]13813.1666666667[/C][C]5535.83333333333[/C][/ROW]
[ROW][C]99[/C][C]247280[/C][C]171507.7[/C][C]75772.3[/C][/ROW]
[ROW][C]100[/C][C]159408[/C][C]171507.7[/C][C]-12099.7[/C][/ROW]
[ROW][C]101[/C][C]72128[/C][C]68746.5555555556[/C][C]3381.44444444444[/C][/ROW]
[ROW][C]102[/C][C]104253[/C][C]114046.5[/C][C]-9793.5[/C][/ROW]
[ROW][C]103[/C][C]151090[/C][C]114046.5[/C][C]37043.5[/C][/ROW]
[ROW][C]104[/C][C]137382[/C][C]143225.263157895[/C][C]-5843.26315789475[/C][/ROW]
[ROW][C]105[/C][C]87448[/C][C]68746.5555555556[/C][C]18701.4444444444[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]13813.1666666667[/C][C]13862.8333333333[/C][/ROW]
[ROW][C]107[/C][C]165507[/C][C]171507.7[/C][C]-6000.70000000001[/C][/ROW]
[ROW][C]108[/C][C]132148[/C][C]114046.5[/C][C]18101.5[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]13813.1666666667[/C][C]-13813.1666666667[/C][/ROW]
[ROW][C]110[/C][C]95778[/C][C]104565.416666667[/C][C]-8787.41666666667[/C][/ROW]
[ROW][C]111[/C][C]109001[/C][C]104565.416666667[/C][C]4435.58333333333[/C][/ROW]
[ROW][C]112[/C][C]158833[/C][C]143225.263157895[/C][C]15607.7368421053[/C][/ROW]
[ROW][C]113[/C][C]147690[/C][C]143225.263157895[/C][C]4464.73684210525[/C][/ROW]
[ROW][C]114[/C][C]89887[/C][C]68746.5555555556[/C][C]21140.4444444444[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]13813.1666666667[/C][C]-10197.1666666667[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]13813.1666666667[/C][C]-13813.1666666667[/C][/ROW]
[ROW][C]117[/C][C]199005[/C][C]185229.2[/C][C]13775.8[/C][/ROW]
[ROW][C]118[/C][C]160930[/C][C]185229.2[/C][C]-24299.2[/C][/ROW]
[ROW][C]119[/C][C]177948[/C][C]171507.7[/C][C]6440.29999999999[/C][/ROW]
[ROW][C]120[/C][C]136061[/C][C]104565.416666667[/C][C]31495.5833333333[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]68746.5555555556[/C][C]-25336.5555555556[/C][/ROW]
[ROW][C]122[/C][C]184277[/C][C]185229.2[/C][C]-952.200000000012[/C][/ROW]
[ROW][C]123[/C][C]108858[/C][C]104565.416666667[/C][C]4292.58333333333[/C][/ROW]
[ROW][C]124[/C][C]151030[/C][C]171507.7[/C][C]-20477.7[/C][/ROW]
[ROW][C]125[/C][C]60493[/C][C]68746.5555555556[/C][C]-8253.55555555556[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]13813.1666666667[/C][C]5950.83333333333[/C][/ROW]
[ROW][C]127[/C][C]177559[/C][C]171507.7[/C][C]6051.29999999999[/C][/ROW]
[ROW][C]128[/C][C]140281[/C][C]114046.5[/C][C]26234.5[/C][/ROW]
[ROW][C]129[/C][C]164249[/C][C]171507.7[/C][C]-7258.70000000001[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]13813.1666666667[/C][C]-2017.16666666667[/C][/ROW]
[ROW][C]131[/C][C]10674[/C][C]13813.1666666667[/C][C]-3139.16666666667[/C][/ROW]
[ROW][C]132[/C][C]151322[/C][C]143225.263157895[/C][C]8096.73684210525[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]13813.1666666667[/C][C]-6977.16666666667[/C][/ROW]
[ROW][C]134[/C][C]174712[/C][C]171507.7[/C][C]3204.29999999999[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]13813.1666666667[/C][C]-8695.16666666667[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]13813.1666666667[/C][C]26434.8333333333[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]13813.1666666667[/C][C]-13813.1666666667[/C][/ROW]
[ROW][C]138[/C][C]127628[/C][C]104565.416666667[/C][C]23062.5833333333[/C][/ROW]
[ROW][C]139[/C][C]88837[/C][C]143225.263157895[/C][C]-54388.2631578947[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]13813.1666666667[/C][C]-6682.16666666667[/C][/ROW]
[ROW][C]141[/C][C]9056[/C][C]13813.1666666667[/C][C]-4757.16666666667[/C][/ROW]
[ROW][C]142[/C][C]87957[/C][C]104565.416666667[/C][C]-16608.4166666667[/C][/ROW]
[ROW][C]143[/C][C]144470[/C][C]143225.263157895[/C][C]1244.73684210525[/C][/ROW]
[ROW][C]144[/C][C]111408[/C][C]143225.263157895[/C][C]-31817.2631578947[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159482&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159482&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	162687	171507.7	-8820.70000000001
2	201906	185229.2	16676.8
3	7215	13813.1666666667	-6598.16666666667
4	146367	171507.7	-25140.7
5	257045	320169.857142857	-63124.8571428572
6	524450	320169.857142857	204280.142857143
7	188294	185229.2	3064.79999999999
8	195674	185229.2	10444.8
9	177020	171507.7	5512.29999999999
10	330194	320169.857142857	10024.1428571428
11	121844	171507.7	-49663.7
12	203938	237015.714285714	-33077.7142857143
13	113213	171507.7	-58294.7
14	220751	320169.857142857	-99418.8571428572
15	172905	143225.263157895	29679.7368421053
16	156326	143225.263157895	13100.7368421053
17	145178	185229.2	-40051.2
18	89171	114046.5	-24875.5
19	172624	171507.7	1116.29999999999
20	39790	68746.5555555556	-28956.5555555556
21	87927	68746.5555555556	19180.4444444444
22	241285	237015.714285714	4269.28571428571
23	198587	171507.7	27079.3
24	146946	171507.7	-24561.7
25	159763	171507.7	-11744.7
26	207078	171507.7	35570.3
27	212394	185229.2	27164.8
28	201536	185229.2	16306.8
29	394662	320169.857142857	74492.1428571428
30	217892	237015.714285714	-19123.7142857143
31	182286	171507.7	10778.3
32	181740	185229.2	-3489.20000000001
33	137978	171507.7	-33529.7
34	255929	320169.857142857	-64240.8571428572
35	236489	237015.714285714	-526.71428571429
36	0	13813.1666666667	-13813.1666666667
37	230761	171507.7	59253.3
38	132807	171507.7	-38700.7
39	157118	171507.7	-14389.7
40	253254	237015.714285714	16238.2857142857
41	269329	237015.714285714	32313.2857142857
42	161273	171507.7	-10234.7
43	107181	104565.416666667	2615.58333333333
44	195891	185229.2	10661.8
45	139667	143225.263157895	-3558.26315789475
46	171101	143225.263157895	27875.7368421053
47	81407	104565.416666667	-23158.4166666667
48	247563	171507.7	76055.3
49	239807	237015.714285714	2791.28571428571
50	172743	171507.7	1235.29999999999
51	48188	13813.1666666667	34374.8333333333
52	169355	171507.7	-2152.70000000001
53	325322	237015.714285714	88306.2857142857
54	241518	171507.7	70010.3
55	195583	185229.2	10353.8
56	159913	171507.7	-11594.7
57	220241	171507.7	48733.3
58	101694	104565.416666667	-2871.41666666667
59	157258	185229.2	-27971.2
60	202536	171507.7	31028.3
61	173505	171507.7	1997.29999999999
62	150518	171507.7	-20989.7
63	141491	143225.263157895	-1734.26315789475
64	125612	143225.263157895	-17613.2631578947
65	166049	143225.263157895	22823.7368421053
66	124197	143225.263157895	-19028.2631578947
67	195043	237015.714285714	-41972.7142857143
68	138708	171507.7	-32799.7
69	116552	104565.416666667	11986.5833333333
70	31970	13813.1666666667	18156.8333333333
71	258158	320169.857142857	-62011.8571428572
72	151194	143225.263157895	7968.73684210525
73	135926	143225.263157895	-7299.26315789475
74	119629	171507.7	-51878.7
75	171518	171507.7	10.2999999999884
76	108949	114046.5	-5097.5
77	183471	171507.7	11963.3
78	159966	237015.714285714	-77049.7142857143
79	93786	104565.416666667	-10779.4166666667
80	84971	114046.5	-29075.5
81	88882	104565.416666667	-15683.4166666667
82	304603	237015.714285714	67587.2857142857
83	75101	68746.5555555556	6354.44444444444
84	145043	171507.7	-26464.7
85	95827	114046.5	-18219.5
86	173924	171507.7	2416.29999999999
87	241957	237015.714285714	4941.28571428571
88	115367	114046.5	1320.5
89	118408	114046.5	4361.5
90	164078	171507.7	-7429.70000000001
91	158931	185229.2	-26298.2
92	184139	237015.714285714	-52876.7142857143
93	152856	143225.263157895	9630.73684210525
94	144014	143225.263157895	788.736842105252
95	62535	68746.5555555556	-6211.55555555556
96	245196	237015.714285714	8180.2857142857
97	199841	185229.2	14611.8
98	19349	13813.1666666667	5535.83333333333
99	247280	171507.7	75772.3
100	159408	171507.7	-12099.7
101	72128	68746.5555555556	3381.44444444444
102	104253	114046.5	-9793.5
103	151090	114046.5	37043.5
104	137382	143225.263157895	-5843.26315789475
105	87448	68746.5555555556	18701.4444444444
106	27676	13813.1666666667	13862.8333333333
107	165507	171507.7	-6000.70000000001
108	132148	114046.5	18101.5
109	0	13813.1666666667	-13813.1666666667
110	95778	104565.416666667	-8787.41666666667
111	109001	104565.416666667	4435.58333333333
112	158833	143225.263157895	15607.7368421053
113	147690	143225.263157895	4464.73684210525
114	89887	68746.5555555556	21140.4444444444
115	3616	13813.1666666667	-10197.1666666667
116	0	13813.1666666667	-13813.1666666667
117	199005	185229.2	13775.8
118	160930	185229.2	-24299.2
119	177948	171507.7	6440.29999999999
120	136061	104565.416666667	31495.5833333333
121	43410	68746.5555555556	-25336.5555555556
122	184277	185229.2	-952.200000000012
123	108858	104565.416666667	4292.58333333333
124	151030	171507.7	-20477.7
125	60493	68746.5555555556	-8253.55555555556
126	19764	13813.1666666667	5950.83333333333
127	177559	171507.7	6051.29999999999
128	140281	114046.5	26234.5
129	164249	171507.7	-7258.70000000001
130	11796	13813.1666666667	-2017.16666666667
131	10674	13813.1666666667	-3139.16666666667
132	151322	143225.263157895	8096.73684210525
133	6836	13813.1666666667	-6977.16666666667
134	174712	171507.7	3204.29999999999
135	5118	13813.1666666667	-8695.16666666667
136	40248	13813.1666666667	26434.8333333333
137	0	13813.1666666667	-13813.1666666667
138	127628	104565.416666667	23062.5833333333
139	88837	143225.263157895	-54388.2631578947
140	7131	13813.1666666667	-6682.16666666667
141	9056	13813.1666666667	-4757.16666666667
142	87957	104565.416666667	-16608.4166666667
143	144470	143225.263157895	1244.73684210525
144	111408	143225.263157895	-31817.2631578947

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 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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