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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Wed, 21 Dec 2011 08:30:58 -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/21/t1324474512mcg25nobq1cy0t6.htm/, Retrieved Sun, 05 May 2024 14:42:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158673, Retrieved Sun, 05 May 2024 14:42:12 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

140

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

-     [Kendall tau Correlation Matrix] [Paper, Pearson Co...] [2011-12-18 12:42:54] [75512e061a94450f738c2449abbaac12]
-   P   [Kendall tau Correlation Matrix] [Paper, Kendall's ...] [2011-12-19 10:46:53] [75512e061a94450f738c2449abbaac12]
- RMP     [Multiple Regression] [Paper, 3.3 Meervo...] [2011-12-19 15:22:52] [75512e061a94450f738c2449abbaac12]
- RMP         [Recursive Partitioning (Regression Trees)] [paper, recursive ...] [2011-12-21 13:30:58] [242bbde8f74d68805b56d9ecebfdbe63] [Current]
-   P           [Recursive Partitioning (Regression Trees)] [Paper, recursive ...] [2011-12-21 20:43:08] [75512e061a94450f738c2449abbaac12] 
- R P             [Recursive Partitioning (Regression Trees)] [paper, RP (quanti...] [2011-12-21 20:57:26] [75512e061a94450f738c2449abbaac12] 
- R P           [Recursive Partitioning (Regression Trees)] [paper, RP (endoge...] [2011-12-21 21:08:01] [75512e061a94450f738c2449abbaac12] 
- RMPD          [ARIMA Backward Selection] [Paper, ARIMA back...] [2011-12-22 10:13:22] [75512e061a94450f738c2449abbaac12] 
- R               [ARIMA Backward Selection] [Backward selection] [2012-12-17 16:20:17] [c19fc719a172e69332289836fdae123a] 
- RMPD          [ARIMA Forecasting] [Paper, ARIMA fore...] [2011-12-22 10:35:36] [75512e061a94450f738c2449abbaac12] 
- R               [ARIMA Forecasting] [ARIMA forecasting] [2012-12-17 16:39:26] [c19fc719a172e69332289836fdae123a] 

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Dataseries X:

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Histogram

Boxplots

1683	150596	84	37	18	63	20465	23975	0
1323	154801	50	42	20	56	33629	85634	1
192	7215	18	0	0	0	1423	1929	0
2172	122139	91	49	26	60	25629	36294	0
3335	221399	129	76	30	112	54002	72255	0
6310	441870	237	118	34	130	151036	189748	1
1478	134379	52	42	23	71	33287	61834	1
1324	140428	53	57	30	107	31172	68167	0
1488	103255	40	45	30	50	28113	38462	0
2756	271630	91	67	26	79	57803	101219	1
1931	121593	71	50	24	58	49830	43270	2
1966	172071	63	71	30	91	52143	76183	0
1575	83707	94	41	19	36	21055	31476	0
2855	197412	98	66	25	91	47007	62157	4
1263	134398	48	42	17	58	28735	46261	4
1479	139224	73	54	19	65	59147	50063	3
1636	134153	52	75	33	131	78950	64483	0
1076	64149	52	0	15	45	13497	2341	5
2376	122294	82	54	34	110	46154	48149	0
678	24889	22	13	15	33	53249	12743	0
902	52197	52	16	15	37	10726	18743	0
2308	188915	89	77	27	78	83700	97057	0
1590	163147	66	34	25	67	40400	17675	0
1863	98575	48	38	34	69	33797	33106	1
1799	143546	80	50	21	58	36205	53311	1
1385	139780	25	39	21	60	30165	42754	0
1870	163784	146	54	25	88	58534	59056	0
1161	152479	75	67	28	71	44663	101621	0
2417	304108	109	55	26	85	92556	118120	0
1952	184024	40	52	20	67	40078	79572	0
1514	151621	41	50	28	84	34711	42744	0
1487	164516	41	54	20	58	31076	65931	2
2051	120179	94	53	17	35	74608	38575	4
2843	214701	116	76	25	74	58092	28795	0
2216	196865	48	52	24	89	42009	94440	1
1	0	1	0	0	0	0	0	0
1830	181527	57	46	27	75	36022	38229	0
1563	93107	49	44	14	39	23333	31972	3
2046	129352	45	35	32	93	53349	40071	9
2005	229143	58	82	31	123	92596	132480	0
1934	177063	67	70	21	73	49598	62797	2
1572	126602	53	31	34	118	44093	40429	0
950	93742	29	25	23	76	84205	45545	2
1877	152153	72	48	24	65	63369	57568	1
1036	95704	42	44	22	82	60132	39019	2
1097	139793	84	40	22	67	37403	53866	2
730	76348	30	23	35	63	24460	38345	1
1918	188980	86	63	21	84	46456	50210	0
1826	172100	79	43	31	112	66616	80947	1
2444	146552	54	62	26	75	41554	43461	7
658	48188	28	12	22	39	22346	14812	0
1425	109185	60	63	21	63	30874	37819	0
2246	263652	68	60	27	93	68701	102738	0
1899	215609	75	53	26	69	35728	54509	0
1630	174876	54	53	33	117	29010	62956	1
1496	115124	49	35	11	30	23110	55411	6
1681	179712	60	49	26	65	38844	50611	0
816	70369	20	25	26	78	27084	26692	0
902	109215	58	47	21	80	35139	60056	0
2606	166096	85	30	38	85	57476	25155	10
1557	130414	51	50	29	107	33277	42840	6
1780	102057	71	36	19	60	31141	39358	0
1265	115310	56	43	19	53	61281	47241	11
1117	101181	32	44	24	62	25820	49611	3
1069	135228	31	14	26	90	23284	41833	0
1229	94982	37	38	29	89	35378	48930	0
2155	166919	67	58	34	127	74990	110600	8
2500	118169	64	68	25	71	29653	52235	2
1003	102361	36	48	24	75	64622	53986	0
340	31970	15	5	21	42	4157	4105	0
2586	200413	107	53	19	42	29245	59331	3
1119	103381	58	36	12	8	50008	47796	1
1251	94940	61	62	28	82	52338	38302	2
1516	101560	65	46	21	41	13310	14063	1
2473	144176	60	67	34	118	92901	54414	0
1288	71921	37	2	32	91	10956	9903	2
1911	126905	54	64	27	96	34241	53987	1
2279	131184	87	59	26	81	75043	88937	0
816	60138	23	16	21	46	21152	21928	0
1234	84971	71	34	31	60	42249	29487	0
907	80420	64	54	26	69	42005	35334	0
1827	233569	57	39	26	85	41152	57596	0
841	56252	25	26	23	17	14399	29750	0
1309	97181	32	37	25	61	28263	41029	0
764	50800	41	17	22	55	17215	12416	0
1439	125941	45	32	26	55	48140	51158	0
2500	211032	210	55	33	124	62897	79935	0
974	71960	92	39	22	65	22883	26552	0
1152	90379	53	39	24	73	41622	25807	6
1261	125650	47	28	21	67	40715	50620	0
1508	115572	36	45	28	66	65897	61467	5
2005	136266	67	66	22	61	76542	65292	1
1191	146715	55	39	22	74	37477	55516	0
1265	124626	57	27	15	55	53216	42006	0
761	49176	33	22	13	27	40911	26273	0
2156	212926	102	43	36	115	57021	90248	0
1689	173884	55	88	24	76	73116	61476	0
223	19349	12	13	1	0	3895	9604	0
2074	181141	95	23	24	83	46609	45108	3
1879	145502	70	40	31	90	29351	47232	0
566	45448	26	8	4	4	2325	3439	0
802	58280	20	41	20	56	31747	30553	0
1131	115944	44	51	23	63	32665	24751	0
981	94341	52	24	23	52	19249	34458	1
591	59090	37	23	12	24	15292	24649	0
596	27676	22	2	16	17	5842	2342	0
1261	120586	41	78	28	101	33994	52739	0
861	88011	31	12	10	20	13018	6245	0
0	0	0	0	0	0	0	0	0
1030	85610	31	46	25	51	98177	35381	0
991	84193	58	22	21	76	37941	19595	0
1178	117769	39	49	21	55	31032	50848	0
1200	107653	56	52	21	70	32683	39443	0
849	71894	57	36	21	38	34545	27023	0
78	3616	5	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0
924	154806	38	35	23	81	27525	61022	0
1480	136061	73	68	29	64	66856	63528	0
1870	141822	89	26	27	66	28549	34835	1
861	106515	37	32	23	89	38610	37172	0
778	43410	19	7	1	3	2781	13	0
1533	146920	64	67	25	76	41211	62548	1
889	88874	38	30	17	48	22698	31334	0
1705	111924	49	55	29	62	41194	20839	8
700	60373	39	3	12	32	32689	5084	3
285	19764	12	10	2	4	5752	9927	1
1490	121665	46	46	18	61	26757	53229	2
981	108685	26	23	25	90	22527	29877	0
1368	124493	37	43	29	91	44810	37310	0
256	11796	9	1	2	1	0	0	0
98	10674	9	0	0	0	0	0	0
1317	131263	52	33	18	39	100674	50067	0
41	6836	3	0	1	0	0	0	0
1768	153278	55	48	21	45	57786	47708	5
42	5118	3	5	0	0	0	0	0
528	40248	16	8	4	7	5444	6012	1
0	0	0	0	0	0	0	0	0
938	100728	42	25	25	75	28470	27749	0
1245	84267	36	21	26	52	61849	47555	0
81	7131	4	0	0	0	0	0	1
257	8812	13	0	4	1	2179	1336	0
891	63952	22	15	17	49	8019	11017	1
1114	120111	47	47	21	69	39644	55184	0
1079	94127	18	17	22	56	23494	43485	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158673&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158673&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158673&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	5 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Goodness of Fit
Correlation	0.8517
R-squared	0.7254
RMSE	15530.6138

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8517 \tabularnewline
R-squared & 0.7254 \tabularnewline
RMSE & 15530.6138 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158673&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8517[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7254[/C][/ROW]
[ROW][C]RMSE[/C][C]15530.6138[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158673&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158673&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	0.8517
R-squared	0.7254
RMSE	15530.6138

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	23975	43900.8679245283	-19925.8679245283
2	85634	64518.9642857143	21115.0357142857
3	1929	1502.94444444444	426.055555555556
4	36294	43900.8679245283	-7606.8679245283
5	72255	110593.714285714	-38338.7142857143
6	189748	110593.714285714	79154.2857142857
7	61834	43900.8679245283	17933.1320754717
8	68167	43900.8679245283	24266.1320754717
9	38462	43900.8679245283	-5438.8679245283
10	101219	110593.714285714	-9374.71428571429
11	43270	43900.8679245283	-630.867924528298
12	76183	64518.9642857143	11664.0357142857
13	31476	28543.25	2932.75
14	62157	64518.9642857143	-2361.96428571428
15	46261	43900.8679245283	2360.1320754717
16	50063	43900.8679245283	6162.1320754717
17	64483	58629.4	5853.6
18	2341	1502.94444444444	838.055555555556
19	48149	43900.8679245283	4248.1320754717
20	12743	9224.875	3518.125
21	18743	28543.25	-9800.25
22	97057	64518.9642857143	32538.0357142857
23	17675	64518.9642857143	-46843.9642857143
24	33106	43900.8679245283	-10794.8679245283
25	53311	43900.8679245283	9410.1320754717
26	42754	43900.8679245283	-1146.8679245283
27	59056	64518.9642857143	-5462.96428571428
28	101621	64518.9642857143	37102.0357142857
29	118120	110593.714285714	7526.28571428571
30	79572	64518.9642857143	15053.0357142857
31	42744	43900.8679245283	-1156.8679245283
32	65931	64518.9642857143	1412.03571428572
33	38575	58629.4	-20054.4
34	28795	64518.9642857143	-35723.9642857143
35	94440	64518.9642857143	29921.0357142857
36	0	1502.94444444444	-1502.94444444444
37	38229	64518.9642857143	-26289.9642857143
38	31972	28543.25	3428.75
39	40071	43900.8679245283	-3829.8679245283
40	132480	110593.714285714	21886.2857142857
41	62797	64518.9642857143	-1721.96428571428
42	40429	43900.8679245283	-3471.8679245283
43	45545	58629.4	-13084.4
44	57568	64518.9642857143	-6950.96428571428
45	39019	43900.8679245283	-4881.8679245283
46	53866	43900.8679245283	9965.1320754717
47	38345	28543.25	9801.75
48	50210	64518.9642857143	-14308.9642857143
49	80947	64518.9642857143	16428.0357142857
50	43461	43900.8679245283	-439.867924528298
51	14812	9224.875	5587.125
52	37819	43900.8679245283	-6081.8679245283
53	102738	110593.714285714	-7855.71428571429
54	54509	64518.9642857143	-10009.9642857143
55	62956	64518.9642857143	-1562.96428571428
56	55411	43900.8679245283	11510.1320754717
57	50611	64518.9642857143	-13907.9642857143
58	26692	28543.25	-1851.25
59	60056	43900.8679245283	16155.1320754717
60	25155	64518.9642857143	-39363.9642857143
61	42840	43900.8679245283	-1060.8679245283
62	39358	43900.8679245283	-4542.8679245283
63	47241	43900.8679245283	3340.1320754717
64	49611	43900.8679245283	5710.1320754717
65	41833	43900.8679245283	-2067.8679245283
66	48930	43900.8679245283	5029.1320754717
67	110600	64518.9642857143	46081.0357142857
68	52235	43900.8679245283	8334.1320754717
69	53986	58629.4	-4643.4
70	4105	1502.94444444444	2602.05555555556
71	59331	64518.9642857143	-5187.96428571428
72	47796	43900.8679245283	3895.1320754717
73	38302	43900.8679245283	-5598.8679245283
74	14063	43900.8679245283	-29837.8679245283
75	54414	58629.4	-4215.4
76	9903	1502.94444444444	8400.05555555555
77	53987	43900.8679245283	10086.1320754717
78	88937	58629.4	30307.6
79	21928	28543.25	-6615.25
80	29487	28543.25	943.75
81	35334	28543.25	6790.75
82	57596	110593.714285714	-52997.7142857143
83	29750	28543.25	1206.75
84	41029	43900.8679245283	-2871.8679245283
85	12416	28543.25	-16127.25
86	51158	43900.8679245283	7257.1320754717
87	79935	64518.9642857143	15416.0357142857
88	26552	28543.25	-1991.25
89	25807	28543.25	-2736.25
90	50620	43900.8679245283	6719.1320754717
91	61467	58629.4	2837.6
92	65292	58629.4	6662.6
93	55516	43900.8679245283	11615.1320754717
94	42006	43900.8679245283	-1894.8679245283
95	26273	28543.25	-2270.25
96	90248	64518.9642857143	25729.0357142857
97	61476	64518.9642857143	-3042.96428571428
98	9604	9224.875	379.125
99	45108	64518.9642857143	-19410.9642857143
100	47232	43900.8679245283	3331.1320754717
101	3439	9224.875	-5785.875
102	30553	28543.25	2009.75
103	24751	43900.8679245283	-19149.8679245283
104	34458	43900.8679245283	-9442.8679245283
105	24649	28543.25	-3894.25
106	2342	1502.94444444444	839.055555555556
107	52739	43900.8679245283	8838.1320754717
108	6245	9224.875	-2979.875
109	0	1502.94444444444	-1502.94444444444
110	35381	28543.25	6837.75
111	19595	28543.25	-8948.25
112	50848	43900.8679245283	6947.1320754717
113	39443	43900.8679245283	-4457.8679245283
114	27023	28543.25	-1520.25
115	0	1502.94444444444	-1502.94444444444
116	0	1502.94444444444	-1502.94444444444
117	61022	64518.9642857143	-3496.96428571428
118	63528	58629.4	4898.6
119	34835	43900.8679245283	-9065.8679245283
120	37172	43900.8679245283	-6728.8679245283
121	13	1502.94444444444	-1489.94444444444
122	62548	43900.8679245283	18647.1320754717
123	31334	28543.25	2790.75
124	20839	43900.8679245283	-23061.8679245283
125	5084	1502.94444444444	3581.05555555556
126	9927	9224.875	702.125
127	53229	43900.8679245283	9328.1320754717
128	29877	43900.8679245283	-14023.8679245283
129	37310	43900.8679245283	-6590.8679245283
130	0	1502.94444444444	-1502.94444444444
131	0	1502.94444444444	-1502.94444444444
132	50067	58629.4	-8562.4
133	0	1502.94444444444	-1502.94444444444
134	47708	64518.9642857143	-16810.9642857143
135	0	1502.94444444444	-1502.94444444444
136	6012	9224.875	-3212.875
137	0	1502.94444444444	-1502.94444444444
138	27749	43900.8679245283	-16151.8679245283
139	47555	28543.25	19011.75
140	0	1502.94444444444	-1502.94444444444
141	1336	1502.94444444444	-166.944444444444
142	11017	9224.875	1792.125
143	55184	43900.8679245283	11283.1320754717
144	43485	43900.8679245283	-415.867924528298

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 23975 & 43900.8679245283 & -19925.8679245283 \tabularnewline
2 & 85634 & 64518.9642857143 & 21115.0357142857 \tabularnewline
3 & 1929 & 1502.94444444444 & 426.055555555556 \tabularnewline
4 & 36294 & 43900.8679245283 & -7606.8679245283 \tabularnewline
5 & 72255 & 110593.714285714 & -38338.7142857143 \tabularnewline
6 & 189748 & 110593.714285714 & 79154.2857142857 \tabularnewline
7 & 61834 & 43900.8679245283 & 17933.1320754717 \tabularnewline
8 & 68167 & 43900.8679245283 & 24266.1320754717 \tabularnewline
9 & 38462 & 43900.8679245283 & -5438.8679245283 \tabularnewline
10 & 101219 & 110593.714285714 & -9374.71428571429 \tabularnewline
11 & 43270 & 43900.8679245283 & -630.867924528298 \tabularnewline
12 & 76183 & 64518.9642857143 & 11664.0357142857 \tabularnewline
13 & 31476 & 28543.25 & 2932.75 \tabularnewline
14 & 62157 & 64518.9642857143 & -2361.96428571428 \tabularnewline
15 & 46261 & 43900.8679245283 & 2360.1320754717 \tabularnewline
16 & 50063 & 43900.8679245283 & 6162.1320754717 \tabularnewline
17 & 64483 & 58629.4 & 5853.6 \tabularnewline
18 & 2341 & 1502.94444444444 & 838.055555555556 \tabularnewline
19 & 48149 & 43900.8679245283 & 4248.1320754717 \tabularnewline
20 & 12743 & 9224.875 & 3518.125 \tabularnewline
21 & 18743 & 28543.25 & -9800.25 \tabularnewline
22 & 97057 & 64518.9642857143 & 32538.0357142857 \tabularnewline
23 & 17675 & 64518.9642857143 & -46843.9642857143 \tabularnewline
24 & 33106 & 43900.8679245283 & -10794.8679245283 \tabularnewline
25 & 53311 & 43900.8679245283 & 9410.1320754717 \tabularnewline
26 & 42754 & 43900.8679245283 & -1146.8679245283 \tabularnewline
27 & 59056 & 64518.9642857143 & -5462.96428571428 \tabularnewline
28 & 101621 & 64518.9642857143 & 37102.0357142857 \tabularnewline
29 & 118120 & 110593.714285714 & 7526.28571428571 \tabularnewline
30 & 79572 & 64518.9642857143 & 15053.0357142857 \tabularnewline
31 & 42744 & 43900.8679245283 & -1156.8679245283 \tabularnewline
32 & 65931 & 64518.9642857143 & 1412.03571428572 \tabularnewline
33 & 38575 & 58629.4 & -20054.4 \tabularnewline
34 & 28795 & 64518.9642857143 & -35723.9642857143 \tabularnewline
35 & 94440 & 64518.9642857143 & 29921.0357142857 \tabularnewline
36 & 0 & 1502.94444444444 & -1502.94444444444 \tabularnewline
37 & 38229 & 64518.9642857143 & -26289.9642857143 \tabularnewline
38 & 31972 & 28543.25 & 3428.75 \tabularnewline
39 & 40071 & 43900.8679245283 & -3829.8679245283 \tabularnewline
40 & 132480 & 110593.714285714 & 21886.2857142857 \tabularnewline
41 & 62797 & 64518.9642857143 & -1721.96428571428 \tabularnewline
42 & 40429 & 43900.8679245283 & -3471.8679245283 \tabularnewline
43 & 45545 & 58629.4 & -13084.4 \tabularnewline
44 & 57568 & 64518.9642857143 & -6950.96428571428 \tabularnewline
45 & 39019 & 43900.8679245283 & -4881.8679245283 \tabularnewline
46 & 53866 & 43900.8679245283 & 9965.1320754717 \tabularnewline
47 & 38345 & 28543.25 & 9801.75 \tabularnewline
48 & 50210 & 64518.9642857143 & -14308.9642857143 \tabularnewline
49 & 80947 & 64518.9642857143 & 16428.0357142857 \tabularnewline
50 & 43461 & 43900.8679245283 & -439.867924528298 \tabularnewline
51 & 14812 & 9224.875 & 5587.125 \tabularnewline
52 & 37819 & 43900.8679245283 & -6081.8679245283 \tabularnewline
53 & 102738 & 110593.714285714 & -7855.71428571429 \tabularnewline
54 & 54509 & 64518.9642857143 & -10009.9642857143 \tabularnewline
55 & 62956 & 64518.9642857143 & -1562.96428571428 \tabularnewline
56 & 55411 & 43900.8679245283 & 11510.1320754717 \tabularnewline
57 & 50611 & 64518.9642857143 & -13907.9642857143 \tabularnewline
58 & 26692 & 28543.25 & -1851.25 \tabularnewline
59 & 60056 & 43900.8679245283 & 16155.1320754717 \tabularnewline
60 & 25155 & 64518.9642857143 & -39363.9642857143 \tabularnewline
61 & 42840 & 43900.8679245283 & -1060.8679245283 \tabularnewline
62 & 39358 & 43900.8679245283 & -4542.8679245283 \tabularnewline
63 & 47241 & 43900.8679245283 & 3340.1320754717 \tabularnewline
64 & 49611 & 43900.8679245283 & 5710.1320754717 \tabularnewline
65 & 41833 & 43900.8679245283 & -2067.8679245283 \tabularnewline
66 & 48930 & 43900.8679245283 & 5029.1320754717 \tabularnewline
67 & 110600 & 64518.9642857143 & 46081.0357142857 \tabularnewline
68 & 52235 & 43900.8679245283 & 8334.1320754717 \tabularnewline
69 & 53986 & 58629.4 & -4643.4 \tabularnewline
70 & 4105 & 1502.94444444444 & 2602.05555555556 \tabularnewline
71 & 59331 & 64518.9642857143 & -5187.96428571428 \tabularnewline
72 & 47796 & 43900.8679245283 & 3895.1320754717 \tabularnewline
73 & 38302 & 43900.8679245283 & -5598.8679245283 \tabularnewline
74 & 14063 & 43900.8679245283 & -29837.8679245283 \tabularnewline
75 & 54414 & 58629.4 & -4215.4 \tabularnewline
76 & 9903 & 1502.94444444444 & 8400.05555555555 \tabularnewline
77 & 53987 & 43900.8679245283 & 10086.1320754717 \tabularnewline
78 & 88937 & 58629.4 & 30307.6 \tabularnewline
79 & 21928 & 28543.25 & -6615.25 \tabularnewline
80 & 29487 & 28543.25 & 943.75 \tabularnewline
81 & 35334 & 28543.25 & 6790.75 \tabularnewline
82 & 57596 & 110593.714285714 & -52997.7142857143 \tabularnewline
83 & 29750 & 28543.25 & 1206.75 \tabularnewline
84 & 41029 & 43900.8679245283 & -2871.8679245283 \tabularnewline
85 & 12416 & 28543.25 & -16127.25 \tabularnewline
86 & 51158 & 43900.8679245283 & 7257.1320754717 \tabularnewline
87 & 79935 & 64518.9642857143 & 15416.0357142857 \tabularnewline
88 & 26552 & 28543.25 & -1991.25 \tabularnewline
89 & 25807 & 28543.25 & -2736.25 \tabularnewline
90 & 50620 & 43900.8679245283 & 6719.1320754717 \tabularnewline
91 & 61467 & 58629.4 & 2837.6 \tabularnewline
92 & 65292 & 58629.4 & 6662.6 \tabularnewline
93 & 55516 & 43900.8679245283 & 11615.1320754717 \tabularnewline
94 & 42006 & 43900.8679245283 & -1894.8679245283 \tabularnewline
95 & 26273 & 28543.25 & -2270.25 \tabularnewline
96 & 90248 & 64518.9642857143 & 25729.0357142857 \tabularnewline
97 & 61476 & 64518.9642857143 & -3042.96428571428 \tabularnewline
98 & 9604 & 9224.875 & 379.125 \tabularnewline
99 & 45108 & 64518.9642857143 & -19410.9642857143 \tabularnewline
100 & 47232 & 43900.8679245283 & 3331.1320754717 \tabularnewline
101 & 3439 & 9224.875 & -5785.875 \tabularnewline
102 & 30553 & 28543.25 & 2009.75 \tabularnewline
103 & 24751 & 43900.8679245283 & -19149.8679245283 \tabularnewline
104 & 34458 & 43900.8679245283 & -9442.8679245283 \tabularnewline
105 & 24649 & 28543.25 & -3894.25 \tabularnewline
106 & 2342 & 1502.94444444444 & 839.055555555556 \tabularnewline
107 & 52739 & 43900.8679245283 & 8838.1320754717 \tabularnewline
108 & 6245 & 9224.875 & -2979.875 \tabularnewline
109 & 0 & 1502.94444444444 & -1502.94444444444 \tabularnewline
110 & 35381 & 28543.25 & 6837.75 \tabularnewline
111 & 19595 & 28543.25 & -8948.25 \tabularnewline
112 & 50848 & 43900.8679245283 & 6947.1320754717 \tabularnewline
113 & 39443 & 43900.8679245283 & -4457.8679245283 \tabularnewline
114 & 27023 & 28543.25 & -1520.25 \tabularnewline
115 & 0 & 1502.94444444444 & -1502.94444444444 \tabularnewline
116 & 0 & 1502.94444444444 & -1502.94444444444 \tabularnewline
117 & 61022 & 64518.9642857143 & -3496.96428571428 \tabularnewline
118 & 63528 & 58629.4 & 4898.6 \tabularnewline
119 & 34835 & 43900.8679245283 & -9065.8679245283 \tabularnewline
120 & 37172 & 43900.8679245283 & -6728.8679245283 \tabularnewline
121 & 13 & 1502.94444444444 & -1489.94444444444 \tabularnewline
122 & 62548 & 43900.8679245283 & 18647.1320754717 \tabularnewline
123 & 31334 & 28543.25 & 2790.75 \tabularnewline
124 & 20839 & 43900.8679245283 & -23061.8679245283 \tabularnewline
125 & 5084 & 1502.94444444444 & 3581.05555555556 \tabularnewline
126 & 9927 & 9224.875 & 702.125 \tabularnewline
127 & 53229 & 43900.8679245283 & 9328.1320754717 \tabularnewline
128 & 29877 & 43900.8679245283 & -14023.8679245283 \tabularnewline
129 & 37310 & 43900.8679245283 & -6590.8679245283 \tabularnewline
130 & 0 & 1502.94444444444 & -1502.94444444444 \tabularnewline
131 & 0 & 1502.94444444444 & -1502.94444444444 \tabularnewline
132 & 50067 & 58629.4 & -8562.4 \tabularnewline
133 & 0 & 1502.94444444444 & -1502.94444444444 \tabularnewline
134 & 47708 & 64518.9642857143 & -16810.9642857143 \tabularnewline
135 & 0 & 1502.94444444444 & -1502.94444444444 \tabularnewline
136 & 6012 & 9224.875 & -3212.875 \tabularnewline
137 & 0 & 1502.94444444444 & -1502.94444444444 \tabularnewline
138 & 27749 & 43900.8679245283 & -16151.8679245283 \tabularnewline
139 & 47555 & 28543.25 & 19011.75 \tabularnewline
140 & 0 & 1502.94444444444 & -1502.94444444444 \tabularnewline
141 & 1336 & 1502.94444444444 & -166.944444444444 \tabularnewline
142 & 11017 & 9224.875 & 1792.125 \tabularnewline
143 & 55184 & 43900.8679245283 & 11283.1320754717 \tabularnewline
144 & 43485 & 43900.8679245283 & -415.867924528298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158673&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]23975[/C][C]43900.8679245283[/C][C]-19925.8679245283[/C][/ROW]
[ROW][C]2[/C][C]85634[/C][C]64518.9642857143[/C][C]21115.0357142857[/C][/ROW]
[ROW][C]3[/C][C]1929[/C][C]1502.94444444444[/C][C]426.055555555556[/C][/ROW]
[ROW][C]4[/C][C]36294[/C][C]43900.8679245283[/C][C]-7606.8679245283[/C][/ROW]
[ROW][C]5[/C][C]72255[/C][C]110593.714285714[/C][C]-38338.7142857143[/C][/ROW]
[ROW][C]6[/C][C]189748[/C][C]110593.714285714[/C][C]79154.2857142857[/C][/ROW]
[ROW][C]7[/C][C]61834[/C][C]43900.8679245283[/C][C]17933.1320754717[/C][/ROW]
[ROW][C]8[/C][C]68167[/C][C]43900.8679245283[/C][C]24266.1320754717[/C][/ROW]
[ROW][C]9[/C][C]38462[/C][C]43900.8679245283[/C][C]-5438.8679245283[/C][/ROW]
[ROW][C]10[/C][C]101219[/C][C]110593.714285714[/C][C]-9374.71428571429[/C][/ROW]
[ROW][C]11[/C][C]43270[/C][C]43900.8679245283[/C][C]-630.867924528298[/C][/ROW]
[ROW][C]12[/C][C]76183[/C][C]64518.9642857143[/C][C]11664.0357142857[/C][/ROW]
[ROW][C]13[/C][C]31476[/C][C]28543.25[/C][C]2932.75[/C][/ROW]
[ROW][C]14[/C][C]62157[/C][C]64518.9642857143[/C][C]-2361.96428571428[/C][/ROW]
[ROW][C]15[/C][C]46261[/C][C]43900.8679245283[/C][C]2360.1320754717[/C][/ROW]
[ROW][C]16[/C][C]50063[/C][C]43900.8679245283[/C][C]6162.1320754717[/C][/ROW]
[ROW][C]17[/C][C]64483[/C][C]58629.4[/C][C]5853.6[/C][/ROW]
[ROW][C]18[/C][C]2341[/C][C]1502.94444444444[/C][C]838.055555555556[/C][/ROW]
[ROW][C]19[/C][C]48149[/C][C]43900.8679245283[/C][C]4248.1320754717[/C][/ROW]
[ROW][C]20[/C][C]12743[/C][C]9224.875[/C][C]3518.125[/C][/ROW]
[ROW][C]21[/C][C]18743[/C][C]28543.25[/C][C]-9800.25[/C][/ROW]
[ROW][C]22[/C][C]97057[/C][C]64518.9642857143[/C][C]32538.0357142857[/C][/ROW]
[ROW][C]23[/C][C]17675[/C][C]64518.9642857143[/C][C]-46843.9642857143[/C][/ROW]
[ROW][C]24[/C][C]33106[/C][C]43900.8679245283[/C][C]-10794.8679245283[/C][/ROW]
[ROW][C]25[/C][C]53311[/C][C]43900.8679245283[/C][C]9410.1320754717[/C][/ROW]
[ROW][C]26[/C][C]42754[/C][C]43900.8679245283[/C][C]-1146.8679245283[/C][/ROW]
[ROW][C]27[/C][C]59056[/C][C]64518.9642857143[/C][C]-5462.96428571428[/C][/ROW]
[ROW][C]28[/C][C]101621[/C][C]64518.9642857143[/C][C]37102.0357142857[/C][/ROW]
[ROW][C]29[/C][C]118120[/C][C]110593.714285714[/C][C]7526.28571428571[/C][/ROW]
[ROW][C]30[/C][C]79572[/C][C]64518.9642857143[/C][C]15053.0357142857[/C][/ROW]
[ROW][C]31[/C][C]42744[/C][C]43900.8679245283[/C][C]-1156.8679245283[/C][/ROW]
[ROW][C]32[/C][C]65931[/C][C]64518.9642857143[/C][C]1412.03571428572[/C][/ROW]
[ROW][C]33[/C][C]38575[/C][C]58629.4[/C][C]-20054.4[/C][/ROW]
[ROW][C]34[/C][C]28795[/C][C]64518.9642857143[/C][C]-35723.9642857143[/C][/ROW]
[ROW][C]35[/C][C]94440[/C][C]64518.9642857143[/C][C]29921.0357142857[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]1502.94444444444[/C][C]-1502.94444444444[/C][/ROW]
[ROW][C]37[/C][C]38229[/C][C]64518.9642857143[/C][C]-26289.9642857143[/C][/ROW]
[ROW][C]38[/C][C]31972[/C][C]28543.25[/C][C]3428.75[/C][/ROW]
[ROW][C]39[/C][C]40071[/C][C]43900.8679245283[/C][C]-3829.8679245283[/C][/ROW]
[ROW][C]40[/C][C]132480[/C][C]110593.714285714[/C][C]21886.2857142857[/C][/ROW]
[ROW][C]41[/C][C]62797[/C][C]64518.9642857143[/C][C]-1721.96428571428[/C][/ROW]
[ROW][C]42[/C][C]40429[/C][C]43900.8679245283[/C][C]-3471.8679245283[/C][/ROW]
[ROW][C]43[/C][C]45545[/C][C]58629.4[/C][C]-13084.4[/C][/ROW]
[ROW][C]44[/C][C]57568[/C][C]64518.9642857143[/C][C]-6950.96428571428[/C][/ROW]
[ROW][C]45[/C][C]39019[/C][C]43900.8679245283[/C][C]-4881.8679245283[/C][/ROW]
[ROW][C]46[/C][C]53866[/C][C]43900.8679245283[/C][C]9965.1320754717[/C][/ROW]
[ROW][C]47[/C][C]38345[/C][C]28543.25[/C][C]9801.75[/C][/ROW]
[ROW][C]48[/C][C]50210[/C][C]64518.9642857143[/C][C]-14308.9642857143[/C][/ROW]
[ROW][C]49[/C][C]80947[/C][C]64518.9642857143[/C][C]16428.0357142857[/C][/ROW]
[ROW][C]50[/C][C]43461[/C][C]43900.8679245283[/C][C]-439.867924528298[/C][/ROW]
[ROW][C]51[/C][C]14812[/C][C]9224.875[/C][C]5587.125[/C][/ROW]
[ROW][C]52[/C][C]37819[/C][C]43900.8679245283[/C][C]-6081.8679245283[/C][/ROW]
[ROW][C]53[/C][C]102738[/C][C]110593.714285714[/C][C]-7855.71428571429[/C][/ROW]
[ROW][C]54[/C][C]54509[/C][C]64518.9642857143[/C][C]-10009.9642857143[/C][/ROW]
[ROW][C]55[/C][C]62956[/C][C]64518.9642857143[/C][C]-1562.96428571428[/C][/ROW]
[ROW][C]56[/C][C]55411[/C][C]43900.8679245283[/C][C]11510.1320754717[/C][/ROW]
[ROW][C]57[/C][C]50611[/C][C]64518.9642857143[/C][C]-13907.9642857143[/C][/ROW]
[ROW][C]58[/C][C]26692[/C][C]28543.25[/C][C]-1851.25[/C][/ROW]
[ROW][C]59[/C][C]60056[/C][C]43900.8679245283[/C][C]16155.1320754717[/C][/ROW]
[ROW][C]60[/C][C]25155[/C][C]64518.9642857143[/C][C]-39363.9642857143[/C][/ROW]
[ROW][C]61[/C][C]42840[/C][C]43900.8679245283[/C][C]-1060.8679245283[/C][/ROW]
[ROW][C]62[/C][C]39358[/C][C]43900.8679245283[/C][C]-4542.8679245283[/C][/ROW]
[ROW][C]63[/C][C]47241[/C][C]43900.8679245283[/C][C]3340.1320754717[/C][/ROW]
[ROW][C]64[/C][C]49611[/C][C]43900.8679245283[/C][C]5710.1320754717[/C][/ROW]
[ROW][C]65[/C][C]41833[/C][C]43900.8679245283[/C][C]-2067.8679245283[/C][/ROW]
[ROW][C]66[/C][C]48930[/C][C]43900.8679245283[/C][C]5029.1320754717[/C][/ROW]
[ROW][C]67[/C][C]110600[/C][C]64518.9642857143[/C][C]46081.0357142857[/C][/ROW]
[ROW][C]68[/C][C]52235[/C][C]43900.8679245283[/C][C]8334.1320754717[/C][/ROW]
[ROW][C]69[/C][C]53986[/C][C]58629.4[/C][C]-4643.4[/C][/ROW]
[ROW][C]70[/C][C]4105[/C][C]1502.94444444444[/C][C]2602.05555555556[/C][/ROW]
[ROW][C]71[/C][C]59331[/C][C]64518.9642857143[/C][C]-5187.96428571428[/C][/ROW]
[ROW][C]72[/C][C]47796[/C][C]43900.8679245283[/C][C]3895.1320754717[/C][/ROW]
[ROW][C]73[/C][C]38302[/C][C]43900.8679245283[/C][C]-5598.8679245283[/C][/ROW]
[ROW][C]74[/C][C]14063[/C][C]43900.8679245283[/C][C]-29837.8679245283[/C][/ROW]
[ROW][C]75[/C][C]54414[/C][C]58629.4[/C][C]-4215.4[/C][/ROW]
[ROW][C]76[/C][C]9903[/C][C]1502.94444444444[/C][C]8400.05555555555[/C][/ROW]
[ROW][C]77[/C][C]53987[/C][C]43900.8679245283[/C][C]10086.1320754717[/C][/ROW]
[ROW][C]78[/C][C]88937[/C][C]58629.4[/C][C]30307.6[/C][/ROW]
[ROW][C]79[/C][C]21928[/C][C]28543.25[/C][C]-6615.25[/C][/ROW]
[ROW][C]80[/C][C]29487[/C][C]28543.25[/C][C]943.75[/C][/ROW]
[ROW][C]81[/C][C]35334[/C][C]28543.25[/C][C]6790.75[/C][/ROW]
[ROW][C]82[/C][C]57596[/C][C]110593.714285714[/C][C]-52997.7142857143[/C][/ROW]
[ROW][C]83[/C][C]29750[/C][C]28543.25[/C][C]1206.75[/C][/ROW]
[ROW][C]84[/C][C]41029[/C][C]43900.8679245283[/C][C]-2871.8679245283[/C][/ROW]
[ROW][C]85[/C][C]12416[/C][C]28543.25[/C][C]-16127.25[/C][/ROW]
[ROW][C]86[/C][C]51158[/C][C]43900.8679245283[/C][C]7257.1320754717[/C][/ROW]
[ROW][C]87[/C][C]79935[/C][C]64518.9642857143[/C][C]15416.0357142857[/C][/ROW]
[ROW][C]88[/C][C]26552[/C][C]28543.25[/C][C]-1991.25[/C][/ROW]
[ROW][C]89[/C][C]25807[/C][C]28543.25[/C][C]-2736.25[/C][/ROW]
[ROW][C]90[/C][C]50620[/C][C]43900.8679245283[/C][C]6719.1320754717[/C][/ROW]
[ROW][C]91[/C][C]61467[/C][C]58629.4[/C][C]2837.6[/C][/ROW]
[ROW][C]92[/C][C]65292[/C][C]58629.4[/C][C]6662.6[/C][/ROW]
[ROW][C]93[/C][C]55516[/C][C]43900.8679245283[/C][C]11615.1320754717[/C][/ROW]
[ROW][C]94[/C][C]42006[/C][C]43900.8679245283[/C][C]-1894.8679245283[/C][/ROW]
[ROW][C]95[/C][C]26273[/C][C]28543.25[/C][C]-2270.25[/C][/ROW]
[ROW][C]96[/C][C]90248[/C][C]64518.9642857143[/C][C]25729.0357142857[/C][/ROW]
[ROW][C]97[/C][C]61476[/C][C]64518.9642857143[/C][C]-3042.96428571428[/C][/ROW]
[ROW][C]98[/C][C]9604[/C][C]9224.875[/C][C]379.125[/C][/ROW]
[ROW][C]99[/C][C]45108[/C][C]64518.9642857143[/C][C]-19410.9642857143[/C][/ROW]
[ROW][C]100[/C][C]47232[/C][C]43900.8679245283[/C][C]3331.1320754717[/C][/ROW]
[ROW][C]101[/C][C]3439[/C][C]9224.875[/C][C]-5785.875[/C][/ROW]
[ROW][C]102[/C][C]30553[/C][C]28543.25[/C][C]2009.75[/C][/ROW]
[ROW][C]103[/C][C]24751[/C][C]43900.8679245283[/C][C]-19149.8679245283[/C][/ROW]
[ROW][C]104[/C][C]34458[/C][C]43900.8679245283[/C][C]-9442.8679245283[/C][/ROW]
[ROW][C]105[/C][C]24649[/C][C]28543.25[/C][C]-3894.25[/C][/ROW]
[ROW][C]106[/C][C]2342[/C][C]1502.94444444444[/C][C]839.055555555556[/C][/ROW]
[ROW][C]107[/C][C]52739[/C][C]43900.8679245283[/C][C]8838.1320754717[/C][/ROW]
[ROW][C]108[/C][C]6245[/C][C]9224.875[/C][C]-2979.875[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]1502.94444444444[/C][C]-1502.94444444444[/C][/ROW]
[ROW][C]110[/C][C]35381[/C][C]28543.25[/C][C]6837.75[/C][/ROW]
[ROW][C]111[/C][C]19595[/C][C]28543.25[/C][C]-8948.25[/C][/ROW]
[ROW][C]112[/C][C]50848[/C][C]43900.8679245283[/C][C]6947.1320754717[/C][/ROW]
[ROW][C]113[/C][C]39443[/C][C]43900.8679245283[/C][C]-4457.8679245283[/C][/ROW]
[ROW][C]114[/C][C]27023[/C][C]28543.25[/C][C]-1520.25[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]1502.94444444444[/C][C]-1502.94444444444[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]1502.94444444444[/C][C]-1502.94444444444[/C][/ROW]
[ROW][C]117[/C][C]61022[/C][C]64518.9642857143[/C][C]-3496.96428571428[/C][/ROW]
[ROW][C]118[/C][C]63528[/C][C]58629.4[/C][C]4898.6[/C][/ROW]
[ROW][C]119[/C][C]34835[/C][C]43900.8679245283[/C][C]-9065.8679245283[/C][/ROW]
[ROW][C]120[/C][C]37172[/C][C]43900.8679245283[/C][C]-6728.8679245283[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]1502.94444444444[/C][C]-1489.94444444444[/C][/ROW]
[ROW][C]122[/C][C]62548[/C][C]43900.8679245283[/C][C]18647.1320754717[/C][/ROW]
[ROW][C]123[/C][C]31334[/C][C]28543.25[/C][C]2790.75[/C][/ROW]
[ROW][C]124[/C][C]20839[/C][C]43900.8679245283[/C][C]-23061.8679245283[/C][/ROW]
[ROW][C]125[/C][C]5084[/C][C]1502.94444444444[/C][C]3581.05555555556[/C][/ROW]
[ROW][C]126[/C][C]9927[/C][C]9224.875[/C][C]702.125[/C][/ROW]
[ROW][C]127[/C][C]53229[/C][C]43900.8679245283[/C][C]9328.1320754717[/C][/ROW]
[ROW][C]128[/C][C]29877[/C][C]43900.8679245283[/C][C]-14023.8679245283[/C][/ROW]
[ROW][C]129[/C][C]37310[/C][C]43900.8679245283[/C][C]-6590.8679245283[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]1502.94444444444[/C][C]-1502.94444444444[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]1502.94444444444[/C][C]-1502.94444444444[/C][/ROW]
[ROW][C]132[/C][C]50067[/C][C]58629.4[/C][C]-8562.4[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]1502.94444444444[/C][C]-1502.94444444444[/C][/ROW]
[ROW][C]134[/C][C]47708[/C][C]64518.9642857143[/C][C]-16810.9642857143[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]1502.94444444444[/C][C]-1502.94444444444[/C][/ROW]
[ROW][C]136[/C][C]6012[/C][C]9224.875[/C][C]-3212.875[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]1502.94444444444[/C][C]-1502.94444444444[/C][/ROW]
[ROW][C]138[/C][C]27749[/C][C]43900.8679245283[/C][C]-16151.8679245283[/C][/ROW]
[ROW][C]139[/C][C]47555[/C][C]28543.25[/C][C]19011.75[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]1502.94444444444[/C][C]-1502.94444444444[/C][/ROW]
[ROW][C]141[/C][C]1336[/C][C]1502.94444444444[/C][C]-166.944444444444[/C][/ROW]
[ROW][C]142[/C][C]11017[/C][C]9224.875[/C][C]1792.125[/C][/ROW]
[ROW][C]143[/C][C]55184[/C][C]43900.8679245283[/C][C]11283.1320754717[/C][/ROW]
[ROW][C]144[/C][C]43485[/C][C]43900.8679245283[/C][C]-415.867924528298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158673&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158673&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	23975	43900.8679245283	-19925.8679245283
2	85634	64518.9642857143	21115.0357142857
3	1929	1502.94444444444	426.055555555556
4	36294	43900.8679245283	-7606.8679245283
5	72255	110593.714285714	-38338.7142857143
6	189748	110593.714285714	79154.2857142857
7	61834	43900.8679245283	17933.1320754717
8	68167	43900.8679245283	24266.1320754717
9	38462	43900.8679245283	-5438.8679245283
10	101219	110593.714285714	-9374.71428571429
11	43270	43900.8679245283	-630.867924528298
12	76183	64518.9642857143	11664.0357142857
13	31476	28543.25	2932.75
14	62157	64518.9642857143	-2361.96428571428
15	46261	43900.8679245283	2360.1320754717
16	50063	43900.8679245283	6162.1320754717
17	64483	58629.4	5853.6
18	2341	1502.94444444444	838.055555555556
19	48149	43900.8679245283	4248.1320754717
20	12743	9224.875	3518.125
21	18743	28543.25	-9800.25
22	97057	64518.9642857143	32538.0357142857
23	17675	64518.9642857143	-46843.9642857143
24	33106	43900.8679245283	-10794.8679245283
25	53311	43900.8679245283	9410.1320754717
26	42754	43900.8679245283	-1146.8679245283
27	59056	64518.9642857143	-5462.96428571428
28	101621	64518.9642857143	37102.0357142857
29	118120	110593.714285714	7526.28571428571
30	79572	64518.9642857143	15053.0357142857
31	42744	43900.8679245283	-1156.8679245283
32	65931	64518.9642857143	1412.03571428572
33	38575	58629.4	-20054.4
34	28795	64518.9642857143	-35723.9642857143
35	94440	64518.9642857143	29921.0357142857
36	0	1502.94444444444	-1502.94444444444
37	38229	64518.9642857143	-26289.9642857143
38	31972	28543.25	3428.75
39	40071	43900.8679245283	-3829.8679245283
40	132480	110593.714285714	21886.2857142857
41	62797	64518.9642857143	-1721.96428571428
42	40429	43900.8679245283	-3471.8679245283
43	45545	58629.4	-13084.4
44	57568	64518.9642857143	-6950.96428571428
45	39019	43900.8679245283	-4881.8679245283
46	53866	43900.8679245283	9965.1320754717
47	38345	28543.25	9801.75
48	50210	64518.9642857143	-14308.9642857143
49	80947	64518.9642857143	16428.0357142857
50	43461	43900.8679245283	-439.867924528298
51	14812	9224.875	5587.125
52	37819	43900.8679245283	-6081.8679245283
53	102738	110593.714285714	-7855.71428571429
54	54509	64518.9642857143	-10009.9642857143
55	62956	64518.9642857143	-1562.96428571428
56	55411	43900.8679245283	11510.1320754717
57	50611	64518.9642857143	-13907.9642857143
58	26692	28543.25	-1851.25
59	60056	43900.8679245283	16155.1320754717
60	25155	64518.9642857143	-39363.9642857143
61	42840	43900.8679245283	-1060.8679245283
62	39358	43900.8679245283	-4542.8679245283
63	47241	43900.8679245283	3340.1320754717
64	49611	43900.8679245283	5710.1320754717
65	41833	43900.8679245283	-2067.8679245283
66	48930	43900.8679245283	5029.1320754717
67	110600	64518.9642857143	46081.0357142857
68	52235	43900.8679245283	8334.1320754717
69	53986	58629.4	-4643.4
70	4105	1502.94444444444	2602.05555555556
71	59331	64518.9642857143	-5187.96428571428
72	47796	43900.8679245283	3895.1320754717
73	38302	43900.8679245283	-5598.8679245283
74	14063	43900.8679245283	-29837.8679245283
75	54414	58629.4	-4215.4
76	9903	1502.94444444444	8400.05555555555
77	53987	43900.8679245283	10086.1320754717
78	88937	58629.4	30307.6
79	21928	28543.25	-6615.25
80	29487	28543.25	943.75
81	35334	28543.25	6790.75
82	57596	110593.714285714	-52997.7142857143
83	29750	28543.25	1206.75
84	41029	43900.8679245283	-2871.8679245283
85	12416	28543.25	-16127.25
86	51158	43900.8679245283	7257.1320754717
87	79935	64518.9642857143	15416.0357142857
88	26552	28543.25	-1991.25
89	25807	28543.25	-2736.25
90	50620	43900.8679245283	6719.1320754717
91	61467	58629.4	2837.6
92	65292	58629.4	6662.6
93	55516	43900.8679245283	11615.1320754717
94	42006	43900.8679245283	-1894.8679245283
95	26273	28543.25	-2270.25
96	90248	64518.9642857143	25729.0357142857
97	61476	64518.9642857143	-3042.96428571428
98	9604	9224.875	379.125
99	45108	64518.9642857143	-19410.9642857143
100	47232	43900.8679245283	3331.1320754717
101	3439	9224.875	-5785.875
102	30553	28543.25	2009.75
103	24751	43900.8679245283	-19149.8679245283
104	34458	43900.8679245283	-9442.8679245283
105	24649	28543.25	-3894.25
106	2342	1502.94444444444	839.055555555556
107	52739	43900.8679245283	8838.1320754717
108	6245	9224.875	-2979.875
109	0	1502.94444444444	-1502.94444444444
110	35381	28543.25	6837.75
111	19595	28543.25	-8948.25
112	50848	43900.8679245283	6947.1320754717
113	39443	43900.8679245283	-4457.8679245283
114	27023	28543.25	-1520.25
115	0	1502.94444444444	-1502.94444444444
116	0	1502.94444444444	-1502.94444444444
117	61022	64518.9642857143	-3496.96428571428
118	63528	58629.4	4898.6
119	34835	43900.8679245283	-9065.8679245283
120	37172	43900.8679245283	-6728.8679245283
121	13	1502.94444444444	-1489.94444444444
122	62548	43900.8679245283	18647.1320754717
123	31334	28543.25	2790.75
124	20839	43900.8679245283	-23061.8679245283
125	5084	1502.94444444444	3581.05555555556
126	9927	9224.875	702.125
127	53229	43900.8679245283	9328.1320754717
128	29877	43900.8679245283	-14023.8679245283
129	37310	43900.8679245283	-6590.8679245283
130	0	1502.94444444444	-1502.94444444444
131	0	1502.94444444444	-1502.94444444444
132	50067	58629.4	-8562.4
133	0	1502.94444444444	-1502.94444444444
134	47708	64518.9642857143	-16810.9642857143
135	0	1502.94444444444	-1502.94444444444
136	6012	9224.875	-3212.875
137	0	1502.94444444444	-1502.94444444444
138	27749	43900.8679245283	-16151.8679245283
139	47555	28543.25	19011.75
140	0	1502.94444444444	-1502.94444444444
141	1336	1502.94444444444	-166.944444444444
142	11017	9224.875	1792.125
143	55184	43900.8679245283	11283.1320754717
144	43485	43900.8679245283	-415.867924528298

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 = 8 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 8 ; 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