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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 computationFri, 09 Dec 2011 08:55:20 -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/2011/Dec/09/t132343893489zvgi82sevkbq6.htm/, Retrieved Thu, 31 Oct 2024 23:51:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153369, Retrieved Thu, 31 Oct 2024 23:51:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [WS 10 Recursive p...] [2011-12-09 12:55:25] [60c0c94f647e2c90e494ab0f2a2f1926]
-   PD      [Recursive Partitioning (Regression Trees)] [WS 10 Recursive p...] [2011-12-09 13:55:20] [7e9b6bd31a62815918579b1facd0f368] [Current]
-   PD        [Recursive Partitioning (Regression Trees)] [] [2011-12-23 14:13:57] [60c0c94f647e2c90e494ab0f2a2f1926]
-   P           [Recursive Partitioning (Regression Trees)] [] [2011-12-23 14:37:55] [60c0c94f647e2c90e494ab0f2a2f1926]
-   P             [Recursive Partitioning (Regression Trees)] [] [2011-12-23 14:57:43] [60c0c94f647e2c90e494ab0f2a2f1926]
-   PD        [Recursive Partitioning (Regression Trees)] [] [2011-12-23 15:54:41] [53298c36f9bda1a036c4d70d0e7a311d]
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Dataseries X:
252101	62	438	92	34	104	165119
134577	59	330	58	30	111	107269
198520	62	609	62	38	93	93497
189326	94	1015	108	34	119	100269
137449	43	294	55	25	57	91627
65295	27	164	8	31	80	47552
439387	103	1912	134	29	107	233933
33186	19	111	1	18	22	6853
178368	51	698	64	30	103	104380
186657	38	556	77	29	72	98431
261949	96	711	86	38	123	156949
191051	95	495	93	49	164	81817
138866	57	544	44	33	100	59238
296878	66	959	106	46	143	101138
192648	72	540	63	38	79	107158
333462	162	1486	160	52	183	155499
243571	58	635	104	32	123	156274
263451	130	940	86	35	81	121777
155679	48	452	93	25	74	105037
227053	70	617	119	42	158	118661
240028	63	695	107	40	133	131187
388549	90	1046	86	35	128	145026
156540	34	405	50	25	84	107016
148421	43	477	92	46	184	87242
177732	97	1012	123	36	127	91699
191441	105	842	81	35	128	110087
249893	122	994	93	38	118	145447
236812	76	530	113	35	125	143307
142329	45	515	52	28	89	61678
259667	53	766	113	37	122	210080
231625	65	734	112	40	151	165005
176062	67	551	44	42	122	97806
286683	79	718	123	44	162	184471
87485	33	280	38	33	121	27786
322865	83	1055	111	35	132	184458
247082	51	950	77	37	110	98765
344092	104	1035	92	39	135	178441
191653	74	552	74	32	80	100619
114673	31	275	33	17	46	58391
284224	161	986	105	34	127	151672
284195	72	1336	108	33	103	124437
155363	59	565	66	35	95	79929
177306	67	571	69	32	100	123064
144571	49	404	62	35	102	50466
140319	73	985	50	45	45	100991
405267	135	1851	91	38	122	79367
78800	42	330	20	26	66	56968
201970	69	611	101	45	159	106257
302674	99	1249	129	44	153	178412
164733	50	812	93	40	131	98520
194221	68	501	89	33	113	153670
24188	24	218	8	4	7	15049
342263	279	785	79	41	147	174478
65029	17	255	21	18	61	25109
101097	64	454	30	14	41	45824
246088	46	944	86	33	108	116772
273108	75	600	116	49	184	189150
282220	160	977	106	32	115	194404
273495	119	863	127	37	132	185881
214872	74	690	75	32	113	67508
335121	123	1176	138	41	141	188597
267171	106	1013	114	25	65	203618
187938	88	890	55	40	87	87232
229512	78	777	67	35	121	110875
209798	61	521	45	33	112	144756
201345	60	409	88	28	81	129825
163833	113	493	67	31	116	92189
204250	129	757	75	40	132	121158
197813	67	736	114	32	104	96219
132955	60	511	123	25	80	84128
216092	59	789	86	42	145	97960
73566	32	385	22	23	67	23824
213198	67	644	67	42	159	103515
181713	49	664	77	38	90	91313
148698	49	505	105	34	120	85407
300103	70	878	119	38	126	95871
251437	78	769	88	32	118	143846
197295	101	499	78	37	112	155387
158163	55	546	112	34	123	74429
155529	57	551	66	33	98	74004
132672	41	565	58	25	78	71987
377205	100	1086	132	40	119	150629
145905	66	649	30	26	99	68580
223701	86	540	100	40	81	119855
80953	25	437	49	8	27	55792
130805	47	732	26	27	77	25157
135082	48	308	67	32	118	90895
300170	154	1236	57	33	122	117510
271806	95	783	95	50	103	144774
150949	96	933	139	37	129	77529
225805	79	710	73	33	69	103123
197389	67	563	134	34	121	104669
156583	56	508	37	28	81	82414
222599	66	936	98	32	119	82390
261601	70	838	58	32	116	128446
178489	35	523	78	32	123	111542
200657	43	500	88	31	111	136048
259084	67	691	142	35	100	197257
313075	130	1060	127	58	221	162079
346933	100	1232	139	27	95	206286
246440	104	735	108	45	153	109858
252444	58	757	128	37	118	182125
159965	159	574	62	32	50	74168
43287	14	214	13	19	64	19630
172239	68	661	89	22	34	88634
181897	119	630	83	35	76	128321
227681	43	1015	116	36	112	118936
260464	81	893	157	36	115	127044
106288	54	293	28	23	69	178377
109632	76	446	83	36	108	69581
268905	58	538	72	36	130	168019
266805	78	627	134	42	110	113598
23623	11	156	12	1	0	5841
152474	65	577	106	32	83	93116
61857	25	192	23	11	30	24610
144889	43	437	83	40	106	60611
346600	99	1054	126	34	91	226620
21054	16	146	4	0	0	6622
224051	45	751	71	27	69	121996
31414	19	200	18	8	9	13155
261043	105	1050	98	35	123	154158
197819	57	590	66	41	143	78489
154984	73	430	44	40	125	22007
112933	45	467	29	28	81	72530
38214	34	276	16	8	21	13983
158671	33	528	56	35	124	73397
302148	70	898	112	47	168	143878
177918	55	411	46	46	149	119956
350552	70	1362	129	42	147	181558
275578	91	743	139	48	145	208236
366217	105	1068	136	49	172	237085
172464	31	431	66	35	126	110297
94381	35	380	42	32	89	61394
243875	278	788	70	36	137	81420
382487	153	1367	97	42	149	191154
114525	40	449	49	35	121	11798
335681	119	1461	113	37	133	135724
147989	72	651	55	34	93	68614
216638	44	494	100	36	119	139926
192862	72	667	80	36	102	105203
184818	107	510	29	32	45	80338
336707	105	1472	95	33	104	121376
215836	76	675	114	35	111	124922
173260	63	716	41	21	78	10901
271773	89	814	128	40	120	135471
130908	52	556	142	49	176	66395
204009	75	887	88	33	109	134041
245514	92	663	147	39	132	153554
1	0	0	0	0	0	0
14688	10	85	4	0	0	7953
98	1	0	0	0	0	0
455	2	0	0	0	0	0
0	0	0	0	0	0	0
0	0	0	0	0	0	0
195765	75	607	56	33	78	98922
326038	121	934	121	42	104	165395
0	0	0	0	0	0	0
203	4	0	0	0	0	0
7199	5	74	7	0	0	4245
46660	20	259	12	5	13	21509
17547	5	69	0	1	4	7670
107465	38	267	37	38	65	15167
969	2	0	0	0	0	0
173102	58	517	47	28	55	63891




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153369&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153369&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153369&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 time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Goodness of Fit
Correlation0.9517
R-squared0.9058
RMSE29698.7641

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9517 \tabularnewline
R-squared & 0.9058 \tabularnewline
RMSE & 29698.7641 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153369&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9517[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9058[/C][/ROW]
[ROW][C]RMSE[/C][C]29698.7641[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153369&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153369&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
Correlation0.9517
R-squared0.9058
RMSE29698.7641







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1252101221604.26315789530496.7368421053
21345779972134856
3198520199158.774193548-638.774193548394
4189326199158.774193548-9832.77419354839
51374499972137728
66529532650.545454545532644.4545454545
7439387343614.195772.9
83318632650.5454545455535.454545454544
9178368199158.774193548-20790.7741935484
10186657167004.77272727319652.2272727273
11261949274750.533333333-12801.5333333333
12191051167004.77272727324046.2272727273
13138866129343.1538461549522.84615384616
14296878199158.77419354897719.2258064516
15192648167004.77272727325643.2272727273
16333462343614.1-10152.1
17243571221604.26315789521966.7368421053
18263451274750.533333333-11299.5333333333
19155679167004.772727273-11325.7727272727
20227053221604.2631578955448.73684210525
21240028246655.25-6627.25
22388549343614.144934.9
23156540167004.772727273-10464.7727272727
24148421167004.772727273-18583.7727272727
25177732199158.774193548-21426.7741935484
26191441199158.774193548-7717.77419354839
27249893274750.533333333-24857.5333333333
28236812221604.26315789515207.7368421053
29142329129343.15384615412985.8461538462
30259667246655.2513011.75
31231625246655.25-15030.25
32176062167004.7727272739057.22727272726
33286683274750.53333333311932.4666666667
348748599721-12236
35322865343614.1-20749.1
36247082199158.77419354847923.2258064516
37344092343614.1477.900000000023
38191653167004.77272727324648.2272727273
391146739972114952
40284224274750.5333333339473.46666666667
41284195343614.1-59419.1
42155363167004.772727273-11641.7727272727
43177306221604.263157895-44298.2631578947
44144571129343.15384615415227.8461538462
45140319199158.774193548-58839.7741935484
46405267343614.161652.9
477880099721-20921
48201970199158.7741935482811.22580645161
49302674343614.1-40940.1
50164733199158.774193548-34425.7741935484
51194221221604.263157895-27383.2631578947
522418832650.5454545455-8462.54545454546
53342263274750.53333333367512.4666666667
546502999721-34692
55101097129343.153846154-28246.1538461538
56246088246655.25-567.25
57273108221604.26315789551503.7368421053
58282220274750.5333333337469.46666666667
59273495274750.533333333-1255.53333333333
60214872199158.77419354815713.2258064516
61335121343614.1-8493.09999999998
62267171274750.533333333-7579.53333333333
63187938199158.774193548-11220.7741935484
64229512199158.77419354830353.2258064516
65209798221604.263157895-11806.2631578947
66201345221604.263157895-20259.2631578947
67163833167004.772727273-3171.77272727274
68204250274750.533333333-70500.5333333333
69197813199158.774193548-1345.77419354839
70132955167004.772727273-34049.7727272727
71216092199158.77419354816933.2258064516
727356699721-26155
73213198199158.77419354814039.2258064516
74181713199158.774193548-17445.7741935484
75148698167004.772727273-18306.7727272727
76300103199158.774193548100944.225806452
77251437246655.254781.75
78197295221604.263157895-24309.2631578947
79158163167004.772727273-8841.77272727274
80155529167004.772727273-11475.7727272727
81132672129343.1538461543328.84615384616
82377205343614.133590.9
83145905199158.774193548-53253.7741935484
84223701221604.2631578952096.73684210525
8580953129343.153846154-48390.1538461538
86130805199158.774193548-68353.7741935484
871350829972135361
88300170343614.1-43444.1
89271806274750.533333333-2944.53333333333
90150949199158.774193548-48209.7741935484
91225805199158.77419354826646.2258064516
92197389167004.77272727330384.2272727273
93156583167004.772727273-10421.7727272727
94222599199158.77419354823440.2258064516
95261601246655.2514945.75
96178489167004.77272727311484.2272727273
97200657221604.263157895-20947.2631578947
98259084246655.2512428.75
99313075343614.1-30539.1
100346933343614.13318.90000000002
101246440199158.77419354847281.2258064516
102252444246655.255788.75
103159965167004.772727273-7039.77272727274
1044328732650.545454545510636.4545454545
105172239199158.774193548-26919.7741935484
106181897221604.263157895-39707.2631578947
107227681246655.25-18974.25
108260464274750.533333333-14286.5333333333
109106288997216567
110109632129343.153846154-19711.1538461538
111268905221604.26315789547300.7368421053
112266805221604.26315789545200.7368421053
1132362332650.5454545455-9027.54545454546
114152474167004.772727273-14530.7727272727
1156185799721-37864
116144889129343.15384615415545.8461538462
117346600343614.12985.90000000002
1182105432650.5454545455-11596.5454545455
119224051246655.25-22604.25
1203141432650.5454545455-1236.54545454546
121261043343614.1-82571.1
122197819199158.774193548-1339.77419354839
123154984129343.15384615425640.8461538462
124112933129343.153846154-16410.1538461538
1253821432650.54545454555563.45454545454
126158671167004.772727273-8333.77272727274
127302148246655.2555492.75
128177918221604.263157895-43686.2631578947
129350552343614.16937.90000000002
130275578274750.533333333827.466666666674
131366217343614.122602.9
132172464167004.7727272735459.22727272726
1339438199721-5340
134243875199158.77419354844716.2258064516
135382487343614.138872.9
136114525129343.153846154-14818.1538461538
137335681343614.1-7933.09999999998
138147989199158.774193548-51169.7741935484
139216638221604.263157895-4966.26315789475
140192862199158.774193548-6296.77419354839
141184818167004.77272727317813.2272727273
142336707343614.1-6907.09999999998
143215836221604.263157895-5768.26315789475
144173260199158.774193548-25898.7741935484
145271773274750.533333333-2977.53333333333
146130908129343.1538461541564.84615384616
147204009246655.25-42646.25
148245514221604.26315789523909.7368421053
1491991.666666666667-990.666666666667
1501468832650.5454545455-17962.5454545455
15198991.666666666667-893.666666666667
152455991.666666666667-536.666666666667
1530991.666666666667-991.666666666667
1540991.666666666667-991.666666666667
155195765199158.774193548-3393.77419354839
156326038274750.53333333351287.4666666667
1570991.666666666667-991.666666666667
158203991.666666666667-788.666666666667
1597199991.6666666666676207.33333333333
1604666032650.545454545514009.4545454545
1611754732650.5454545455-15103.5454545455
162107465997217744
163969991.666666666667-22.6666666666666
164173102129343.15384615443758.8461538462

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 252101 & 221604.263157895 & 30496.7368421053 \tabularnewline
2 & 134577 & 99721 & 34856 \tabularnewline
3 & 198520 & 199158.774193548 & -638.774193548394 \tabularnewline
4 & 189326 & 199158.774193548 & -9832.77419354839 \tabularnewline
5 & 137449 & 99721 & 37728 \tabularnewline
6 & 65295 & 32650.5454545455 & 32644.4545454545 \tabularnewline
7 & 439387 & 343614.1 & 95772.9 \tabularnewline
8 & 33186 & 32650.5454545455 & 535.454545454544 \tabularnewline
9 & 178368 & 199158.774193548 & -20790.7741935484 \tabularnewline
10 & 186657 & 167004.772727273 & 19652.2272727273 \tabularnewline
11 & 261949 & 274750.533333333 & -12801.5333333333 \tabularnewline
12 & 191051 & 167004.772727273 & 24046.2272727273 \tabularnewline
13 & 138866 & 129343.153846154 & 9522.84615384616 \tabularnewline
14 & 296878 & 199158.774193548 & 97719.2258064516 \tabularnewline
15 & 192648 & 167004.772727273 & 25643.2272727273 \tabularnewline
16 & 333462 & 343614.1 & -10152.1 \tabularnewline
17 & 243571 & 221604.263157895 & 21966.7368421053 \tabularnewline
18 & 263451 & 274750.533333333 & -11299.5333333333 \tabularnewline
19 & 155679 & 167004.772727273 & -11325.7727272727 \tabularnewline
20 & 227053 & 221604.263157895 & 5448.73684210525 \tabularnewline
21 & 240028 & 246655.25 & -6627.25 \tabularnewline
22 & 388549 & 343614.1 & 44934.9 \tabularnewline
23 & 156540 & 167004.772727273 & -10464.7727272727 \tabularnewline
24 & 148421 & 167004.772727273 & -18583.7727272727 \tabularnewline
25 & 177732 & 199158.774193548 & -21426.7741935484 \tabularnewline
26 & 191441 & 199158.774193548 & -7717.77419354839 \tabularnewline
27 & 249893 & 274750.533333333 & -24857.5333333333 \tabularnewline
28 & 236812 & 221604.263157895 & 15207.7368421053 \tabularnewline
29 & 142329 & 129343.153846154 & 12985.8461538462 \tabularnewline
30 & 259667 & 246655.25 & 13011.75 \tabularnewline
31 & 231625 & 246655.25 & -15030.25 \tabularnewline
32 & 176062 & 167004.772727273 & 9057.22727272726 \tabularnewline
33 & 286683 & 274750.533333333 & 11932.4666666667 \tabularnewline
34 & 87485 & 99721 & -12236 \tabularnewline
35 & 322865 & 343614.1 & -20749.1 \tabularnewline
36 & 247082 & 199158.774193548 & 47923.2258064516 \tabularnewline
37 & 344092 & 343614.1 & 477.900000000023 \tabularnewline
38 & 191653 & 167004.772727273 & 24648.2272727273 \tabularnewline
39 & 114673 & 99721 & 14952 \tabularnewline
40 & 284224 & 274750.533333333 & 9473.46666666667 \tabularnewline
41 & 284195 & 343614.1 & -59419.1 \tabularnewline
42 & 155363 & 167004.772727273 & -11641.7727272727 \tabularnewline
43 & 177306 & 221604.263157895 & -44298.2631578947 \tabularnewline
44 & 144571 & 129343.153846154 & 15227.8461538462 \tabularnewline
45 & 140319 & 199158.774193548 & -58839.7741935484 \tabularnewline
46 & 405267 & 343614.1 & 61652.9 \tabularnewline
47 & 78800 & 99721 & -20921 \tabularnewline
48 & 201970 & 199158.774193548 & 2811.22580645161 \tabularnewline
49 & 302674 & 343614.1 & -40940.1 \tabularnewline
50 & 164733 & 199158.774193548 & -34425.7741935484 \tabularnewline
51 & 194221 & 221604.263157895 & -27383.2631578947 \tabularnewline
52 & 24188 & 32650.5454545455 & -8462.54545454546 \tabularnewline
53 & 342263 & 274750.533333333 & 67512.4666666667 \tabularnewline
54 & 65029 & 99721 & -34692 \tabularnewline
55 & 101097 & 129343.153846154 & -28246.1538461538 \tabularnewline
56 & 246088 & 246655.25 & -567.25 \tabularnewline
57 & 273108 & 221604.263157895 & 51503.7368421053 \tabularnewline
58 & 282220 & 274750.533333333 & 7469.46666666667 \tabularnewline
59 & 273495 & 274750.533333333 & -1255.53333333333 \tabularnewline
60 & 214872 & 199158.774193548 & 15713.2258064516 \tabularnewline
61 & 335121 & 343614.1 & -8493.09999999998 \tabularnewline
62 & 267171 & 274750.533333333 & -7579.53333333333 \tabularnewline
63 & 187938 & 199158.774193548 & -11220.7741935484 \tabularnewline
64 & 229512 & 199158.774193548 & 30353.2258064516 \tabularnewline
65 & 209798 & 221604.263157895 & -11806.2631578947 \tabularnewline
66 & 201345 & 221604.263157895 & -20259.2631578947 \tabularnewline
67 & 163833 & 167004.772727273 & -3171.77272727274 \tabularnewline
68 & 204250 & 274750.533333333 & -70500.5333333333 \tabularnewline
69 & 197813 & 199158.774193548 & -1345.77419354839 \tabularnewline
70 & 132955 & 167004.772727273 & -34049.7727272727 \tabularnewline
71 & 216092 & 199158.774193548 & 16933.2258064516 \tabularnewline
72 & 73566 & 99721 & -26155 \tabularnewline
73 & 213198 & 199158.774193548 & 14039.2258064516 \tabularnewline
74 & 181713 & 199158.774193548 & -17445.7741935484 \tabularnewline
75 & 148698 & 167004.772727273 & -18306.7727272727 \tabularnewline
76 & 300103 & 199158.774193548 & 100944.225806452 \tabularnewline
77 & 251437 & 246655.25 & 4781.75 \tabularnewline
78 & 197295 & 221604.263157895 & -24309.2631578947 \tabularnewline
79 & 158163 & 167004.772727273 & -8841.77272727274 \tabularnewline
80 & 155529 & 167004.772727273 & -11475.7727272727 \tabularnewline
81 & 132672 & 129343.153846154 & 3328.84615384616 \tabularnewline
82 & 377205 & 343614.1 & 33590.9 \tabularnewline
83 & 145905 & 199158.774193548 & -53253.7741935484 \tabularnewline
84 & 223701 & 221604.263157895 & 2096.73684210525 \tabularnewline
85 & 80953 & 129343.153846154 & -48390.1538461538 \tabularnewline
86 & 130805 & 199158.774193548 & -68353.7741935484 \tabularnewline
87 & 135082 & 99721 & 35361 \tabularnewline
88 & 300170 & 343614.1 & -43444.1 \tabularnewline
89 & 271806 & 274750.533333333 & -2944.53333333333 \tabularnewline
90 & 150949 & 199158.774193548 & -48209.7741935484 \tabularnewline
91 & 225805 & 199158.774193548 & 26646.2258064516 \tabularnewline
92 & 197389 & 167004.772727273 & 30384.2272727273 \tabularnewline
93 & 156583 & 167004.772727273 & -10421.7727272727 \tabularnewline
94 & 222599 & 199158.774193548 & 23440.2258064516 \tabularnewline
95 & 261601 & 246655.25 & 14945.75 \tabularnewline
96 & 178489 & 167004.772727273 & 11484.2272727273 \tabularnewline
97 & 200657 & 221604.263157895 & -20947.2631578947 \tabularnewline
98 & 259084 & 246655.25 & 12428.75 \tabularnewline
99 & 313075 & 343614.1 & -30539.1 \tabularnewline
100 & 346933 & 343614.1 & 3318.90000000002 \tabularnewline
101 & 246440 & 199158.774193548 & 47281.2258064516 \tabularnewline
102 & 252444 & 246655.25 & 5788.75 \tabularnewline
103 & 159965 & 167004.772727273 & -7039.77272727274 \tabularnewline
104 & 43287 & 32650.5454545455 & 10636.4545454545 \tabularnewline
105 & 172239 & 199158.774193548 & -26919.7741935484 \tabularnewline
106 & 181897 & 221604.263157895 & -39707.2631578947 \tabularnewline
107 & 227681 & 246655.25 & -18974.25 \tabularnewline
108 & 260464 & 274750.533333333 & -14286.5333333333 \tabularnewline
109 & 106288 & 99721 & 6567 \tabularnewline
110 & 109632 & 129343.153846154 & -19711.1538461538 \tabularnewline
111 & 268905 & 221604.263157895 & 47300.7368421053 \tabularnewline
112 & 266805 & 221604.263157895 & 45200.7368421053 \tabularnewline
113 & 23623 & 32650.5454545455 & -9027.54545454546 \tabularnewline
114 & 152474 & 167004.772727273 & -14530.7727272727 \tabularnewline
115 & 61857 & 99721 & -37864 \tabularnewline
116 & 144889 & 129343.153846154 & 15545.8461538462 \tabularnewline
117 & 346600 & 343614.1 & 2985.90000000002 \tabularnewline
118 & 21054 & 32650.5454545455 & -11596.5454545455 \tabularnewline
119 & 224051 & 246655.25 & -22604.25 \tabularnewline
120 & 31414 & 32650.5454545455 & -1236.54545454546 \tabularnewline
121 & 261043 & 343614.1 & -82571.1 \tabularnewline
122 & 197819 & 199158.774193548 & -1339.77419354839 \tabularnewline
123 & 154984 & 129343.153846154 & 25640.8461538462 \tabularnewline
124 & 112933 & 129343.153846154 & -16410.1538461538 \tabularnewline
125 & 38214 & 32650.5454545455 & 5563.45454545454 \tabularnewline
126 & 158671 & 167004.772727273 & -8333.77272727274 \tabularnewline
127 & 302148 & 246655.25 & 55492.75 \tabularnewline
128 & 177918 & 221604.263157895 & -43686.2631578947 \tabularnewline
129 & 350552 & 343614.1 & 6937.90000000002 \tabularnewline
130 & 275578 & 274750.533333333 & 827.466666666674 \tabularnewline
131 & 366217 & 343614.1 & 22602.9 \tabularnewline
132 & 172464 & 167004.772727273 & 5459.22727272726 \tabularnewline
133 & 94381 & 99721 & -5340 \tabularnewline
134 & 243875 & 199158.774193548 & 44716.2258064516 \tabularnewline
135 & 382487 & 343614.1 & 38872.9 \tabularnewline
136 & 114525 & 129343.153846154 & -14818.1538461538 \tabularnewline
137 & 335681 & 343614.1 & -7933.09999999998 \tabularnewline
138 & 147989 & 199158.774193548 & -51169.7741935484 \tabularnewline
139 & 216638 & 221604.263157895 & -4966.26315789475 \tabularnewline
140 & 192862 & 199158.774193548 & -6296.77419354839 \tabularnewline
141 & 184818 & 167004.772727273 & 17813.2272727273 \tabularnewline
142 & 336707 & 343614.1 & -6907.09999999998 \tabularnewline
143 & 215836 & 221604.263157895 & -5768.26315789475 \tabularnewline
144 & 173260 & 199158.774193548 & -25898.7741935484 \tabularnewline
145 & 271773 & 274750.533333333 & -2977.53333333333 \tabularnewline
146 & 130908 & 129343.153846154 & 1564.84615384616 \tabularnewline
147 & 204009 & 246655.25 & -42646.25 \tabularnewline
148 & 245514 & 221604.263157895 & 23909.7368421053 \tabularnewline
149 & 1 & 991.666666666667 & -990.666666666667 \tabularnewline
150 & 14688 & 32650.5454545455 & -17962.5454545455 \tabularnewline
151 & 98 & 991.666666666667 & -893.666666666667 \tabularnewline
152 & 455 & 991.666666666667 & -536.666666666667 \tabularnewline
153 & 0 & 991.666666666667 & -991.666666666667 \tabularnewline
154 & 0 & 991.666666666667 & -991.666666666667 \tabularnewline
155 & 195765 & 199158.774193548 & -3393.77419354839 \tabularnewline
156 & 326038 & 274750.533333333 & 51287.4666666667 \tabularnewline
157 & 0 & 991.666666666667 & -991.666666666667 \tabularnewline
158 & 203 & 991.666666666667 & -788.666666666667 \tabularnewline
159 & 7199 & 991.666666666667 & 6207.33333333333 \tabularnewline
160 & 46660 & 32650.5454545455 & 14009.4545454545 \tabularnewline
161 & 17547 & 32650.5454545455 & -15103.5454545455 \tabularnewline
162 & 107465 & 99721 & 7744 \tabularnewline
163 & 969 & 991.666666666667 & -22.6666666666666 \tabularnewline
164 & 173102 & 129343.153846154 & 43758.8461538462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153369&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]252101[/C][C]221604.263157895[/C][C]30496.7368421053[/C][/ROW]
[ROW][C]2[/C][C]134577[/C][C]99721[/C][C]34856[/C][/ROW]
[ROW][C]3[/C][C]198520[/C][C]199158.774193548[/C][C]-638.774193548394[/C][/ROW]
[ROW][C]4[/C][C]189326[/C][C]199158.774193548[/C][C]-9832.77419354839[/C][/ROW]
[ROW][C]5[/C][C]137449[/C][C]99721[/C][C]37728[/C][/ROW]
[ROW][C]6[/C][C]65295[/C][C]32650.5454545455[/C][C]32644.4545454545[/C][/ROW]
[ROW][C]7[/C][C]439387[/C][C]343614.1[/C][C]95772.9[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]32650.5454545455[/C][C]535.454545454544[/C][/ROW]
[ROW][C]9[/C][C]178368[/C][C]199158.774193548[/C][C]-20790.7741935484[/C][/ROW]
[ROW][C]10[/C][C]186657[/C][C]167004.772727273[/C][C]19652.2272727273[/C][/ROW]
[ROW][C]11[/C][C]261949[/C][C]274750.533333333[/C][C]-12801.5333333333[/C][/ROW]
[ROW][C]12[/C][C]191051[/C][C]167004.772727273[/C][C]24046.2272727273[/C][/ROW]
[ROW][C]13[/C][C]138866[/C][C]129343.153846154[/C][C]9522.84615384616[/C][/ROW]
[ROW][C]14[/C][C]296878[/C][C]199158.774193548[/C][C]97719.2258064516[/C][/ROW]
[ROW][C]15[/C][C]192648[/C][C]167004.772727273[/C][C]25643.2272727273[/C][/ROW]
[ROW][C]16[/C][C]333462[/C][C]343614.1[/C][C]-10152.1[/C][/ROW]
[ROW][C]17[/C][C]243571[/C][C]221604.263157895[/C][C]21966.7368421053[/C][/ROW]
[ROW][C]18[/C][C]263451[/C][C]274750.533333333[/C][C]-11299.5333333333[/C][/ROW]
[ROW][C]19[/C][C]155679[/C][C]167004.772727273[/C][C]-11325.7727272727[/C][/ROW]
[ROW][C]20[/C][C]227053[/C][C]221604.263157895[/C][C]5448.73684210525[/C][/ROW]
[ROW][C]21[/C][C]240028[/C][C]246655.25[/C][C]-6627.25[/C][/ROW]
[ROW][C]22[/C][C]388549[/C][C]343614.1[/C][C]44934.9[/C][/ROW]
[ROW][C]23[/C][C]156540[/C][C]167004.772727273[/C][C]-10464.7727272727[/C][/ROW]
[ROW][C]24[/C][C]148421[/C][C]167004.772727273[/C][C]-18583.7727272727[/C][/ROW]
[ROW][C]25[/C][C]177732[/C][C]199158.774193548[/C][C]-21426.7741935484[/C][/ROW]
[ROW][C]26[/C][C]191441[/C][C]199158.774193548[/C][C]-7717.77419354839[/C][/ROW]
[ROW][C]27[/C][C]249893[/C][C]274750.533333333[/C][C]-24857.5333333333[/C][/ROW]
[ROW][C]28[/C][C]236812[/C][C]221604.263157895[/C][C]15207.7368421053[/C][/ROW]
[ROW][C]29[/C][C]142329[/C][C]129343.153846154[/C][C]12985.8461538462[/C][/ROW]
[ROW][C]30[/C][C]259667[/C][C]246655.25[/C][C]13011.75[/C][/ROW]
[ROW][C]31[/C][C]231625[/C][C]246655.25[/C][C]-15030.25[/C][/ROW]
[ROW][C]32[/C][C]176062[/C][C]167004.772727273[/C][C]9057.22727272726[/C][/ROW]
[ROW][C]33[/C][C]286683[/C][C]274750.533333333[/C][C]11932.4666666667[/C][/ROW]
[ROW][C]34[/C][C]87485[/C][C]99721[/C][C]-12236[/C][/ROW]
[ROW][C]35[/C][C]322865[/C][C]343614.1[/C][C]-20749.1[/C][/ROW]
[ROW][C]36[/C][C]247082[/C][C]199158.774193548[/C][C]47923.2258064516[/C][/ROW]
[ROW][C]37[/C][C]344092[/C][C]343614.1[/C][C]477.900000000023[/C][/ROW]
[ROW][C]38[/C][C]191653[/C][C]167004.772727273[/C][C]24648.2272727273[/C][/ROW]
[ROW][C]39[/C][C]114673[/C][C]99721[/C][C]14952[/C][/ROW]
[ROW][C]40[/C][C]284224[/C][C]274750.533333333[/C][C]9473.46666666667[/C][/ROW]
[ROW][C]41[/C][C]284195[/C][C]343614.1[/C][C]-59419.1[/C][/ROW]
[ROW][C]42[/C][C]155363[/C][C]167004.772727273[/C][C]-11641.7727272727[/C][/ROW]
[ROW][C]43[/C][C]177306[/C][C]221604.263157895[/C][C]-44298.2631578947[/C][/ROW]
[ROW][C]44[/C][C]144571[/C][C]129343.153846154[/C][C]15227.8461538462[/C][/ROW]
[ROW][C]45[/C][C]140319[/C][C]199158.774193548[/C][C]-58839.7741935484[/C][/ROW]
[ROW][C]46[/C][C]405267[/C][C]343614.1[/C][C]61652.9[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]99721[/C][C]-20921[/C][/ROW]
[ROW][C]48[/C][C]201970[/C][C]199158.774193548[/C][C]2811.22580645161[/C][/ROW]
[ROW][C]49[/C][C]302674[/C][C]343614.1[/C][C]-40940.1[/C][/ROW]
[ROW][C]50[/C][C]164733[/C][C]199158.774193548[/C][C]-34425.7741935484[/C][/ROW]
[ROW][C]51[/C][C]194221[/C][C]221604.263157895[/C][C]-27383.2631578947[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]32650.5454545455[/C][C]-8462.54545454546[/C][/ROW]
[ROW][C]53[/C][C]342263[/C][C]274750.533333333[/C][C]67512.4666666667[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]99721[/C][C]-34692[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]129343.153846154[/C][C]-28246.1538461538[/C][/ROW]
[ROW][C]56[/C][C]246088[/C][C]246655.25[/C][C]-567.25[/C][/ROW]
[ROW][C]57[/C][C]273108[/C][C]221604.263157895[/C][C]51503.7368421053[/C][/ROW]
[ROW][C]58[/C][C]282220[/C][C]274750.533333333[/C][C]7469.46666666667[/C][/ROW]
[ROW][C]59[/C][C]273495[/C][C]274750.533333333[/C][C]-1255.53333333333[/C][/ROW]
[ROW][C]60[/C][C]214872[/C][C]199158.774193548[/C][C]15713.2258064516[/C][/ROW]
[ROW][C]61[/C][C]335121[/C][C]343614.1[/C][C]-8493.09999999998[/C][/ROW]
[ROW][C]62[/C][C]267171[/C][C]274750.533333333[/C][C]-7579.53333333333[/C][/ROW]
[ROW][C]63[/C][C]187938[/C][C]199158.774193548[/C][C]-11220.7741935484[/C][/ROW]
[ROW][C]64[/C][C]229512[/C][C]199158.774193548[/C][C]30353.2258064516[/C][/ROW]
[ROW][C]65[/C][C]209798[/C][C]221604.263157895[/C][C]-11806.2631578947[/C][/ROW]
[ROW][C]66[/C][C]201345[/C][C]221604.263157895[/C][C]-20259.2631578947[/C][/ROW]
[ROW][C]67[/C][C]163833[/C][C]167004.772727273[/C][C]-3171.77272727274[/C][/ROW]
[ROW][C]68[/C][C]204250[/C][C]274750.533333333[/C][C]-70500.5333333333[/C][/ROW]
[ROW][C]69[/C][C]197813[/C][C]199158.774193548[/C][C]-1345.77419354839[/C][/ROW]
[ROW][C]70[/C][C]132955[/C][C]167004.772727273[/C][C]-34049.7727272727[/C][/ROW]
[ROW][C]71[/C][C]216092[/C][C]199158.774193548[/C][C]16933.2258064516[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]99721[/C][C]-26155[/C][/ROW]
[ROW][C]73[/C][C]213198[/C][C]199158.774193548[/C][C]14039.2258064516[/C][/ROW]
[ROW][C]74[/C][C]181713[/C][C]199158.774193548[/C][C]-17445.7741935484[/C][/ROW]
[ROW][C]75[/C][C]148698[/C][C]167004.772727273[/C][C]-18306.7727272727[/C][/ROW]
[ROW][C]76[/C][C]300103[/C][C]199158.774193548[/C][C]100944.225806452[/C][/ROW]
[ROW][C]77[/C][C]251437[/C][C]246655.25[/C][C]4781.75[/C][/ROW]
[ROW][C]78[/C][C]197295[/C][C]221604.263157895[/C][C]-24309.2631578947[/C][/ROW]
[ROW][C]79[/C][C]158163[/C][C]167004.772727273[/C][C]-8841.77272727274[/C][/ROW]
[ROW][C]80[/C][C]155529[/C][C]167004.772727273[/C][C]-11475.7727272727[/C][/ROW]
[ROW][C]81[/C][C]132672[/C][C]129343.153846154[/C][C]3328.84615384616[/C][/ROW]
[ROW][C]82[/C][C]377205[/C][C]343614.1[/C][C]33590.9[/C][/ROW]
[ROW][C]83[/C][C]145905[/C][C]199158.774193548[/C][C]-53253.7741935484[/C][/ROW]
[ROW][C]84[/C][C]223701[/C][C]221604.263157895[/C][C]2096.73684210525[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]129343.153846154[/C][C]-48390.1538461538[/C][/ROW]
[ROW][C]86[/C][C]130805[/C][C]199158.774193548[/C][C]-68353.7741935484[/C][/ROW]
[ROW][C]87[/C][C]135082[/C][C]99721[/C][C]35361[/C][/ROW]
[ROW][C]88[/C][C]300170[/C][C]343614.1[/C][C]-43444.1[/C][/ROW]
[ROW][C]89[/C][C]271806[/C][C]274750.533333333[/C][C]-2944.53333333333[/C][/ROW]
[ROW][C]90[/C][C]150949[/C][C]199158.774193548[/C][C]-48209.7741935484[/C][/ROW]
[ROW][C]91[/C][C]225805[/C][C]199158.774193548[/C][C]26646.2258064516[/C][/ROW]
[ROW][C]92[/C][C]197389[/C][C]167004.772727273[/C][C]30384.2272727273[/C][/ROW]
[ROW][C]93[/C][C]156583[/C][C]167004.772727273[/C][C]-10421.7727272727[/C][/ROW]
[ROW][C]94[/C][C]222599[/C][C]199158.774193548[/C][C]23440.2258064516[/C][/ROW]
[ROW][C]95[/C][C]261601[/C][C]246655.25[/C][C]14945.75[/C][/ROW]
[ROW][C]96[/C][C]178489[/C][C]167004.772727273[/C][C]11484.2272727273[/C][/ROW]
[ROW][C]97[/C][C]200657[/C][C]221604.263157895[/C][C]-20947.2631578947[/C][/ROW]
[ROW][C]98[/C][C]259084[/C][C]246655.25[/C][C]12428.75[/C][/ROW]
[ROW][C]99[/C][C]313075[/C][C]343614.1[/C][C]-30539.1[/C][/ROW]
[ROW][C]100[/C][C]346933[/C][C]343614.1[/C][C]3318.90000000002[/C][/ROW]
[ROW][C]101[/C][C]246440[/C][C]199158.774193548[/C][C]47281.2258064516[/C][/ROW]
[ROW][C]102[/C][C]252444[/C][C]246655.25[/C][C]5788.75[/C][/ROW]
[ROW][C]103[/C][C]159965[/C][C]167004.772727273[/C][C]-7039.77272727274[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]32650.5454545455[/C][C]10636.4545454545[/C][/ROW]
[ROW][C]105[/C][C]172239[/C][C]199158.774193548[/C][C]-26919.7741935484[/C][/ROW]
[ROW][C]106[/C][C]181897[/C][C]221604.263157895[/C][C]-39707.2631578947[/C][/ROW]
[ROW][C]107[/C][C]227681[/C][C]246655.25[/C][C]-18974.25[/C][/ROW]
[ROW][C]108[/C][C]260464[/C][C]274750.533333333[/C][C]-14286.5333333333[/C][/ROW]
[ROW][C]109[/C][C]106288[/C][C]99721[/C][C]6567[/C][/ROW]
[ROW][C]110[/C][C]109632[/C][C]129343.153846154[/C][C]-19711.1538461538[/C][/ROW]
[ROW][C]111[/C][C]268905[/C][C]221604.263157895[/C][C]47300.7368421053[/C][/ROW]
[ROW][C]112[/C][C]266805[/C][C]221604.263157895[/C][C]45200.7368421053[/C][/ROW]
[ROW][C]113[/C][C]23623[/C][C]32650.5454545455[/C][C]-9027.54545454546[/C][/ROW]
[ROW][C]114[/C][C]152474[/C][C]167004.772727273[/C][C]-14530.7727272727[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]99721[/C][C]-37864[/C][/ROW]
[ROW][C]116[/C][C]144889[/C][C]129343.153846154[/C][C]15545.8461538462[/C][/ROW]
[ROW][C]117[/C][C]346600[/C][C]343614.1[/C][C]2985.90000000002[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]32650.5454545455[/C][C]-11596.5454545455[/C][/ROW]
[ROW][C]119[/C][C]224051[/C][C]246655.25[/C][C]-22604.25[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]32650.5454545455[/C][C]-1236.54545454546[/C][/ROW]
[ROW][C]121[/C][C]261043[/C][C]343614.1[/C][C]-82571.1[/C][/ROW]
[ROW][C]122[/C][C]197819[/C][C]199158.774193548[/C][C]-1339.77419354839[/C][/ROW]
[ROW][C]123[/C][C]154984[/C][C]129343.153846154[/C][C]25640.8461538462[/C][/ROW]
[ROW][C]124[/C][C]112933[/C][C]129343.153846154[/C][C]-16410.1538461538[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]32650.5454545455[/C][C]5563.45454545454[/C][/ROW]
[ROW][C]126[/C][C]158671[/C][C]167004.772727273[/C][C]-8333.77272727274[/C][/ROW]
[ROW][C]127[/C][C]302148[/C][C]246655.25[/C][C]55492.75[/C][/ROW]
[ROW][C]128[/C][C]177918[/C][C]221604.263157895[/C][C]-43686.2631578947[/C][/ROW]
[ROW][C]129[/C][C]350552[/C][C]343614.1[/C][C]6937.90000000002[/C][/ROW]
[ROW][C]130[/C][C]275578[/C][C]274750.533333333[/C][C]827.466666666674[/C][/ROW]
[ROW][C]131[/C][C]366217[/C][C]343614.1[/C][C]22602.9[/C][/ROW]
[ROW][C]132[/C][C]172464[/C][C]167004.772727273[/C][C]5459.22727272726[/C][/ROW]
[ROW][C]133[/C][C]94381[/C][C]99721[/C][C]-5340[/C][/ROW]
[ROW][C]134[/C][C]243875[/C][C]199158.774193548[/C][C]44716.2258064516[/C][/ROW]
[ROW][C]135[/C][C]382487[/C][C]343614.1[/C][C]38872.9[/C][/ROW]
[ROW][C]136[/C][C]114525[/C][C]129343.153846154[/C][C]-14818.1538461538[/C][/ROW]
[ROW][C]137[/C][C]335681[/C][C]343614.1[/C][C]-7933.09999999998[/C][/ROW]
[ROW][C]138[/C][C]147989[/C][C]199158.774193548[/C][C]-51169.7741935484[/C][/ROW]
[ROW][C]139[/C][C]216638[/C][C]221604.263157895[/C][C]-4966.26315789475[/C][/ROW]
[ROW][C]140[/C][C]192862[/C][C]199158.774193548[/C][C]-6296.77419354839[/C][/ROW]
[ROW][C]141[/C][C]184818[/C][C]167004.772727273[/C][C]17813.2272727273[/C][/ROW]
[ROW][C]142[/C][C]336707[/C][C]343614.1[/C][C]-6907.09999999998[/C][/ROW]
[ROW][C]143[/C][C]215836[/C][C]221604.263157895[/C][C]-5768.26315789475[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]199158.774193548[/C][C]-25898.7741935484[/C][/ROW]
[ROW][C]145[/C][C]271773[/C][C]274750.533333333[/C][C]-2977.53333333333[/C][/ROW]
[ROW][C]146[/C][C]130908[/C][C]129343.153846154[/C][C]1564.84615384616[/C][/ROW]
[ROW][C]147[/C][C]204009[/C][C]246655.25[/C][C]-42646.25[/C][/ROW]
[ROW][C]148[/C][C]245514[/C][C]221604.263157895[/C][C]23909.7368421053[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]991.666666666667[/C][C]-990.666666666667[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]32650.5454545455[/C][C]-17962.5454545455[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]991.666666666667[/C][C]-893.666666666667[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]991.666666666667[/C][C]-536.666666666667[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]991.666666666667[/C][C]-991.666666666667[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]991.666666666667[/C][C]-991.666666666667[/C][/ROW]
[ROW][C]155[/C][C]195765[/C][C]199158.774193548[/C][C]-3393.77419354839[/C][/ROW]
[ROW][C]156[/C][C]326038[/C][C]274750.533333333[/C][C]51287.4666666667[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]991.666666666667[/C][C]-991.666666666667[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]991.666666666667[/C][C]-788.666666666667[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]991.666666666667[/C][C]6207.33333333333[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]32650.5454545455[/C][C]14009.4545454545[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]32650.5454545455[/C][C]-15103.5454545455[/C][/ROW]
[ROW][C]162[/C][C]107465[/C][C]99721[/C][C]7744[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]991.666666666667[/C][C]-22.6666666666666[/C][/ROW]
[ROW][C]164[/C][C]173102[/C][C]129343.153846154[/C][C]43758.8461538462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153369&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153369&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
1252101221604.26315789530496.7368421053
21345779972134856
3198520199158.774193548-638.774193548394
4189326199158.774193548-9832.77419354839
51374499972137728
66529532650.545454545532644.4545454545
7439387343614.195772.9
83318632650.5454545455535.454545454544
9178368199158.774193548-20790.7741935484
10186657167004.77272727319652.2272727273
11261949274750.533333333-12801.5333333333
12191051167004.77272727324046.2272727273
13138866129343.1538461549522.84615384616
14296878199158.77419354897719.2258064516
15192648167004.77272727325643.2272727273
16333462343614.1-10152.1
17243571221604.26315789521966.7368421053
18263451274750.533333333-11299.5333333333
19155679167004.772727273-11325.7727272727
20227053221604.2631578955448.73684210525
21240028246655.25-6627.25
22388549343614.144934.9
23156540167004.772727273-10464.7727272727
24148421167004.772727273-18583.7727272727
25177732199158.774193548-21426.7741935484
26191441199158.774193548-7717.77419354839
27249893274750.533333333-24857.5333333333
28236812221604.26315789515207.7368421053
29142329129343.15384615412985.8461538462
30259667246655.2513011.75
31231625246655.25-15030.25
32176062167004.7727272739057.22727272726
33286683274750.53333333311932.4666666667
348748599721-12236
35322865343614.1-20749.1
36247082199158.77419354847923.2258064516
37344092343614.1477.900000000023
38191653167004.77272727324648.2272727273
391146739972114952
40284224274750.5333333339473.46666666667
41284195343614.1-59419.1
42155363167004.772727273-11641.7727272727
43177306221604.263157895-44298.2631578947
44144571129343.15384615415227.8461538462
45140319199158.774193548-58839.7741935484
46405267343614.161652.9
477880099721-20921
48201970199158.7741935482811.22580645161
49302674343614.1-40940.1
50164733199158.774193548-34425.7741935484
51194221221604.263157895-27383.2631578947
522418832650.5454545455-8462.54545454546
53342263274750.53333333367512.4666666667
546502999721-34692
55101097129343.153846154-28246.1538461538
56246088246655.25-567.25
57273108221604.26315789551503.7368421053
58282220274750.5333333337469.46666666667
59273495274750.533333333-1255.53333333333
60214872199158.77419354815713.2258064516
61335121343614.1-8493.09999999998
62267171274750.533333333-7579.53333333333
63187938199158.774193548-11220.7741935484
64229512199158.77419354830353.2258064516
65209798221604.263157895-11806.2631578947
66201345221604.263157895-20259.2631578947
67163833167004.772727273-3171.77272727274
68204250274750.533333333-70500.5333333333
69197813199158.774193548-1345.77419354839
70132955167004.772727273-34049.7727272727
71216092199158.77419354816933.2258064516
727356699721-26155
73213198199158.77419354814039.2258064516
74181713199158.774193548-17445.7741935484
75148698167004.772727273-18306.7727272727
76300103199158.774193548100944.225806452
77251437246655.254781.75
78197295221604.263157895-24309.2631578947
79158163167004.772727273-8841.77272727274
80155529167004.772727273-11475.7727272727
81132672129343.1538461543328.84615384616
82377205343614.133590.9
83145905199158.774193548-53253.7741935484
84223701221604.2631578952096.73684210525
8580953129343.153846154-48390.1538461538
86130805199158.774193548-68353.7741935484
871350829972135361
88300170343614.1-43444.1
89271806274750.533333333-2944.53333333333
90150949199158.774193548-48209.7741935484
91225805199158.77419354826646.2258064516
92197389167004.77272727330384.2272727273
93156583167004.772727273-10421.7727272727
94222599199158.77419354823440.2258064516
95261601246655.2514945.75
96178489167004.77272727311484.2272727273
97200657221604.263157895-20947.2631578947
98259084246655.2512428.75
99313075343614.1-30539.1
100346933343614.13318.90000000002
101246440199158.77419354847281.2258064516
102252444246655.255788.75
103159965167004.772727273-7039.77272727274
1044328732650.545454545510636.4545454545
105172239199158.774193548-26919.7741935484
106181897221604.263157895-39707.2631578947
107227681246655.25-18974.25
108260464274750.533333333-14286.5333333333
109106288997216567
110109632129343.153846154-19711.1538461538
111268905221604.26315789547300.7368421053
112266805221604.26315789545200.7368421053
1132362332650.5454545455-9027.54545454546
114152474167004.772727273-14530.7727272727
1156185799721-37864
116144889129343.15384615415545.8461538462
117346600343614.12985.90000000002
1182105432650.5454545455-11596.5454545455
119224051246655.25-22604.25
1203141432650.5454545455-1236.54545454546
121261043343614.1-82571.1
122197819199158.774193548-1339.77419354839
123154984129343.15384615425640.8461538462
124112933129343.153846154-16410.1538461538
1253821432650.54545454555563.45454545454
126158671167004.772727273-8333.77272727274
127302148246655.2555492.75
128177918221604.263157895-43686.2631578947
129350552343614.16937.90000000002
130275578274750.533333333827.466666666674
131366217343614.122602.9
132172464167004.7727272735459.22727272726
1339438199721-5340
134243875199158.77419354844716.2258064516
135382487343614.138872.9
136114525129343.153846154-14818.1538461538
137335681343614.1-7933.09999999998
138147989199158.774193548-51169.7741935484
139216638221604.263157895-4966.26315789475
140192862199158.774193548-6296.77419354839
141184818167004.77272727317813.2272727273
142336707343614.1-6907.09999999998
143215836221604.263157895-5768.26315789475
144173260199158.774193548-25898.7741935484
145271773274750.533333333-2977.53333333333
146130908129343.1538461541564.84615384616
147204009246655.25-42646.25
148245514221604.26315789523909.7368421053
1491991.666666666667-990.666666666667
1501468832650.5454545455-17962.5454545455
15198991.666666666667-893.666666666667
152455991.666666666667-536.666666666667
1530991.666666666667-991.666666666667
1540991.666666666667-991.666666666667
155195765199158.774193548-3393.77419354839
156326038274750.53333333351287.4666666667
1570991.666666666667-991.666666666667
158203991.666666666667-788.666666666667
1597199991.6666666666676207.33333333333
1604666032650.545454545514009.4545454545
1611754732650.5454545455-15103.5454545455
162107465997217744
163969991.666666666667-22.6666666666666
164173102129343.15384615443758.8461538462



Parameters (Session):
par1 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;
R code (references can be found in the software module):
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}