Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationThu, 06 Dec 2012 08:06:25 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/06/t13547993303mtrnzep6536j22.htm/, Retrieved Thu, 28 Mar 2024 16:01:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197065, Retrieved Thu, 28 Mar 2024 16:01:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [Workshop 10 - Pea...] [2012-12-06 12:40:18] [498ff5d3288f0a3191251bab12f09e42]
- RMP       [Recursive Partitioning (Regression Trees)] [Workshop 10 - Rec...] [2012-12-06 13:06:25] [88970af05b38e2e8b1d3faaed6004b57] [Current]
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Dataseries X:
1418	210907	56	396	3	115	112285
869	120982	56	297	4	109	84786
1530	176508	54	559	12	146	83123
2172	179321	89	967	2	116	101193
901	123185	40	270	1	68	38361
463	52746	25	143	3	101	68504
3201	385534	92	1562	0	96	119182
371	33170	18	109	0	67	22807
1583	149061	44	656	5	100	116174
1439	165446	33	511	0	93	57635
1764	237213	84	655	0	140	66198
1495	173326	88	465	7	166	71701
1373	133131	55	525	7	99	57793
2187	258873	60	885	3	139	80444
1491	180083	66	497	9	130	53855
4041	324799	154	1436	0	181	97668
1706	230964	53	612	4	116	133824
2152	236785	119	865	3	116	101481
1036	135473	41	385	0	88	99645
1882	202925	61	567	7	139	114789
1929	215147	58	639	0	135	99052
2242	344297	75	963	1	108	67654
1220	153935	33	398	5	89	65553
1289	132943	40	410	7	156	97500
2515	174724	92	966	0	129	69112
2147	174415	100	801	0	118	82753
2352	225548	112	892	5	118	85323
1638	223632	73	513	0	125	72654
1222	124817	40	469	0	95	30727
1812	221698	45	683	0	126	77873
1677	210767	60	643	3	135	117478
1579	170266	62	535	4	154	74007
1731	260561	75	625	1	165	90183
807	84853	31	264	4	113	61542
2452	294424	77	992	2	127	101494
1940	215641	46	818	0	121	55813
2662	325107	99	937	0	136	79215
1499	167542	66	507	2	108	55461
865	106408	30	260	1	46	31081
2527	265769	146	927	2	124	83122
2747	269651	67	1269	10	115	70106
1324	149112	56	537	6	128	60578
1383	152871	58	532	5	97	79892
1179	111665	34	345	4	104	49810
2099	116408	61	918	1	59	71570
4308	362301	119	1635	2	125	100708
918	78800	42	330	2	82	33032
1831	183167	66	557	0	149	82875
3373	277965	89	1178	8	149	139077
1713	150629	44	740	3	122	71595
1438	168809	66	452	0	118	72260
496	24188	24	218	0	12	5950
2253	329267	259	764	8	144	115762
744	65029	17	255	5	67	32551
1161	101097	64	454	3	52	31701
2352	218946	41	866	1	108	80670
2144	244052	68	574	5	166	143558
2694	233328	132	825	5	107	120733
1973	256462	105	798	0	127	105195
1769	206161	71	663	12	107	73107
3148	311473	112	1069	8	146	132068
2474	235800	94	921	8	84	149193
2084	177939	82	858	8	141	46821
1954	207176	70	711	8	123	87011
1226	196553	57	503	2	111	95260
1389	174184	53	382	0	98	55183
1496	143246	103	464	5	105	106671
2269	187559	121	717	8	135	73511
1833	187681	62	690	2	107	92945
1268	119016	52	462	5	85	78664
1943	182192	52	657	12	155	70054
893	73566	32	385	6	88	22618
1762	194979	62	577	7	155	74011
1403	167488	45	619	2	104	83737
1425	143756	46	479	0	132	69094
1857	275541	63	817	4	127	93133
1840	243199	75	752	3	108	95536
1502	182999	88	430	6	129	225920
1441	135649	46	451	2	116	62133
1420	152299	53	537	0	122	61370
1416	120221	37	519	1	85	43836
2970	346485	90	1000	0	147	106117
1317	145790	63	637	5	99	38692
1644	193339	78	465	2	87	84651
870	80953	25	437	0	28	56622
1654	122774	45	711	0	90	15986
1054	130585	46	299	5	109	95364
3004	286468	144	1162	1	111	89691
2008	241066	82	714	0	158	67267
2547	148446	91	905	1	141	126846
1885	204713	71	649	1	122	41140
1626	182079	63	512	2	124	102860
1468	140344	53	472	6	93	51715
2445	220516	62	905	1	124	55801
1964	243060	63	786	4	112	111813
1381	162765	32	489	2	108	120293
1369	182613	39	479	3	99	138599
1659	232138	62	617	0	117	161647
2888	265318	117	925	10	199	115929
2845	310839	92	1144	9	91	162901
1982	225060	93	669	7	158	109825
1904	232317	54	707	0	126	129838
1391	144966	144	458	0	122	37510
602	43287	14	214	4	71	43750
1743	155754	61	599	4	75	40652
1559	164709	109	572	0	115	87771
2014	201940	38	897	0	119	85872
2143	235454	73	819	0	124	89275
874	99466	50	273	0	91	192565
1281	100750	72	407	0	119	140867
1401	224549	50	465	4	117	120662
1944	243511	71	603	0	155	101338
391	22938	10	154	0	0	1168
1605	152474	65	577	0	123	65567
530	61857	25	192	4	32	25162
1386	132487	41	411	0	136	40735
2395	317394	86	975	1	117	91413
387	21054	16	146	0	0	855
1742	209641	42	705	5	88	97068
449	31414	19	200	0	25	14116
2699	244749	95	964	2	124	76643
1606	184510	49	537	7	151	110681
1204	128423	64	369	8	145	92696
1138	97839	38	417	2	87	94785
568	38214	34	276	0	27	8773
1459	151101	32	514	2	131	83209
2158	272458	65	822	0	162	93815
1111	172494	52	389	0	165	86687
2833	328107	65	1255	3	159	105547
1955	250579	83	694	0	147	103487
2922	351067	95	1024	3	170	213688
1002	158015	29	400	0	119	71220
956	85439	33	350	0	104	56926
2186	229242	247	719	4	120	91721
3604	351619	139	1277	4	150	115168
1035	84207	29	356	11	112	111194
3261	324598	110	1402	0	136	135777
1587	131069	67	600	4	107	51513
1424	204271	42	480	0	130	74163
1701	165543	65	595	1	115	51633
1249	141722	94	436	0	107	75345
3352	299775	95	1367	9	120	98952
1641	195838	67	564	1	116	102372
2035	173260	63	716	3	79	37238
2312	254488	83	747	10	150	103772
1369	104389	45	467	5	156	123969
2201	199476	70	861	2	118	135400
1900	224330	83	612	1	144	130115
207	14688	10	85	0	0	6023
1645	181633	70	564	2	110	64466
2429	271856	103	824	1	147	54990
151	7199	5	74	0	0	1644
474	46660	20	259	0	15	6179
141	17547	5	69	0	4	3926
872	95227	34	239	0	111	34777
1318	152601	48	438	2	85	73224
1192	101645	63	371	0	44	17140
829	101011	34	238	0	52	27570
186	7176	17	70	0	0	1423
1793	96560	76	503	0	54	22996
2702	175824	107	910	0	80	39992
4691	341570	168	1276	1	80	117105
1112	103597	43	379	1	60	23789
937	112611	41	248	0	78	26706
1290	85574	34	351	0	78	24266
2146	220801	75	720	1	72	44418
1590	92661	61	508	1	45	35232
1590	133328	55	506	0	78	40909
1210	61361	77	451	0	39	13294
2072	125930	75	699	4	68	32387
834	82316	32	245	4	39	21233
1105	102010	53	370	3	50	44332
1272	101523	42	316	0	88	61056
761	41566	35	229	5	36	13497
1988	99923	66	617	0	99	32334
620	22648	19	184	0	39	44339
800	46698	45	274	0	52	10288
1684	131698	65	502	0	75	65622
1050	91735	35	382	0	71	16563
1502	79863	37	438	1	71	29011
1421	108043	62	466	1	54	34553
1060	98866	18	397	0	49	23517
1417	120445	118	457	0	59	51009
946	116048	64	230	0	75	33416
1926	250047	81	651	0	71	83305
1577	136084	30	671	0	51	27142
961	92499	32	319	0	71	21399
1254	135781	31	433	2	47	24874
1335	74408	67	434	4	28	34988
1597	81240	66	503	0	68	45549
1639	133368	36	535	1	64	32755
1018	98146	40	459	0	68	27114
1383	79619	43	426	3	40	20760
1314	59194	31	288	6	80	37636
1335	139942	42	498	0	88	65461
1403	118612	46	454	2	48	30080
910	72880	33	376	0	76	24094




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 7 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197065&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197065&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197065&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Goodness of Fit
Correlation0.9262
R-squared0.8578
RMSE30992.4574

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9262 \tabularnewline
R-squared & 0.8578 \tabularnewline
RMSE & 30992.4574 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197065&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9262[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8578[/C][/ROW]
[ROW][C]RMSE[/C][C]30992.4574[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197065&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197065&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
Correlation0.9262
R-squared0.8578
RMSE30992.4574







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1210907156134.4390243954772.5609756098
2120982107346.7513635.25
3176508156134.4390243920373.5609756098
4179321217099.565217391-37778.5652173913
5123185108090.82142857115094.1785714286
65274633226.611111111119519.3888888889
7385534322592.29411764762941.705882353
83317033226.6111111111-56.6111111111095
9149061156134.43902439-7073.43902439025
10165446156134.439024399311.56097560975
11237213217099.56521739120113.4347826087
12173326156134.4390243917191.5609756098
13133131156134.43902439-23003.4390243902
14258873217099.56521739141773.4347826087
15180083156134.4390243923948.5609756098
16324799322592.2941176472206.70588235295
17230964217099.56521739113864.4347826087
18236785249646.538461538-12861.5384615385
19135473108090.82142857127382.1785714286
20202925217099.565217391-14174.5652173913
21215147217099.565217391-1952.5652173913
22344297217099.565217391127197.434782609
23153935156134.43902439-2199.43902439025
24132943156134.43902439-23191.4390243902
25174724217099.565217391-42375.5652173913
26174415249646.538461538-75231.5384615385
27225548249646.538461538-24098.5384615385
28223632217099.5652173916532.4347826087
29124817156134.43902439-31317.4390243902
30221698217099.5652173914598.4347826087
31210767217099.565217391-6332.5652173913
32170266156134.4390243914131.5609756098
33260561217099.56521739143461.4347826087
3484853107346.75-22493.75
35294424322592.294117647-28168.294117647
36215641217099.565217391-1458.5652173913
37325107249646.53846153875460.4615384615
38167542156134.4390243911407.5609756098
39106408108090.821428571-1682.82142857143
40265769249646.53846153816122.4615384615
41269651322592.294117647-52941.294117647
42149112156134.43902439-7022.43902439025
43152871156134.43902439-3263.43902439025
44111665156134.43902439-44469.4390243902
45116408162741.8-46333.8
46362301322592.29411764739708.705882353
4778800108090.821428571-29290.8214285714
48183167217099.565217391-33932.5652173913
49277965322592.294117647-44627.294117647
50150629217099.565217391-66470.5652173913
51168809156134.4390243912674.5609756098
522418833226.6111111111-9038.61111111111
53329267249646.53846153879620.4615384615
546502933226.611111111131802.3888888889
55101097108090.821428571-6993.82142857143
56218946217099.5652173911846.4347826087
57244052217099.56521739126952.4347826087
58233328249646.538461538-16318.5384615385
59256462249646.5384615386815.46153846153
60206161217099.565217391-10938.5652173913
61311473322592.294117647-11119.294117647
62235800162741.873058.2
63177939217099.565217391-39160.5652173913
64207176217099.565217391-9923.5652173913
65196553156134.4390243940418.5609756098
66174184156134.4390243918049.5609756098
67143246156134.43902439-12888.4390243902
68187559249646.538461538-62087.5384615385
69187681217099.565217391-29418.5652173913
70119016108090.82142857110925.1785714286
71182192217099.565217391-34907.5652173913
727356685787.0909090909-12221.0909090909
73194979217099.565217391-22120.5652173913
74167488156134.4390243911353.5609756098
75143756156134.43902439-12378.4390243902
76275541217099.56521739158441.4347826087
77243199217099.56521739126099.4347826087
78182999156134.4390243926864.5609756098
79135649156134.43902439-20485.4390243902
80152299156134.43902439-3835.43902439025
81120221108090.82142857112130.1785714286
82346485322592.29411764723892.705882353
83145790156134.43902439-10344.4390243902
84193339162741.830597.2
8580953108090.821428571-27137.8214285714
86122774162741.8-39967.8
87130585107346.7523238.25
88286468322592.294117647-36124.294117647
89241066217099.56521739123966.4347826087
90148446217099.565217391-68653.5652173913
91204713217099.565217391-12386.5652173913
92182079217099.565217391-35020.5652173913
93140344156134.43902439-15790.4390243902
94220516217099.5652173913416.4347826087
95243060217099.56521739125960.4347826087
96162765156134.439024396630.56097560975
97182613156134.4390243926478.5609756098
98232138217099.56521739115038.4347826087
99265318249646.53846153815671.4615384615
100310839322592.294117647-11753.294117647
101225060217099.5652173917960.4347826087
102232317217099.56521739115217.4347826087
103144966156134.43902439-11168.4390243902
1044328733226.611111111110060.3888888889
105155754162741.8-6987.79999999999
106164709156134.439024398574.56097560975
107201940217099.565217391-15159.5652173913
108235454217099.56521739118354.4347826087
10999466107346.75-7880.75
110100750156134.43902439-55384.4390243902
111224549156134.4390243968414.5609756098
112243511217099.56521739126411.4347826087
1132293833226.6111111111-10288.6111111111
114152474156134.43902439-3660.43902439025
1156185733226.611111111128630.3888888889
116132487156134.43902439-23647.4390243902
117317394322592.294117647-5198.29411764705
1182105433226.6111111111-12172.6111111111
119209641162741.846899.2
1203141433226.6111111111-1812.61111111111
121244749249646.538461538-4897.53846153847
122184510217099.565217391-32589.5652173913
123128423156134.43902439-27711.4390243902
12497839108090.821428571-10251.8214285714
1253821433226.61111111114987.38888888889
126151101156134.43902439-5033.43902439025
127272458217099.56521739155358.4347826087
128172494156134.4390243916359.5609756098
129328107322592.2941176475514.70588235295
130250579217099.56521739133479.4347826087
131351067322592.29411764728474.705882353
132158015107346.7550668.25
13385439107346.75-21907.75
134229242249646.538461538-20404.5384615385
135351619322592.29411764729026.705882353
13684207107346.75-23139.75
137324598322592.2941176472005.70588235295
138131069156134.43902439-25065.4390243902
139204271156134.4390243948136.5609756098
140165543217099.565217391-51556.5652173913
141141722156134.43902439-14412.4390243902
142299775322592.294117647-22817.294117647
143195838217099.565217391-21261.5652173913
144173260162741.810518.2
145254488217099.56521739137388.4347826087
146104389156134.43902439-51745.4390243902
147199476217099.565217391-17623.5652173913
148224330217099.5652173917230.4347826087
1491468833226.6111111111-18538.6111111111
150181633217099.565217391-35466.5652173913
151271856249646.53846153822209.4615384615
152719933226.6111111111-26027.6111111111
1534666033226.611111111113433.3888888889
1541754733226.6111111111-15679.6111111111
15595227107346.75-12119.75
156152601108090.82142857144510.1785714286
15710164585787.090909090915857.9090909091
158101011108090.821428571-7079.82142857143
159717633226.6111111111-26050.6111111111
16096560162741.8-66181.8
161175824162741.813082.2
162341570322592.29411764718977.705882353
16310359785787.090909090917809.9090909091
164112611108090.8214285714520.17857142857
1658557485787.0909090909-213.090909090912
166220801162741.858059.2
16792661108090.821428571-15429.8214285714
168133328108090.82142857125237.1785714286
1696136185787.0909090909-24426.0909090909
170125930162741.8-36811.8
1718231685787.0909090909-3471.09090909091
172102010108090.821428571-6080.82142857143
173101523108090.821428571-6567.82142857143
1744156633226.61111111118339.38888888889
17599923162741.8-62818.8
1762264833226.6111111111-10578.6111111111
1774669833226.611111111113471.3888888889
178131698162741.8-31043.8
1799173585787.09090909095947.90909090909
18079863108090.821428571-28227.8214285714
181108043108090.821428571-47.8214285714348
1829886685787.090909090913078.9090909091
183120445108090.82142857112354.1785714286
184116048108090.8214285717957.17857142857
185250047162741.887305.2
186136084108090.82142857127993.1785714286
1879249985787.09090909096711.90909090909
188135781108090.82142857127690.1785714286
18974408108090.821428571-33682.8214285714
19081240108090.821428571-26850.8214285714
191133368162741.8-29373.8
19298146108090.821428571-9944.82142857143
1937961985787.0909090909-6168.09090909091
19459194108090.821428571-48896.8214285714
195139942108090.82142857131851.1785714286
196118612108090.82142857110521.1785714286
1977288085787.0909090909-12907.0909090909

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 210907 & 156134.43902439 & 54772.5609756098 \tabularnewline
2 & 120982 & 107346.75 & 13635.25 \tabularnewline
3 & 176508 & 156134.43902439 & 20373.5609756098 \tabularnewline
4 & 179321 & 217099.565217391 & -37778.5652173913 \tabularnewline
5 & 123185 & 108090.821428571 & 15094.1785714286 \tabularnewline
6 & 52746 & 33226.6111111111 & 19519.3888888889 \tabularnewline
7 & 385534 & 322592.294117647 & 62941.705882353 \tabularnewline
8 & 33170 & 33226.6111111111 & -56.6111111111095 \tabularnewline
9 & 149061 & 156134.43902439 & -7073.43902439025 \tabularnewline
10 & 165446 & 156134.43902439 & 9311.56097560975 \tabularnewline
11 & 237213 & 217099.565217391 & 20113.4347826087 \tabularnewline
12 & 173326 & 156134.43902439 & 17191.5609756098 \tabularnewline
13 & 133131 & 156134.43902439 & -23003.4390243902 \tabularnewline
14 & 258873 & 217099.565217391 & 41773.4347826087 \tabularnewline
15 & 180083 & 156134.43902439 & 23948.5609756098 \tabularnewline
16 & 324799 & 322592.294117647 & 2206.70588235295 \tabularnewline
17 & 230964 & 217099.565217391 & 13864.4347826087 \tabularnewline
18 & 236785 & 249646.538461538 & -12861.5384615385 \tabularnewline
19 & 135473 & 108090.821428571 & 27382.1785714286 \tabularnewline
20 & 202925 & 217099.565217391 & -14174.5652173913 \tabularnewline
21 & 215147 & 217099.565217391 & -1952.5652173913 \tabularnewline
22 & 344297 & 217099.565217391 & 127197.434782609 \tabularnewline
23 & 153935 & 156134.43902439 & -2199.43902439025 \tabularnewline
24 & 132943 & 156134.43902439 & -23191.4390243902 \tabularnewline
25 & 174724 & 217099.565217391 & -42375.5652173913 \tabularnewline
26 & 174415 & 249646.538461538 & -75231.5384615385 \tabularnewline
27 & 225548 & 249646.538461538 & -24098.5384615385 \tabularnewline
28 & 223632 & 217099.565217391 & 6532.4347826087 \tabularnewline
29 & 124817 & 156134.43902439 & -31317.4390243902 \tabularnewline
30 & 221698 & 217099.565217391 & 4598.4347826087 \tabularnewline
31 & 210767 & 217099.565217391 & -6332.5652173913 \tabularnewline
32 & 170266 & 156134.43902439 & 14131.5609756098 \tabularnewline
33 & 260561 & 217099.565217391 & 43461.4347826087 \tabularnewline
34 & 84853 & 107346.75 & -22493.75 \tabularnewline
35 & 294424 & 322592.294117647 & -28168.294117647 \tabularnewline
36 & 215641 & 217099.565217391 & -1458.5652173913 \tabularnewline
37 & 325107 & 249646.538461538 & 75460.4615384615 \tabularnewline
38 & 167542 & 156134.43902439 & 11407.5609756098 \tabularnewline
39 & 106408 & 108090.821428571 & -1682.82142857143 \tabularnewline
40 & 265769 & 249646.538461538 & 16122.4615384615 \tabularnewline
41 & 269651 & 322592.294117647 & -52941.294117647 \tabularnewline
42 & 149112 & 156134.43902439 & -7022.43902439025 \tabularnewline
43 & 152871 & 156134.43902439 & -3263.43902439025 \tabularnewline
44 & 111665 & 156134.43902439 & -44469.4390243902 \tabularnewline
45 & 116408 & 162741.8 & -46333.8 \tabularnewline
46 & 362301 & 322592.294117647 & 39708.705882353 \tabularnewline
47 & 78800 & 108090.821428571 & -29290.8214285714 \tabularnewline
48 & 183167 & 217099.565217391 & -33932.5652173913 \tabularnewline
49 & 277965 & 322592.294117647 & -44627.294117647 \tabularnewline
50 & 150629 & 217099.565217391 & -66470.5652173913 \tabularnewline
51 & 168809 & 156134.43902439 & 12674.5609756098 \tabularnewline
52 & 24188 & 33226.6111111111 & -9038.61111111111 \tabularnewline
53 & 329267 & 249646.538461538 & 79620.4615384615 \tabularnewline
54 & 65029 & 33226.6111111111 & 31802.3888888889 \tabularnewline
55 & 101097 & 108090.821428571 & -6993.82142857143 \tabularnewline
56 & 218946 & 217099.565217391 & 1846.4347826087 \tabularnewline
57 & 244052 & 217099.565217391 & 26952.4347826087 \tabularnewline
58 & 233328 & 249646.538461538 & -16318.5384615385 \tabularnewline
59 & 256462 & 249646.538461538 & 6815.46153846153 \tabularnewline
60 & 206161 & 217099.565217391 & -10938.5652173913 \tabularnewline
61 & 311473 & 322592.294117647 & -11119.294117647 \tabularnewline
62 & 235800 & 162741.8 & 73058.2 \tabularnewline
63 & 177939 & 217099.565217391 & -39160.5652173913 \tabularnewline
64 & 207176 & 217099.565217391 & -9923.5652173913 \tabularnewline
65 & 196553 & 156134.43902439 & 40418.5609756098 \tabularnewline
66 & 174184 & 156134.43902439 & 18049.5609756098 \tabularnewline
67 & 143246 & 156134.43902439 & -12888.4390243902 \tabularnewline
68 & 187559 & 249646.538461538 & -62087.5384615385 \tabularnewline
69 & 187681 & 217099.565217391 & -29418.5652173913 \tabularnewline
70 & 119016 & 108090.821428571 & 10925.1785714286 \tabularnewline
71 & 182192 & 217099.565217391 & -34907.5652173913 \tabularnewline
72 & 73566 & 85787.0909090909 & -12221.0909090909 \tabularnewline
73 & 194979 & 217099.565217391 & -22120.5652173913 \tabularnewline
74 & 167488 & 156134.43902439 & 11353.5609756098 \tabularnewline
75 & 143756 & 156134.43902439 & -12378.4390243902 \tabularnewline
76 & 275541 & 217099.565217391 & 58441.4347826087 \tabularnewline
77 & 243199 & 217099.565217391 & 26099.4347826087 \tabularnewline
78 & 182999 & 156134.43902439 & 26864.5609756098 \tabularnewline
79 & 135649 & 156134.43902439 & -20485.4390243902 \tabularnewline
80 & 152299 & 156134.43902439 & -3835.43902439025 \tabularnewline
81 & 120221 & 108090.821428571 & 12130.1785714286 \tabularnewline
82 & 346485 & 322592.294117647 & 23892.705882353 \tabularnewline
83 & 145790 & 156134.43902439 & -10344.4390243902 \tabularnewline
84 & 193339 & 162741.8 & 30597.2 \tabularnewline
85 & 80953 & 108090.821428571 & -27137.8214285714 \tabularnewline
86 & 122774 & 162741.8 & -39967.8 \tabularnewline
87 & 130585 & 107346.75 & 23238.25 \tabularnewline
88 & 286468 & 322592.294117647 & -36124.294117647 \tabularnewline
89 & 241066 & 217099.565217391 & 23966.4347826087 \tabularnewline
90 & 148446 & 217099.565217391 & -68653.5652173913 \tabularnewline
91 & 204713 & 217099.565217391 & -12386.5652173913 \tabularnewline
92 & 182079 & 217099.565217391 & -35020.5652173913 \tabularnewline
93 & 140344 & 156134.43902439 & -15790.4390243902 \tabularnewline
94 & 220516 & 217099.565217391 & 3416.4347826087 \tabularnewline
95 & 243060 & 217099.565217391 & 25960.4347826087 \tabularnewline
96 & 162765 & 156134.43902439 & 6630.56097560975 \tabularnewline
97 & 182613 & 156134.43902439 & 26478.5609756098 \tabularnewline
98 & 232138 & 217099.565217391 & 15038.4347826087 \tabularnewline
99 & 265318 & 249646.538461538 & 15671.4615384615 \tabularnewline
100 & 310839 & 322592.294117647 & -11753.294117647 \tabularnewline
101 & 225060 & 217099.565217391 & 7960.4347826087 \tabularnewline
102 & 232317 & 217099.565217391 & 15217.4347826087 \tabularnewline
103 & 144966 & 156134.43902439 & -11168.4390243902 \tabularnewline
104 & 43287 & 33226.6111111111 & 10060.3888888889 \tabularnewline
105 & 155754 & 162741.8 & -6987.79999999999 \tabularnewline
106 & 164709 & 156134.43902439 & 8574.56097560975 \tabularnewline
107 & 201940 & 217099.565217391 & -15159.5652173913 \tabularnewline
108 & 235454 & 217099.565217391 & 18354.4347826087 \tabularnewline
109 & 99466 & 107346.75 & -7880.75 \tabularnewline
110 & 100750 & 156134.43902439 & -55384.4390243902 \tabularnewline
111 & 224549 & 156134.43902439 & 68414.5609756098 \tabularnewline
112 & 243511 & 217099.565217391 & 26411.4347826087 \tabularnewline
113 & 22938 & 33226.6111111111 & -10288.6111111111 \tabularnewline
114 & 152474 & 156134.43902439 & -3660.43902439025 \tabularnewline
115 & 61857 & 33226.6111111111 & 28630.3888888889 \tabularnewline
116 & 132487 & 156134.43902439 & -23647.4390243902 \tabularnewline
117 & 317394 & 322592.294117647 & -5198.29411764705 \tabularnewline
118 & 21054 & 33226.6111111111 & -12172.6111111111 \tabularnewline
119 & 209641 & 162741.8 & 46899.2 \tabularnewline
120 & 31414 & 33226.6111111111 & -1812.61111111111 \tabularnewline
121 & 244749 & 249646.538461538 & -4897.53846153847 \tabularnewline
122 & 184510 & 217099.565217391 & -32589.5652173913 \tabularnewline
123 & 128423 & 156134.43902439 & -27711.4390243902 \tabularnewline
124 & 97839 & 108090.821428571 & -10251.8214285714 \tabularnewline
125 & 38214 & 33226.6111111111 & 4987.38888888889 \tabularnewline
126 & 151101 & 156134.43902439 & -5033.43902439025 \tabularnewline
127 & 272458 & 217099.565217391 & 55358.4347826087 \tabularnewline
128 & 172494 & 156134.43902439 & 16359.5609756098 \tabularnewline
129 & 328107 & 322592.294117647 & 5514.70588235295 \tabularnewline
130 & 250579 & 217099.565217391 & 33479.4347826087 \tabularnewline
131 & 351067 & 322592.294117647 & 28474.705882353 \tabularnewline
132 & 158015 & 107346.75 & 50668.25 \tabularnewline
133 & 85439 & 107346.75 & -21907.75 \tabularnewline
134 & 229242 & 249646.538461538 & -20404.5384615385 \tabularnewline
135 & 351619 & 322592.294117647 & 29026.705882353 \tabularnewline
136 & 84207 & 107346.75 & -23139.75 \tabularnewline
137 & 324598 & 322592.294117647 & 2005.70588235295 \tabularnewline
138 & 131069 & 156134.43902439 & -25065.4390243902 \tabularnewline
139 & 204271 & 156134.43902439 & 48136.5609756098 \tabularnewline
140 & 165543 & 217099.565217391 & -51556.5652173913 \tabularnewline
141 & 141722 & 156134.43902439 & -14412.4390243902 \tabularnewline
142 & 299775 & 322592.294117647 & -22817.294117647 \tabularnewline
143 & 195838 & 217099.565217391 & -21261.5652173913 \tabularnewline
144 & 173260 & 162741.8 & 10518.2 \tabularnewline
145 & 254488 & 217099.565217391 & 37388.4347826087 \tabularnewline
146 & 104389 & 156134.43902439 & -51745.4390243902 \tabularnewline
147 & 199476 & 217099.565217391 & -17623.5652173913 \tabularnewline
148 & 224330 & 217099.565217391 & 7230.4347826087 \tabularnewline
149 & 14688 & 33226.6111111111 & -18538.6111111111 \tabularnewline
150 & 181633 & 217099.565217391 & -35466.5652173913 \tabularnewline
151 & 271856 & 249646.538461538 & 22209.4615384615 \tabularnewline
152 & 7199 & 33226.6111111111 & -26027.6111111111 \tabularnewline
153 & 46660 & 33226.6111111111 & 13433.3888888889 \tabularnewline
154 & 17547 & 33226.6111111111 & -15679.6111111111 \tabularnewline
155 & 95227 & 107346.75 & -12119.75 \tabularnewline
156 & 152601 & 108090.821428571 & 44510.1785714286 \tabularnewline
157 & 101645 & 85787.0909090909 & 15857.9090909091 \tabularnewline
158 & 101011 & 108090.821428571 & -7079.82142857143 \tabularnewline
159 & 7176 & 33226.6111111111 & -26050.6111111111 \tabularnewline
160 & 96560 & 162741.8 & -66181.8 \tabularnewline
161 & 175824 & 162741.8 & 13082.2 \tabularnewline
162 & 341570 & 322592.294117647 & 18977.705882353 \tabularnewline
163 & 103597 & 85787.0909090909 & 17809.9090909091 \tabularnewline
164 & 112611 & 108090.821428571 & 4520.17857142857 \tabularnewline
165 & 85574 & 85787.0909090909 & -213.090909090912 \tabularnewline
166 & 220801 & 162741.8 & 58059.2 \tabularnewline
167 & 92661 & 108090.821428571 & -15429.8214285714 \tabularnewline
168 & 133328 & 108090.821428571 & 25237.1785714286 \tabularnewline
169 & 61361 & 85787.0909090909 & -24426.0909090909 \tabularnewline
170 & 125930 & 162741.8 & -36811.8 \tabularnewline
171 & 82316 & 85787.0909090909 & -3471.09090909091 \tabularnewline
172 & 102010 & 108090.821428571 & -6080.82142857143 \tabularnewline
173 & 101523 & 108090.821428571 & -6567.82142857143 \tabularnewline
174 & 41566 & 33226.6111111111 & 8339.38888888889 \tabularnewline
175 & 99923 & 162741.8 & -62818.8 \tabularnewline
176 & 22648 & 33226.6111111111 & -10578.6111111111 \tabularnewline
177 & 46698 & 33226.6111111111 & 13471.3888888889 \tabularnewline
178 & 131698 & 162741.8 & -31043.8 \tabularnewline
179 & 91735 & 85787.0909090909 & 5947.90909090909 \tabularnewline
180 & 79863 & 108090.821428571 & -28227.8214285714 \tabularnewline
181 & 108043 & 108090.821428571 & -47.8214285714348 \tabularnewline
182 & 98866 & 85787.0909090909 & 13078.9090909091 \tabularnewline
183 & 120445 & 108090.821428571 & 12354.1785714286 \tabularnewline
184 & 116048 & 108090.821428571 & 7957.17857142857 \tabularnewline
185 & 250047 & 162741.8 & 87305.2 \tabularnewline
186 & 136084 & 108090.821428571 & 27993.1785714286 \tabularnewline
187 & 92499 & 85787.0909090909 & 6711.90909090909 \tabularnewline
188 & 135781 & 108090.821428571 & 27690.1785714286 \tabularnewline
189 & 74408 & 108090.821428571 & -33682.8214285714 \tabularnewline
190 & 81240 & 108090.821428571 & -26850.8214285714 \tabularnewline
191 & 133368 & 162741.8 & -29373.8 \tabularnewline
192 & 98146 & 108090.821428571 & -9944.82142857143 \tabularnewline
193 & 79619 & 85787.0909090909 & -6168.09090909091 \tabularnewline
194 & 59194 & 108090.821428571 & -48896.8214285714 \tabularnewline
195 & 139942 & 108090.821428571 & 31851.1785714286 \tabularnewline
196 & 118612 & 108090.821428571 & 10521.1785714286 \tabularnewline
197 & 72880 & 85787.0909090909 & -12907.0909090909 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197065&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]210907[/C][C]156134.43902439[/C][C]54772.5609756098[/C][/ROW]
[ROW][C]2[/C][C]120982[/C][C]107346.75[/C][C]13635.25[/C][/ROW]
[ROW][C]3[/C][C]176508[/C][C]156134.43902439[/C][C]20373.5609756098[/C][/ROW]
[ROW][C]4[/C][C]179321[/C][C]217099.565217391[/C][C]-37778.5652173913[/C][/ROW]
[ROW][C]5[/C][C]123185[/C][C]108090.821428571[/C][C]15094.1785714286[/C][/ROW]
[ROW][C]6[/C][C]52746[/C][C]33226.6111111111[/C][C]19519.3888888889[/C][/ROW]
[ROW][C]7[/C][C]385534[/C][C]322592.294117647[/C][C]62941.705882353[/C][/ROW]
[ROW][C]8[/C][C]33170[/C][C]33226.6111111111[/C][C]-56.6111111111095[/C][/ROW]
[ROW][C]9[/C][C]149061[/C][C]156134.43902439[/C][C]-7073.43902439025[/C][/ROW]
[ROW][C]10[/C][C]165446[/C][C]156134.43902439[/C][C]9311.56097560975[/C][/ROW]
[ROW][C]11[/C][C]237213[/C][C]217099.565217391[/C][C]20113.4347826087[/C][/ROW]
[ROW][C]12[/C][C]173326[/C][C]156134.43902439[/C][C]17191.5609756098[/C][/ROW]
[ROW][C]13[/C][C]133131[/C][C]156134.43902439[/C][C]-23003.4390243902[/C][/ROW]
[ROW][C]14[/C][C]258873[/C][C]217099.565217391[/C][C]41773.4347826087[/C][/ROW]
[ROW][C]15[/C][C]180083[/C][C]156134.43902439[/C][C]23948.5609756098[/C][/ROW]
[ROW][C]16[/C][C]324799[/C][C]322592.294117647[/C][C]2206.70588235295[/C][/ROW]
[ROW][C]17[/C][C]230964[/C][C]217099.565217391[/C][C]13864.4347826087[/C][/ROW]
[ROW][C]18[/C][C]236785[/C][C]249646.538461538[/C][C]-12861.5384615385[/C][/ROW]
[ROW][C]19[/C][C]135473[/C][C]108090.821428571[/C][C]27382.1785714286[/C][/ROW]
[ROW][C]20[/C][C]202925[/C][C]217099.565217391[/C][C]-14174.5652173913[/C][/ROW]
[ROW][C]21[/C][C]215147[/C][C]217099.565217391[/C][C]-1952.5652173913[/C][/ROW]
[ROW][C]22[/C][C]344297[/C][C]217099.565217391[/C][C]127197.434782609[/C][/ROW]
[ROW][C]23[/C][C]153935[/C][C]156134.43902439[/C][C]-2199.43902439025[/C][/ROW]
[ROW][C]24[/C][C]132943[/C][C]156134.43902439[/C][C]-23191.4390243902[/C][/ROW]
[ROW][C]25[/C][C]174724[/C][C]217099.565217391[/C][C]-42375.5652173913[/C][/ROW]
[ROW][C]26[/C][C]174415[/C][C]249646.538461538[/C][C]-75231.5384615385[/C][/ROW]
[ROW][C]27[/C][C]225548[/C][C]249646.538461538[/C][C]-24098.5384615385[/C][/ROW]
[ROW][C]28[/C][C]223632[/C][C]217099.565217391[/C][C]6532.4347826087[/C][/ROW]
[ROW][C]29[/C][C]124817[/C][C]156134.43902439[/C][C]-31317.4390243902[/C][/ROW]
[ROW][C]30[/C][C]221698[/C][C]217099.565217391[/C][C]4598.4347826087[/C][/ROW]
[ROW][C]31[/C][C]210767[/C][C]217099.565217391[/C][C]-6332.5652173913[/C][/ROW]
[ROW][C]32[/C][C]170266[/C][C]156134.43902439[/C][C]14131.5609756098[/C][/ROW]
[ROW][C]33[/C][C]260561[/C][C]217099.565217391[/C][C]43461.4347826087[/C][/ROW]
[ROW][C]34[/C][C]84853[/C][C]107346.75[/C][C]-22493.75[/C][/ROW]
[ROW][C]35[/C][C]294424[/C][C]322592.294117647[/C][C]-28168.294117647[/C][/ROW]
[ROW][C]36[/C][C]215641[/C][C]217099.565217391[/C][C]-1458.5652173913[/C][/ROW]
[ROW][C]37[/C][C]325107[/C][C]249646.538461538[/C][C]75460.4615384615[/C][/ROW]
[ROW][C]38[/C][C]167542[/C][C]156134.43902439[/C][C]11407.5609756098[/C][/ROW]
[ROW][C]39[/C][C]106408[/C][C]108090.821428571[/C][C]-1682.82142857143[/C][/ROW]
[ROW][C]40[/C][C]265769[/C][C]249646.538461538[/C][C]16122.4615384615[/C][/ROW]
[ROW][C]41[/C][C]269651[/C][C]322592.294117647[/C][C]-52941.294117647[/C][/ROW]
[ROW][C]42[/C][C]149112[/C][C]156134.43902439[/C][C]-7022.43902439025[/C][/ROW]
[ROW][C]43[/C][C]152871[/C][C]156134.43902439[/C][C]-3263.43902439025[/C][/ROW]
[ROW][C]44[/C][C]111665[/C][C]156134.43902439[/C][C]-44469.4390243902[/C][/ROW]
[ROW][C]45[/C][C]116408[/C][C]162741.8[/C][C]-46333.8[/C][/ROW]
[ROW][C]46[/C][C]362301[/C][C]322592.294117647[/C][C]39708.705882353[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]108090.821428571[/C][C]-29290.8214285714[/C][/ROW]
[ROW][C]48[/C][C]183167[/C][C]217099.565217391[/C][C]-33932.5652173913[/C][/ROW]
[ROW][C]49[/C][C]277965[/C][C]322592.294117647[/C][C]-44627.294117647[/C][/ROW]
[ROW][C]50[/C][C]150629[/C][C]217099.565217391[/C][C]-66470.5652173913[/C][/ROW]
[ROW][C]51[/C][C]168809[/C][C]156134.43902439[/C][C]12674.5609756098[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]33226.6111111111[/C][C]-9038.61111111111[/C][/ROW]
[ROW][C]53[/C][C]329267[/C][C]249646.538461538[/C][C]79620.4615384615[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]33226.6111111111[/C][C]31802.3888888889[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]108090.821428571[/C][C]-6993.82142857143[/C][/ROW]
[ROW][C]56[/C][C]218946[/C][C]217099.565217391[/C][C]1846.4347826087[/C][/ROW]
[ROW][C]57[/C][C]244052[/C][C]217099.565217391[/C][C]26952.4347826087[/C][/ROW]
[ROW][C]58[/C][C]233328[/C][C]249646.538461538[/C][C]-16318.5384615385[/C][/ROW]
[ROW][C]59[/C][C]256462[/C][C]249646.538461538[/C][C]6815.46153846153[/C][/ROW]
[ROW][C]60[/C][C]206161[/C][C]217099.565217391[/C][C]-10938.5652173913[/C][/ROW]
[ROW][C]61[/C][C]311473[/C][C]322592.294117647[/C][C]-11119.294117647[/C][/ROW]
[ROW][C]62[/C][C]235800[/C][C]162741.8[/C][C]73058.2[/C][/ROW]
[ROW][C]63[/C][C]177939[/C][C]217099.565217391[/C][C]-39160.5652173913[/C][/ROW]
[ROW][C]64[/C][C]207176[/C][C]217099.565217391[/C][C]-9923.5652173913[/C][/ROW]
[ROW][C]65[/C][C]196553[/C][C]156134.43902439[/C][C]40418.5609756098[/C][/ROW]
[ROW][C]66[/C][C]174184[/C][C]156134.43902439[/C][C]18049.5609756098[/C][/ROW]
[ROW][C]67[/C][C]143246[/C][C]156134.43902439[/C][C]-12888.4390243902[/C][/ROW]
[ROW][C]68[/C][C]187559[/C][C]249646.538461538[/C][C]-62087.5384615385[/C][/ROW]
[ROW][C]69[/C][C]187681[/C][C]217099.565217391[/C][C]-29418.5652173913[/C][/ROW]
[ROW][C]70[/C][C]119016[/C][C]108090.821428571[/C][C]10925.1785714286[/C][/ROW]
[ROW][C]71[/C][C]182192[/C][C]217099.565217391[/C][C]-34907.5652173913[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]85787.0909090909[/C][C]-12221.0909090909[/C][/ROW]
[ROW][C]73[/C][C]194979[/C][C]217099.565217391[/C][C]-22120.5652173913[/C][/ROW]
[ROW][C]74[/C][C]167488[/C][C]156134.43902439[/C][C]11353.5609756098[/C][/ROW]
[ROW][C]75[/C][C]143756[/C][C]156134.43902439[/C][C]-12378.4390243902[/C][/ROW]
[ROW][C]76[/C][C]275541[/C][C]217099.565217391[/C][C]58441.4347826087[/C][/ROW]
[ROW][C]77[/C][C]243199[/C][C]217099.565217391[/C][C]26099.4347826087[/C][/ROW]
[ROW][C]78[/C][C]182999[/C][C]156134.43902439[/C][C]26864.5609756098[/C][/ROW]
[ROW][C]79[/C][C]135649[/C][C]156134.43902439[/C][C]-20485.4390243902[/C][/ROW]
[ROW][C]80[/C][C]152299[/C][C]156134.43902439[/C][C]-3835.43902439025[/C][/ROW]
[ROW][C]81[/C][C]120221[/C][C]108090.821428571[/C][C]12130.1785714286[/C][/ROW]
[ROW][C]82[/C][C]346485[/C][C]322592.294117647[/C][C]23892.705882353[/C][/ROW]
[ROW][C]83[/C][C]145790[/C][C]156134.43902439[/C][C]-10344.4390243902[/C][/ROW]
[ROW][C]84[/C][C]193339[/C][C]162741.8[/C][C]30597.2[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]108090.821428571[/C][C]-27137.8214285714[/C][/ROW]
[ROW][C]86[/C][C]122774[/C][C]162741.8[/C][C]-39967.8[/C][/ROW]
[ROW][C]87[/C][C]130585[/C][C]107346.75[/C][C]23238.25[/C][/ROW]
[ROW][C]88[/C][C]286468[/C][C]322592.294117647[/C][C]-36124.294117647[/C][/ROW]
[ROW][C]89[/C][C]241066[/C][C]217099.565217391[/C][C]23966.4347826087[/C][/ROW]
[ROW][C]90[/C][C]148446[/C][C]217099.565217391[/C][C]-68653.5652173913[/C][/ROW]
[ROW][C]91[/C][C]204713[/C][C]217099.565217391[/C][C]-12386.5652173913[/C][/ROW]
[ROW][C]92[/C][C]182079[/C][C]217099.565217391[/C][C]-35020.5652173913[/C][/ROW]
[ROW][C]93[/C][C]140344[/C][C]156134.43902439[/C][C]-15790.4390243902[/C][/ROW]
[ROW][C]94[/C][C]220516[/C][C]217099.565217391[/C][C]3416.4347826087[/C][/ROW]
[ROW][C]95[/C][C]243060[/C][C]217099.565217391[/C][C]25960.4347826087[/C][/ROW]
[ROW][C]96[/C][C]162765[/C][C]156134.43902439[/C][C]6630.56097560975[/C][/ROW]
[ROW][C]97[/C][C]182613[/C][C]156134.43902439[/C][C]26478.5609756098[/C][/ROW]
[ROW][C]98[/C][C]232138[/C][C]217099.565217391[/C][C]15038.4347826087[/C][/ROW]
[ROW][C]99[/C][C]265318[/C][C]249646.538461538[/C][C]15671.4615384615[/C][/ROW]
[ROW][C]100[/C][C]310839[/C][C]322592.294117647[/C][C]-11753.294117647[/C][/ROW]
[ROW][C]101[/C][C]225060[/C][C]217099.565217391[/C][C]7960.4347826087[/C][/ROW]
[ROW][C]102[/C][C]232317[/C][C]217099.565217391[/C][C]15217.4347826087[/C][/ROW]
[ROW][C]103[/C][C]144966[/C][C]156134.43902439[/C][C]-11168.4390243902[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]33226.6111111111[/C][C]10060.3888888889[/C][/ROW]
[ROW][C]105[/C][C]155754[/C][C]162741.8[/C][C]-6987.79999999999[/C][/ROW]
[ROW][C]106[/C][C]164709[/C][C]156134.43902439[/C][C]8574.56097560975[/C][/ROW]
[ROW][C]107[/C][C]201940[/C][C]217099.565217391[/C][C]-15159.5652173913[/C][/ROW]
[ROW][C]108[/C][C]235454[/C][C]217099.565217391[/C][C]18354.4347826087[/C][/ROW]
[ROW][C]109[/C][C]99466[/C][C]107346.75[/C][C]-7880.75[/C][/ROW]
[ROW][C]110[/C][C]100750[/C][C]156134.43902439[/C][C]-55384.4390243902[/C][/ROW]
[ROW][C]111[/C][C]224549[/C][C]156134.43902439[/C][C]68414.5609756098[/C][/ROW]
[ROW][C]112[/C][C]243511[/C][C]217099.565217391[/C][C]26411.4347826087[/C][/ROW]
[ROW][C]113[/C][C]22938[/C][C]33226.6111111111[/C][C]-10288.6111111111[/C][/ROW]
[ROW][C]114[/C][C]152474[/C][C]156134.43902439[/C][C]-3660.43902439025[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]33226.6111111111[/C][C]28630.3888888889[/C][/ROW]
[ROW][C]116[/C][C]132487[/C][C]156134.43902439[/C][C]-23647.4390243902[/C][/ROW]
[ROW][C]117[/C][C]317394[/C][C]322592.294117647[/C][C]-5198.29411764705[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]33226.6111111111[/C][C]-12172.6111111111[/C][/ROW]
[ROW][C]119[/C][C]209641[/C][C]162741.8[/C][C]46899.2[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]33226.6111111111[/C][C]-1812.61111111111[/C][/ROW]
[ROW][C]121[/C][C]244749[/C][C]249646.538461538[/C][C]-4897.53846153847[/C][/ROW]
[ROW][C]122[/C][C]184510[/C][C]217099.565217391[/C][C]-32589.5652173913[/C][/ROW]
[ROW][C]123[/C][C]128423[/C][C]156134.43902439[/C][C]-27711.4390243902[/C][/ROW]
[ROW][C]124[/C][C]97839[/C][C]108090.821428571[/C][C]-10251.8214285714[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]33226.6111111111[/C][C]4987.38888888889[/C][/ROW]
[ROW][C]126[/C][C]151101[/C][C]156134.43902439[/C][C]-5033.43902439025[/C][/ROW]
[ROW][C]127[/C][C]272458[/C][C]217099.565217391[/C][C]55358.4347826087[/C][/ROW]
[ROW][C]128[/C][C]172494[/C][C]156134.43902439[/C][C]16359.5609756098[/C][/ROW]
[ROW][C]129[/C][C]328107[/C][C]322592.294117647[/C][C]5514.70588235295[/C][/ROW]
[ROW][C]130[/C][C]250579[/C][C]217099.565217391[/C][C]33479.4347826087[/C][/ROW]
[ROW][C]131[/C][C]351067[/C][C]322592.294117647[/C][C]28474.705882353[/C][/ROW]
[ROW][C]132[/C][C]158015[/C][C]107346.75[/C][C]50668.25[/C][/ROW]
[ROW][C]133[/C][C]85439[/C][C]107346.75[/C][C]-21907.75[/C][/ROW]
[ROW][C]134[/C][C]229242[/C][C]249646.538461538[/C][C]-20404.5384615385[/C][/ROW]
[ROW][C]135[/C][C]351619[/C][C]322592.294117647[/C][C]29026.705882353[/C][/ROW]
[ROW][C]136[/C][C]84207[/C][C]107346.75[/C][C]-23139.75[/C][/ROW]
[ROW][C]137[/C][C]324598[/C][C]322592.294117647[/C][C]2005.70588235295[/C][/ROW]
[ROW][C]138[/C][C]131069[/C][C]156134.43902439[/C][C]-25065.4390243902[/C][/ROW]
[ROW][C]139[/C][C]204271[/C][C]156134.43902439[/C][C]48136.5609756098[/C][/ROW]
[ROW][C]140[/C][C]165543[/C][C]217099.565217391[/C][C]-51556.5652173913[/C][/ROW]
[ROW][C]141[/C][C]141722[/C][C]156134.43902439[/C][C]-14412.4390243902[/C][/ROW]
[ROW][C]142[/C][C]299775[/C][C]322592.294117647[/C][C]-22817.294117647[/C][/ROW]
[ROW][C]143[/C][C]195838[/C][C]217099.565217391[/C][C]-21261.5652173913[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]162741.8[/C][C]10518.2[/C][/ROW]
[ROW][C]145[/C][C]254488[/C][C]217099.565217391[/C][C]37388.4347826087[/C][/ROW]
[ROW][C]146[/C][C]104389[/C][C]156134.43902439[/C][C]-51745.4390243902[/C][/ROW]
[ROW][C]147[/C][C]199476[/C][C]217099.565217391[/C][C]-17623.5652173913[/C][/ROW]
[ROW][C]148[/C][C]224330[/C][C]217099.565217391[/C][C]7230.4347826087[/C][/ROW]
[ROW][C]149[/C][C]14688[/C][C]33226.6111111111[/C][C]-18538.6111111111[/C][/ROW]
[ROW][C]150[/C][C]181633[/C][C]217099.565217391[/C][C]-35466.5652173913[/C][/ROW]
[ROW][C]151[/C][C]271856[/C][C]249646.538461538[/C][C]22209.4615384615[/C][/ROW]
[ROW][C]152[/C][C]7199[/C][C]33226.6111111111[/C][C]-26027.6111111111[/C][/ROW]
[ROW][C]153[/C][C]46660[/C][C]33226.6111111111[/C][C]13433.3888888889[/C][/ROW]
[ROW][C]154[/C][C]17547[/C][C]33226.6111111111[/C][C]-15679.6111111111[/C][/ROW]
[ROW][C]155[/C][C]95227[/C][C]107346.75[/C][C]-12119.75[/C][/ROW]
[ROW][C]156[/C][C]152601[/C][C]108090.821428571[/C][C]44510.1785714286[/C][/ROW]
[ROW][C]157[/C][C]101645[/C][C]85787.0909090909[/C][C]15857.9090909091[/C][/ROW]
[ROW][C]158[/C][C]101011[/C][C]108090.821428571[/C][C]-7079.82142857143[/C][/ROW]
[ROW][C]159[/C][C]7176[/C][C]33226.6111111111[/C][C]-26050.6111111111[/C][/ROW]
[ROW][C]160[/C][C]96560[/C][C]162741.8[/C][C]-66181.8[/C][/ROW]
[ROW][C]161[/C][C]175824[/C][C]162741.8[/C][C]13082.2[/C][/ROW]
[ROW][C]162[/C][C]341570[/C][C]322592.294117647[/C][C]18977.705882353[/C][/ROW]
[ROW][C]163[/C][C]103597[/C][C]85787.0909090909[/C][C]17809.9090909091[/C][/ROW]
[ROW][C]164[/C][C]112611[/C][C]108090.821428571[/C][C]4520.17857142857[/C][/ROW]
[ROW][C]165[/C][C]85574[/C][C]85787.0909090909[/C][C]-213.090909090912[/C][/ROW]
[ROW][C]166[/C][C]220801[/C][C]162741.8[/C][C]58059.2[/C][/ROW]
[ROW][C]167[/C][C]92661[/C][C]108090.821428571[/C][C]-15429.8214285714[/C][/ROW]
[ROW][C]168[/C][C]133328[/C][C]108090.821428571[/C][C]25237.1785714286[/C][/ROW]
[ROW][C]169[/C][C]61361[/C][C]85787.0909090909[/C][C]-24426.0909090909[/C][/ROW]
[ROW][C]170[/C][C]125930[/C][C]162741.8[/C][C]-36811.8[/C][/ROW]
[ROW][C]171[/C][C]82316[/C][C]85787.0909090909[/C][C]-3471.09090909091[/C][/ROW]
[ROW][C]172[/C][C]102010[/C][C]108090.821428571[/C][C]-6080.82142857143[/C][/ROW]
[ROW][C]173[/C][C]101523[/C][C]108090.821428571[/C][C]-6567.82142857143[/C][/ROW]
[ROW][C]174[/C][C]41566[/C][C]33226.6111111111[/C][C]8339.38888888889[/C][/ROW]
[ROW][C]175[/C][C]99923[/C][C]162741.8[/C][C]-62818.8[/C][/ROW]
[ROW][C]176[/C][C]22648[/C][C]33226.6111111111[/C][C]-10578.6111111111[/C][/ROW]
[ROW][C]177[/C][C]46698[/C][C]33226.6111111111[/C][C]13471.3888888889[/C][/ROW]
[ROW][C]178[/C][C]131698[/C][C]162741.8[/C][C]-31043.8[/C][/ROW]
[ROW][C]179[/C][C]91735[/C][C]85787.0909090909[/C][C]5947.90909090909[/C][/ROW]
[ROW][C]180[/C][C]79863[/C][C]108090.821428571[/C][C]-28227.8214285714[/C][/ROW]
[ROW][C]181[/C][C]108043[/C][C]108090.821428571[/C][C]-47.8214285714348[/C][/ROW]
[ROW][C]182[/C][C]98866[/C][C]85787.0909090909[/C][C]13078.9090909091[/C][/ROW]
[ROW][C]183[/C][C]120445[/C][C]108090.821428571[/C][C]12354.1785714286[/C][/ROW]
[ROW][C]184[/C][C]116048[/C][C]108090.821428571[/C][C]7957.17857142857[/C][/ROW]
[ROW][C]185[/C][C]250047[/C][C]162741.8[/C][C]87305.2[/C][/ROW]
[ROW][C]186[/C][C]136084[/C][C]108090.821428571[/C][C]27993.1785714286[/C][/ROW]
[ROW][C]187[/C][C]92499[/C][C]85787.0909090909[/C][C]6711.90909090909[/C][/ROW]
[ROW][C]188[/C][C]135781[/C][C]108090.821428571[/C][C]27690.1785714286[/C][/ROW]
[ROW][C]189[/C][C]74408[/C][C]108090.821428571[/C][C]-33682.8214285714[/C][/ROW]
[ROW][C]190[/C][C]81240[/C][C]108090.821428571[/C][C]-26850.8214285714[/C][/ROW]
[ROW][C]191[/C][C]133368[/C][C]162741.8[/C][C]-29373.8[/C][/ROW]
[ROW][C]192[/C][C]98146[/C][C]108090.821428571[/C][C]-9944.82142857143[/C][/ROW]
[ROW][C]193[/C][C]79619[/C][C]85787.0909090909[/C][C]-6168.09090909091[/C][/ROW]
[ROW][C]194[/C][C]59194[/C][C]108090.821428571[/C][C]-48896.8214285714[/C][/ROW]
[ROW][C]195[/C][C]139942[/C][C]108090.821428571[/C][C]31851.1785714286[/C][/ROW]
[ROW][C]196[/C][C]118612[/C][C]108090.821428571[/C][C]10521.1785714286[/C][/ROW]
[ROW][C]197[/C][C]72880[/C][C]85787.0909090909[/C][C]-12907.0909090909[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197065&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197065&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
#ActualsForecastsResiduals
1210907156134.4390243954772.5609756098
2120982107346.7513635.25
3176508156134.4390243920373.5609756098
4179321217099.565217391-37778.5652173913
5123185108090.82142857115094.1785714286
65274633226.611111111119519.3888888889
7385534322592.29411764762941.705882353
83317033226.6111111111-56.6111111111095
9149061156134.43902439-7073.43902439025
10165446156134.439024399311.56097560975
11237213217099.56521739120113.4347826087
12173326156134.4390243917191.5609756098
13133131156134.43902439-23003.4390243902
14258873217099.56521739141773.4347826087
15180083156134.4390243923948.5609756098
16324799322592.2941176472206.70588235295
17230964217099.56521739113864.4347826087
18236785249646.538461538-12861.5384615385
19135473108090.82142857127382.1785714286
20202925217099.565217391-14174.5652173913
21215147217099.565217391-1952.5652173913
22344297217099.565217391127197.434782609
23153935156134.43902439-2199.43902439025
24132943156134.43902439-23191.4390243902
25174724217099.565217391-42375.5652173913
26174415249646.538461538-75231.5384615385
27225548249646.538461538-24098.5384615385
28223632217099.5652173916532.4347826087
29124817156134.43902439-31317.4390243902
30221698217099.5652173914598.4347826087
31210767217099.565217391-6332.5652173913
32170266156134.4390243914131.5609756098
33260561217099.56521739143461.4347826087
3484853107346.75-22493.75
35294424322592.294117647-28168.294117647
36215641217099.565217391-1458.5652173913
37325107249646.53846153875460.4615384615
38167542156134.4390243911407.5609756098
39106408108090.821428571-1682.82142857143
40265769249646.53846153816122.4615384615
41269651322592.294117647-52941.294117647
42149112156134.43902439-7022.43902439025
43152871156134.43902439-3263.43902439025
44111665156134.43902439-44469.4390243902
45116408162741.8-46333.8
46362301322592.29411764739708.705882353
4778800108090.821428571-29290.8214285714
48183167217099.565217391-33932.5652173913
49277965322592.294117647-44627.294117647
50150629217099.565217391-66470.5652173913
51168809156134.4390243912674.5609756098
522418833226.6111111111-9038.61111111111
53329267249646.53846153879620.4615384615
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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')
}