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Author

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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Wed, 21 Dec 2011 04:38:44 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/21/t1324460345nf74yehotn56o48.htm/, Retrieved Wed, 08 May 2024 00:34:39 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158387, Retrieved Wed, 08 May 2024 00:34:39 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

126

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

-     [Univariate Data Series] [web traffic] [2010-10-19 15:13:07] [b98453cac15ba1066b407e146608df68]
- RMP   [Variance Reduction Matrix] [Traffic] [2010-11-29 09:57:15] [b98453cac15ba1066b407e146608df68]
- RM      [Standard Deviation-Mean Plot] [Traffic] [2010-11-29 11:05:08] [b98453cac15ba1066b407e146608df68]
- RMP       [ARIMA Forecasting] [Traffic] [2010-11-29 21:10:32] [b98453cac15ba1066b407e146608df68]
- R PD        [ARIMA Forecasting] [] [2011-12-06 10:39:07] [aba4febe8a2e49e81bdc61a6c01f5c21]
-   PD          [ARIMA Forecasting] [] [2011-12-20 15:47:34] [aba4febe8a2e49e81bdc61a6c01f5c21]
- R               [ARIMA Forecasting] [ARIMA Forecasting CV] [2011-12-20 15:48:10] [aba4febe8a2e49e81bdc61a6c01f5c21]
- RMPD                [Recursive Partitioning (Regression Trees)] [Paper Recursive p...] [2011-12-21 09:38:44] [3627de22d386f4cb93d383ef7c1ade7f] [Current]

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Dataseries X:

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Histogram

Boxplots

1785	276257	71	492	3	96	41
1227	180480	66	436	4	71	34
1931	229040	75	694	16	70	44
2683	218443	104	1137	2	134	38
1247	171533	52	380	1	67	27
631	70849	28	179	3	8	35
4845	536497	125	2354	0	162	33
381	33186	19	111	0	1	18
2066	217320	60	740	7	83	34
1759	213274	45	595	0	82	33
2157	310843	112	809	0	92	46
2211	242788	122	693	7	117	57
1967	195022	76	738	10	56	37
3005	367785	81	1184	4	139	55
2228	261990	86	713	10	83	44
5009	392509	181	1729	0	176	62
2353	335528	75	844	8	114	40
3278	376673	163	1298	4	105	39
1473	181980	56	514	3	103	32
2347	266736	87	689	8	135	51
2522	278265	70	837	0	123	49
2987	461287	104	1330	1	87	39
1523	195786	42	491	5	74	25
1761	197058	55	622	9	103	56
3660	250284	126	1332	1	154	45
2937	245373	129	1043	0	113	38
2914	274121	129	1082	5	99	45
2009	278027	88	636	0	117	43
1555	165597	52	586	0	59	32
3049	371452	72	1170	0	142	41
2430	295296	80	973	3	132	50
2092	248320	81	721	6	50	50
2404	351980	95	863	1	141	51
1018	101014	40	343	4	43	37
3462	412722	101	1278	4	141	44
2764	273950	56	1186	0	83	42
3710	425531	122	1334	0	112	44
1947	231912	84	652	2	79	36
947	115658	33	284	1	33	17
3608	376008	201	1273	2	137	43
3324	335039	82	1518	10	125	41
1842	194127	71	715	10	80	41
1869	206947	78	671	5	78	38
1813	182286	64	486	6	68	49
2496	153778	82	1022	1	50	45
5557	457592	153	2084	2	101	42
918	78800	42	330	2	20	26
2254	208277	77	658	0	101	52
4103	359144	121	1385	10	149	50
2241	184648	60	930	3	108	45
1942	234078	76	620	0	96	40
496	24188	24	218	0	8	4
2594	380576	326	840	8	88	44
744	65029	17	255	5	21	18
1161	101097	64	454	3	30	14
3121	288327	60	1149	1	97	38
2621	334829	87	684	5	134	61
3905	359400	198	1190	6	132	39
2722	369577	146	1079	0	161	42
2312	269961	86	883	12	89	40
4061	389738	147	1331	10	160	51
3195	309474	119	1159	12	139	28
3041	282769	118	1217	11	104	43
2643	269324	89	946	8	92	42
1496	234773	69	579	2	54	37
1835	237561	68	474	0	119	30
2080	211396	135	626	6	93	39
2772	240376	151	843	10	85	44
2440	247447	84	893	2	145	36
1718	181550	70	633	5	143	28
2566	242152	70	873	13	99	47
893	73566	32	385	6	22	23
2227	246125	86	729	7	78	48
1878	199565	56	774	2	83	38
2177	222676	65	769	4	131	42
2352	363558	88	996	4	140	46
2981	365442	96	1194	3	148	37
1926	217036	108	575	6	80	41
2400	213466	66	725	2	133	42
2034	204477	67	706	0	99	41
1800	169080	48	665	1	62	36
4168	478396	128	1259	0	161	45
1369	145943	69	653	5	30	26
2403	288626	106	694	2	120	44
870	80953	25	437	0	49	8
1968	150221	55	822	0	52	27
1569	179317	60	458	6	76	38
3962	395368	213	1545	1	85	38
2928	349460	120	987	0	146	57
3098	180679	106	1051	1	165	45
2557	286578	100	838	1	87	40
2329	274477	81	703	3	165	42
1858	195623	65	613	10	48	31
3146	282361	77	1128	1	149	36
2581	329121	85	967	4	75	40
1824	198658	42	617	4	84	40
1914	262630	59	654	5	110	35
2210	300481	83	805	0	165	39
4118	403404	155	1355	12	155	65
3844	406613	114	1456	13	165	33
2791	294371	138	878	8	121	51
2425	313267	67	887	0	156	42
2052	189276	188	663	0	73	36
602	43287	14	214	4	13	19
2195	189520	83	733	4	89	25
2412	250254	148	830	0	105	44
2628	268886	51	1174	0	129	40
2758	314153	92	1068	0	169	44
1268	160308	81	413	0	28	30
3233	162843	97	946	0	118	45
2025	344925	74	657	5	82	42
2310	300526	89	690	0	147	45
398	23623	11	156	0	12	1
2205	195817	73	779	0	146	40
530	61857	25	192	4	23	11
1610	163931	50	461	0	83	45
3153	428191	120	1213	1	163	38
387	21054	16	146	0	4	0
2137	252805	52	866	5	81	30
492	31961	22	200	0	18	8
3674	335888	119	1290	3	115	41
2136	246100	68	715	7	76	48
1748	180591	93	514	13	55	48
1790	163400	54	697	3	53	32
568	38214	34	276	0	16	8
2191	224597	44	752	3	81	43
2792	357602	82	1021	0	137	52
1396	198104	62	481	0	50	53
3718	424398	85	1626	4	142	49
2554	348017	99	884	0	163	48
3460	421610	129	1187	3	141	56
1283	192170	37	488	0	71	40
1130	102510	43	403	0	42	36
2974	302158	347	977	4	94	44
4346	444599	182	1525	5	118	46
1785	148707	57	551	15	63	43
4349	407736	134	1807	5	127	46
1920	164406	79	723	5	55	39
1923	278077	50	632	2	117	41
2541	282461	96	898	1	110	46
1844	219544	115	621	0	39	32
4081	384177	122	1606	9	95	45
2339	246963	92	811	1	128	39
2035	173260	63	716	3	41	21
3108	336715	104	1001	11	146	49
1974	176654	58	732	5	147	55
2651	253341	87	1024	2	119	36
2702	307133	109	831	1	185	48
2	1	0	0	9	0	0
207	14688	10	85	0	4	0
5	98	1	0	0	0	0
8	455	2	0	0	0	0
0	0	0	0	1	0	0
0	0	0	0	0	0	0
2255	260901	88	773	2	75	43
3447	409280	162	1128	3	157	52
0	0	0	0	0	0	0
4	203	4	0	0	0	0
151	7199	5	74	0	7	0
474	46660	20	259	0	12	5
141	17547	5	69	0	0	1
1111	118589	45	301	0	37	45
29	969	2	0	0	0	0
2011	233108	70	668	2	61	34

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158387&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158387&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158387&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

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

Goodness of Fit
Correlation	0.9761
R-squared	0.9528
RMSE	240.674

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9761 \tabularnewline
R-squared & 0.9528 \tabularnewline
RMSE & 240.674 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158387&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9761[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9528[/C][/ROW]
[ROW][C]RMSE[/C][C]240.674[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158387&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158387&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goodness of Fit
Correlation	0.9761
R-squared	0.9528
RMSE	240.674

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1785	1868	-83
2	1227	1071.81818181818	155.181818181818
3	1931	2029.08695652174	-98.0869565217392
4	2683	3053	-370
5	1247	1071.81818181818	175.181818181818
6	631	518.454545454545	112.545454545455
7	4845	4483.375	361.625
8	381	518.454545454545	-137.454545454545
9	2066	2029.08695652174	36.9130434782608
10	1759	1868	-109
11	2157	2029.08695652174	127.913043478261
12	2211	2290.76923076923	-79.7692307692309
13	1967	2029.08695652174	-62.0869565217392
14	3005	3053	-48
15	2228	2029.08695652174	198.913043478261
16	5009	4483.375	525.625
17	2353	2589.67857142857	-236.678571428572
18	3278	3692.07692307692	-414.076923076923
19	1473	1604.35714285714	-131.357142857143
20	2347	2290.76923076923	56.2307692307691
21	2522	2589.67857142857	-67.6785714285716
22	2987	3692.07692307692	-705.076923076923
23	1523	1604.35714285714	-81.3571428571429
24	1761	1604.35714285714	156.642857142857
25	3660	3692.07692307692	-32.0769230769229
26	2937	3053	-116
27	2914	3053	-139
28	2009	1868	141
29	1555	1604.35714285714	-49.3571428571429
30	3049	3053	-4
31	2430	2589.67857142857	-159.678571428572
32	2092	2029.08695652174	62.9130434782608
33	2404	2589.67857142857	-185.678571428572
34	1018	1071.81818181818	-53.8181818181818
35	3462	3692.07692307692	-230.076923076923
36	2764	3053	-289
37	3710	3692.07692307692	17.9230769230771
38	1947	1868	79
39	947	1071.81818181818	-124.818181818182
40	3608	3692.07692307692	-84.0769230769229
41	3324	3692.07692307692	-368.076923076923
42	1842	2029.08695652174	-187.086956521739
43	1869	2029.08695652174	-160.086956521739
44	1813	1604.35714285714	208.642857142857
45	2496	2589.67857142857	-93.6785714285716
46	5557	4483.375	1073.625
47	918	1071.81818181818	-153.818181818182
48	2254	2290.76923076923	-36.7692307692309
49	4103	3692.07692307692	410.923076923077
50	2241	2589.67857142857	-348.678571428572
51	1942	1868	74
52	496	518.454545454545	-22.4545454545455
53	2594	2589.67857142857	4.32142857142844
54	744	518.454545454545	225.545454545455
55	1161	1071.81818181818	89.1818181818182
56	3121	3053	68
57	2621	2290.76923076923	330.230769230769
58	3905	3053	852
59	2722	3053	-331
60	2312	2589.67857142857	-277.678571428572
61	4061	3692.07692307692	368.923076923077
62	3195	3053	142
63	3041	3053	-12
64	2643	2589.67857142857	53.3214285714284
65	1496	1868	-372
66	1835	1868	-33
67	2080	1868	212
68	2772	2589.67857142857	182.321428571428
69	2440	2589.67857142857	-149.678571428572
70	1718	1604.35714285714	113.642857142857
71	2566	2589.67857142857	-23.6785714285716
72	893	1071.81818181818	-178.818181818182
73	2227	2029.08695652174	197.913043478261
74	1878	2029.08695652174	-151.086956521739
75	2177	2290.76923076923	-113.769230769231
76	2352	2589.67857142857	-237.678571428572
77	2981	3053	-72
78	1926	1868	58
79	2400	2290.76923076923	109.230769230769
80	2034	2029.08695652174	4.91304347826076
81	1800	2029.08695652174	-229.086956521739
82	4168	3692.07692307692	475.923076923077
83	1369	1604.35714285714	-235.357142857143
84	2403	2290.76923076923	112.230769230769
85	870	1071.81818181818	-201.818181818182
86	1968	2029.08695652174	-61.0869565217392
87	1569	1604.35714285714	-35.3571428571429
88	3962	4483.375	-521.375
89	2928	2589.67857142857	338.321428571428
90	3098	3053	45
91	2557	2589.67857142857	-32.6785714285716
92	2329	2290.76923076923	38.2307692307691
93	1858	1604.35714285714	253.642857142857
94	3146	3053	93
95	2581	2589.67857142857	-8.67857142857156
96	1824	1868	-44
97	1914	1868	46
98	2210	2290.76923076923	-80.7692307692309
99	4118	3692.07692307692	425.923076923077
100	3844	3692.07692307692	151.923076923077
101	2791	2589.67857142857	201.321428571428
102	2425	2589.67857142857	-164.678571428572
103	2052	2029.08695652174	22.9130434782608
104	602	518.454545454545	83.5454545454545
105	2195	2029.08695652174	165.913043478261
106	2412	2589.67857142857	-177.678571428572
107	2628	3053	-425
108	2758	3053	-295
109	1268	1071.81818181818	196.181818181818
110	3233	2589.67857142857	643.321428571428
111	2025	2029.08695652174	-4.08695652173924
112	2310	2290.76923076923	19.2307692307691
113	398	518.454545454545	-120.454545454545
114	2205	2290.76923076923	-85.7692307692309
115	530	518.454545454545	11.5454545454545
116	1610	1604.35714285714	5.64285714285711
117	3153	3053	100
118	387	518.454545454545	-131.454545454545
119	2137	2589.67857142857	-452.678571428572
120	492	518.454545454545	-26.4545454545455
121	3674	3692.07692307692	-18.0769230769229
122	2136	2029.08695652174	106.913043478261
123	1748	1604.35714285714	143.642857142857
124	1790	2029.08695652174	-239.086956521739
125	568	518.454545454545	49.5454545454545
126	2191	2029.08695652174	161.913043478261
127	2792	2589.67857142857	202.321428571428
128	1396	1604.35714285714	-208.357142857143
129	3718	4483.375	-765.375
130	2554	2589.67857142857	-35.6785714285716
131	3460	3053	407
132	1283	1604.35714285714	-321.357142857143
133	1130	1071.81818181818	58.1818181818182
134	2974	2589.67857142857	384.321428571428
135	4346	4483.375	-137.375
136	1785	1604.35714285714	180.642857142857
137	4349	4483.375	-134.375
138	1920	2029.08695652174	-109.086956521739
139	1923	1868	55
140	2541	2589.67857142857	-48.6785714285716
141	1844	1868	-24
142	4081	4483.375	-402.375
143	2339	2290.76923076923	48.2307692307691
144	2035	2029.08695652174	5.91304347826076
145	3108	2589.67857142857	518.321428571428
146	1974	2290.76923076923	-316.769230769231
147	2651	2589.67857142857	61.3214285714284
148	2702	2589.67857142857	112.321428571428
149	2	49.7272727272727	-47.7272727272727
150	207	49.7272727272727	157.272727272727
151	5	49.7272727272727	-44.7272727272727
152	8	49.7272727272727	-41.7272727272727
153	0	49.7272727272727	-49.7272727272727
154	0	49.7272727272727	-49.7272727272727
155	2255	2029.08695652174	225.913043478261
156	3447	3053	394
157	0	49.7272727272727	-49.7272727272727
158	4	49.7272727272727	-45.7272727272727
159	151	49.7272727272727	101.272727272727
160	474	518.454545454545	-44.4545454545455
161	141	49.7272727272727	91.2727272727273
162	1111	1071.81818181818	39.1818181818182
163	29	49.7272727272727	-20.7272727272727
164	2011	2029.08695652174	-18.0869565217392

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1785 & 1868 & -83 \tabularnewline
2 & 1227 & 1071.81818181818 & 155.181818181818 \tabularnewline
3 & 1931 & 2029.08695652174 & -98.0869565217392 \tabularnewline
4 & 2683 & 3053 & -370 \tabularnewline
5 & 1247 & 1071.81818181818 & 175.181818181818 \tabularnewline
6 & 631 & 518.454545454545 & 112.545454545455 \tabularnewline
7 & 4845 & 4483.375 & 361.625 \tabularnewline
8 & 381 & 518.454545454545 & -137.454545454545 \tabularnewline
9 & 2066 & 2029.08695652174 & 36.9130434782608 \tabularnewline
10 & 1759 & 1868 & -109 \tabularnewline
11 & 2157 & 2029.08695652174 & 127.913043478261 \tabularnewline
12 & 2211 & 2290.76923076923 & -79.7692307692309 \tabularnewline
13 & 1967 & 2029.08695652174 & -62.0869565217392 \tabularnewline
14 & 3005 & 3053 & -48 \tabularnewline
15 & 2228 & 2029.08695652174 & 198.913043478261 \tabularnewline
16 & 5009 & 4483.375 & 525.625 \tabularnewline
17 & 2353 & 2589.67857142857 & -236.678571428572 \tabularnewline
18 & 3278 & 3692.07692307692 & -414.076923076923 \tabularnewline
19 & 1473 & 1604.35714285714 & -131.357142857143 \tabularnewline
20 & 2347 & 2290.76923076923 & 56.2307692307691 \tabularnewline
21 & 2522 & 2589.67857142857 & -67.6785714285716 \tabularnewline
22 & 2987 & 3692.07692307692 & -705.076923076923 \tabularnewline
23 & 1523 & 1604.35714285714 & -81.3571428571429 \tabularnewline
24 & 1761 & 1604.35714285714 & 156.642857142857 \tabularnewline
25 & 3660 & 3692.07692307692 & -32.0769230769229 \tabularnewline
26 & 2937 & 3053 & -116 \tabularnewline
27 & 2914 & 3053 & -139 \tabularnewline
28 & 2009 & 1868 & 141 \tabularnewline
29 & 1555 & 1604.35714285714 & -49.3571428571429 \tabularnewline
30 & 3049 & 3053 & -4 \tabularnewline
31 & 2430 & 2589.67857142857 & -159.678571428572 \tabularnewline
32 & 2092 & 2029.08695652174 & 62.9130434782608 \tabularnewline
33 & 2404 & 2589.67857142857 & -185.678571428572 \tabularnewline
34 & 1018 & 1071.81818181818 & -53.8181818181818 \tabularnewline
35 & 3462 & 3692.07692307692 & -230.076923076923 \tabularnewline
36 & 2764 & 3053 & -289 \tabularnewline
37 & 3710 & 3692.07692307692 & 17.9230769230771 \tabularnewline
38 & 1947 & 1868 & 79 \tabularnewline
39 & 947 & 1071.81818181818 & -124.818181818182 \tabularnewline
40 & 3608 & 3692.07692307692 & -84.0769230769229 \tabularnewline
41 & 3324 & 3692.07692307692 & -368.076923076923 \tabularnewline
42 & 1842 & 2029.08695652174 & -187.086956521739 \tabularnewline
43 & 1869 & 2029.08695652174 & -160.086956521739 \tabularnewline
44 & 1813 & 1604.35714285714 & 208.642857142857 \tabularnewline
45 & 2496 & 2589.67857142857 & -93.6785714285716 \tabularnewline
46 & 5557 & 4483.375 & 1073.625 \tabularnewline
47 & 918 & 1071.81818181818 & -153.818181818182 \tabularnewline
48 & 2254 & 2290.76923076923 & -36.7692307692309 \tabularnewline
49 & 4103 & 3692.07692307692 & 410.923076923077 \tabularnewline
50 & 2241 & 2589.67857142857 & -348.678571428572 \tabularnewline
51 & 1942 & 1868 & 74 \tabularnewline
52 & 496 & 518.454545454545 & -22.4545454545455 \tabularnewline
53 & 2594 & 2589.67857142857 & 4.32142857142844 \tabularnewline
54 & 744 & 518.454545454545 & 225.545454545455 \tabularnewline
55 & 1161 & 1071.81818181818 & 89.1818181818182 \tabularnewline
56 & 3121 & 3053 & 68 \tabularnewline
57 & 2621 & 2290.76923076923 & 330.230769230769 \tabularnewline
58 & 3905 & 3053 & 852 \tabularnewline
59 & 2722 & 3053 & -331 \tabularnewline
60 & 2312 & 2589.67857142857 & -277.678571428572 \tabularnewline
61 & 4061 & 3692.07692307692 & 368.923076923077 \tabularnewline
62 & 3195 & 3053 & 142 \tabularnewline
63 & 3041 & 3053 & -12 \tabularnewline
64 & 2643 & 2589.67857142857 & 53.3214285714284 \tabularnewline
65 & 1496 & 1868 & -372 \tabularnewline
66 & 1835 & 1868 & -33 \tabularnewline
67 & 2080 & 1868 & 212 \tabularnewline
68 & 2772 & 2589.67857142857 & 182.321428571428 \tabularnewline
69 & 2440 & 2589.67857142857 & -149.678571428572 \tabularnewline
70 & 1718 & 1604.35714285714 & 113.642857142857 \tabularnewline
71 & 2566 & 2589.67857142857 & -23.6785714285716 \tabularnewline
72 & 893 & 1071.81818181818 & -178.818181818182 \tabularnewline
73 & 2227 & 2029.08695652174 & 197.913043478261 \tabularnewline
74 & 1878 & 2029.08695652174 & -151.086956521739 \tabularnewline
75 & 2177 & 2290.76923076923 & -113.769230769231 \tabularnewline
76 & 2352 & 2589.67857142857 & -237.678571428572 \tabularnewline
77 & 2981 & 3053 & -72 \tabularnewline
78 & 1926 & 1868 & 58 \tabularnewline
79 & 2400 & 2290.76923076923 & 109.230769230769 \tabularnewline
80 & 2034 & 2029.08695652174 & 4.91304347826076 \tabularnewline
81 & 1800 & 2029.08695652174 & -229.086956521739 \tabularnewline
82 & 4168 & 3692.07692307692 & 475.923076923077 \tabularnewline
83 & 1369 & 1604.35714285714 & -235.357142857143 \tabularnewline
84 & 2403 & 2290.76923076923 & 112.230769230769 \tabularnewline
85 & 870 & 1071.81818181818 & -201.818181818182 \tabularnewline
86 & 1968 & 2029.08695652174 & -61.0869565217392 \tabularnewline
87 & 1569 & 1604.35714285714 & -35.3571428571429 \tabularnewline
88 & 3962 & 4483.375 & -521.375 \tabularnewline
89 & 2928 & 2589.67857142857 & 338.321428571428 \tabularnewline
90 & 3098 & 3053 & 45 \tabularnewline
91 & 2557 & 2589.67857142857 & -32.6785714285716 \tabularnewline
92 & 2329 & 2290.76923076923 & 38.2307692307691 \tabularnewline
93 & 1858 & 1604.35714285714 & 253.642857142857 \tabularnewline
94 & 3146 & 3053 & 93 \tabularnewline
95 & 2581 & 2589.67857142857 & -8.67857142857156 \tabularnewline
96 & 1824 & 1868 & -44 \tabularnewline
97 & 1914 & 1868 & 46 \tabularnewline
98 & 2210 & 2290.76923076923 & -80.7692307692309 \tabularnewline
99 & 4118 & 3692.07692307692 & 425.923076923077 \tabularnewline
100 & 3844 & 3692.07692307692 & 151.923076923077 \tabularnewline
101 & 2791 & 2589.67857142857 & 201.321428571428 \tabularnewline
102 & 2425 & 2589.67857142857 & -164.678571428572 \tabularnewline
103 & 2052 & 2029.08695652174 & 22.9130434782608 \tabularnewline
104 & 602 & 518.454545454545 & 83.5454545454545 \tabularnewline
105 & 2195 & 2029.08695652174 & 165.913043478261 \tabularnewline
106 & 2412 & 2589.67857142857 & -177.678571428572 \tabularnewline
107 & 2628 & 3053 & -425 \tabularnewline
108 & 2758 & 3053 & -295 \tabularnewline
109 & 1268 & 1071.81818181818 & 196.181818181818 \tabularnewline
110 & 3233 & 2589.67857142857 & 643.321428571428 \tabularnewline
111 & 2025 & 2029.08695652174 & -4.08695652173924 \tabularnewline
112 & 2310 & 2290.76923076923 & 19.2307692307691 \tabularnewline
113 & 398 & 518.454545454545 & -120.454545454545 \tabularnewline
114 & 2205 & 2290.76923076923 & -85.7692307692309 \tabularnewline
115 & 530 & 518.454545454545 & 11.5454545454545 \tabularnewline
116 & 1610 & 1604.35714285714 & 5.64285714285711 \tabularnewline
117 & 3153 & 3053 & 100 \tabularnewline
118 & 387 & 518.454545454545 & -131.454545454545 \tabularnewline
119 & 2137 & 2589.67857142857 & -452.678571428572 \tabularnewline
120 & 492 & 518.454545454545 & -26.4545454545455 \tabularnewline
121 & 3674 & 3692.07692307692 & -18.0769230769229 \tabularnewline
122 & 2136 & 2029.08695652174 & 106.913043478261 \tabularnewline
123 & 1748 & 1604.35714285714 & 143.642857142857 \tabularnewline
124 & 1790 & 2029.08695652174 & -239.086956521739 \tabularnewline
125 & 568 & 518.454545454545 & 49.5454545454545 \tabularnewline
126 & 2191 & 2029.08695652174 & 161.913043478261 \tabularnewline
127 & 2792 & 2589.67857142857 & 202.321428571428 \tabularnewline
128 & 1396 & 1604.35714285714 & -208.357142857143 \tabularnewline
129 & 3718 & 4483.375 & -765.375 \tabularnewline
130 & 2554 & 2589.67857142857 & -35.6785714285716 \tabularnewline
131 & 3460 & 3053 & 407 \tabularnewline
132 & 1283 & 1604.35714285714 & -321.357142857143 \tabularnewline
133 & 1130 & 1071.81818181818 & 58.1818181818182 \tabularnewline
134 & 2974 & 2589.67857142857 & 384.321428571428 \tabularnewline
135 & 4346 & 4483.375 & -137.375 \tabularnewline
136 & 1785 & 1604.35714285714 & 180.642857142857 \tabularnewline
137 & 4349 & 4483.375 & -134.375 \tabularnewline
138 & 1920 & 2029.08695652174 & -109.086956521739 \tabularnewline
139 & 1923 & 1868 & 55 \tabularnewline
140 & 2541 & 2589.67857142857 & -48.6785714285716 \tabularnewline
141 & 1844 & 1868 & -24 \tabularnewline
142 & 4081 & 4483.375 & -402.375 \tabularnewline
143 & 2339 & 2290.76923076923 & 48.2307692307691 \tabularnewline
144 & 2035 & 2029.08695652174 & 5.91304347826076 \tabularnewline
145 & 3108 & 2589.67857142857 & 518.321428571428 \tabularnewline
146 & 1974 & 2290.76923076923 & -316.769230769231 \tabularnewline
147 & 2651 & 2589.67857142857 & 61.3214285714284 \tabularnewline
148 & 2702 & 2589.67857142857 & 112.321428571428 \tabularnewline
149 & 2 & 49.7272727272727 & -47.7272727272727 \tabularnewline
150 & 207 & 49.7272727272727 & 157.272727272727 \tabularnewline
151 & 5 & 49.7272727272727 & -44.7272727272727 \tabularnewline
152 & 8 & 49.7272727272727 & -41.7272727272727 \tabularnewline
153 & 0 & 49.7272727272727 & -49.7272727272727 \tabularnewline
154 & 0 & 49.7272727272727 & -49.7272727272727 \tabularnewline
155 & 2255 & 2029.08695652174 & 225.913043478261 \tabularnewline
156 & 3447 & 3053 & 394 \tabularnewline
157 & 0 & 49.7272727272727 & -49.7272727272727 \tabularnewline
158 & 4 & 49.7272727272727 & -45.7272727272727 \tabularnewline
159 & 151 & 49.7272727272727 & 101.272727272727 \tabularnewline
160 & 474 & 518.454545454545 & -44.4545454545455 \tabularnewline
161 & 141 & 49.7272727272727 & 91.2727272727273 \tabularnewline
162 & 1111 & 1071.81818181818 & 39.1818181818182 \tabularnewline
163 & 29 & 49.7272727272727 & -20.7272727272727 \tabularnewline
164 & 2011 & 2029.08695652174 & -18.0869565217392 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158387&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]1785[/C][C]1868[/C][C]-83[/C][/ROW]
[ROW][C]2[/C][C]1227[/C][C]1071.81818181818[/C][C]155.181818181818[/C][/ROW]
[ROW][C]3[/C][C]1931[/C][C]2029.08695652174[/C][C]-98.0869565217392[/C][/ROW]
[ROW][C]4[/C][C]2683[/C][C]3053[/C][C]-370[/C][/ROW]
[ROW][C]5[/C][C]1247[/C][C]1071.81818181818[/C][C]175.181818181818[/C][/ROW]
[ROW][C]6[/C][C]631[/C][C]518.454545454545[/C][C]112.545454545455[/C][/ROW]
[ROW][C]7[/C][C]4845[/C][C]4483.375[/C][C]361.625[/C][/ROW]
[ROW][C]8[/C][C]381[/C][C]518.454545454545[/C][C]-137.454545454545[/C][/ROW]
[ROW][C]9[/C][C]2066[/C][C]2029.08695652174[/C][C]36.9130434782608[/C][/ROW]
[ROW][C]10[/C][C]1759[/C][C]1868[/C][C]-109[/C][/ROW]
[ROW][C]11[/C][C]2157[/C][C]2029.08695652174[/C][C]127.913043478261[/C][/ROW]
[ROW][C]12[/C][C]2211[/C][C]2290.76923076923[/C][C]-79.7692307692309[/C][/ROW]
[ROW][C]13[/C][C]1967[/C][C]2029.08695652174[/C][C]-62.0869565217392[/C][/ROW]
[ROW][C]14[/C][C]3005[/C][C]3053[/C][C]-48[/C][/ROW]
[ROW][C]15[/C][C]2228[/C][C]2029.08695652174[/C][C]198.913043478261[/C][/ROW]
[ROW][C]16[/C][C]5009[/C][C]4483.375[/C][C]525.625[/C][/ROW]
[ROW][C]17[/C][C]2353[/C][C]2589.67857142857[/C][C]-236.678571428572[/C][/ROW]
[ROW][C]18[/C][C]3278[/C][C]3692.07692307692[/C][C]-414.076923076923[/C][/ROW]
[ROW][C]19[/C][C]1473[/C][C]1604.35714285714[/C][C]-131.357142857143[/C][/ROW]
[ROW][C]20[/C][C]2347[/C][C]2290.76923076923[/C][C]56.2307692307691[/C][/ROW]
[ROW][C]21[/C][C]2522[/C][C]2589.67857142857[/C][C]-67.6785714285716[/C][/ROW]
[ROW][C]22[/C][C]2987[/C][C]3692.07692307692[/C][C]-705.076923076923[/C][/ROW]
[ROW][C]23[/C][C]1523[/C][C]1604.35714285714[/C][C]-81.3571428571429[/C][/ROW]
[ROW][C]24[/C][C]1761[/C][C]1604.35714285714[/C][C]156.642857142857[/C][/ROW]
[ROW][C]25[/C][C]3660[/C][C]3692.07692307692[/C][C]-32.0769230769229[/C][/ROW]
[ROW][C]26[/C][C]2937[/C][C]3053[/C][C]-116[/C][/ROW]
[ROW][C]27[/C][C]2914[/C][C]3053[/C][C]-139[/C][/ROW]
[ROW][C]28[/C][C]2009[/C][C]1868[/C][C]141[/C][/ROW]
[ROW][C]29[/C][C]1555[/C][C]1604.35714285714[/C][C]-49.3571428571429[/C][/ROW]
[ROW][C]30[/C][C]3049[/C][C]3053[/C][C]-4[/C][/ROW]
[ROW][C]31[/C][C]2430[/C][C]2589.67857142857[/C][C]-159.678571428572[/C][/ROW]
[ROW][C]32[/C][C]2092[/C][C]2029.08695652174[/C][C]62.9130434782608[/C][/ROW]
[ROW][C]33[/C][C]2404[/C][C]2589.67857142857[/C][C]-185.678571428572[/C][/ROW]
[ROW][C]34[/C][C]1018[/C][C]1071.81818181818[/C][C]-53.8181818181818[/C][/ROW]
[ROW][C]35[/C][C]3462[/C][C]3692.07692307692[/C][C]-230.076923076923[/C][/ROW]
[ROW][C]36[/C][C]2764[/C][C]3053[/C][C]-289[/C][/ROW]
[ROW][C]37[/C][C]3710[/C][C]3692.07692307692[/C][C]17.9230769230771[/C][/ROW]
[ROW][C]38[/C][C]1947[/C][C]1868[/C][C]79[/C][/ROW]
[ROW][C]39[/C][C]947[/C][C]1071.81818181818[/C][C]-124.818181818182[/C][/ROW]
[ROW][C]40[/C][C]3608[/C][C]3692.07692307692[/C][C]-84.0769230769229[/C][/ROW]
[ROW][C]41[/C][C]3324[/C][C]3692.07692307692[/C][C]-368.076923076923[/C][/ROW]
[ROW][C]42[/C][C]1842[/C][C]2029.08695652174[/C][C]-187.086956521739[/C][/ROW]
[ROW][C]43[/C][C]1869[/C][C]2029.08695652174[/C][C]-160.086956521739[/C][/ROW]
[ROW][C]44[/C][C]1813[/C][C]1604.35714285714[/C][C]208.642857142857[/C][/ROW]
[ROW][C]45[/C][C]2496[/C][C]2589.67857142857[/C][C]-93.6785714285716[/C][/ROW]
[ROW][C]46[/C][C]5557[/C][C]4483.375[/C][C]1073.625[/C][/ROW]
[ROW][C]47[/C][C]918[/C][C]1071.81818181818[/C][C]-153.818181818182[/C][/ROW]
[ROW][C]48[/C][C]2254[/C][C]2290.76923076923[/C][C]-36.7692307692309[/C][/ROW]
[ROW][C]49[/C][C]4103[/C][C]3692.07692307692[/C][C]410.923076923077[/C][/ROW]
[ROW][C]50[/C][C]2241[/C][C]2589.67857142857[/C][C]-348.678571428572[/C][/ROW]
[ROW][C]51[/C][C]1942[/C][C]1868[/C][C]74[/C][/ROW]
[ROW][C]52[/C][C]496[/C][C]518.454545454545[/C][C]-22.4545454545455[/C][/ROW]
[ROW][C]53[/C][C]2594[/C][C]2589.67857142857[/C][C]4.32142857142844[/C][/ROW]
[ROW][C]54[/C][C]744[/C][C]518.454545454545[/C][C]225.545454545455[/C][/ROW]
[ROW][C]55[/C][C]1161[/C][C]1071.81818181818[/C][C]89.1818181818182[/C][/ROW]
[ROW][C]56[/C][C]3121[/C][C]3053[/C][C]68[/C][/ROW]
[ROW][C]57[/C][C]2621[/C][C]2290.76923076923[/C][C]330.230769230769[/C][/ROW]
[ROW][C]58[/C][C]3905[/C][C]3053[/C][C]852[/C][/ROW]
[ROW][C]59[/C][C]2722[/C][C]3053[/C][C]-331[/C][/ROW]
[ROW][C]60[/C][C]2312[/C][C]2589.67857142857[/C][C]-277.678571428572[/C][/ROW]
[ROW][C]61[/C][C]4061[/C][C]3692.07692307692[/C][C]368.923076923077[/C][/ROW]
[ROW][C]62[/C][C]3195[/C][C]3053[/C][C]142[/C][/ROW]
[ROW][C]63[/C][C]3041[/C][C]3053[/C][C]-12[/C][/ROW]
[ROW][C]64[/C][C]2643[/C][C]2589.67857142857[/C][C]53.3214285714284[/C][/ROW]
[ROW][C]65[/C][C]1496[/C][C]1868[/C][C]-372[/C][/ROW]
[ROW][C]66[/C][C]1835[/C][C]1868[/C][C]-33[/C][/ROW]
[ROW][C]67[/C][C]2080[/C][C]1868[/C][C]212[/C][/ROW]
[ROW][C]68[/C][C]2772[/C][C]2589.67857142857[/C][C]182.321428571428[/C][/ROW]
[ROW][C]69[/C][C]2440[/C][C]2589.67857142857[/C][C]-149.678571428572[/C][/ROW]
[ROW][C]70[/C][C]1718[/C][C]1604.35714285714[/C][C]113.642857142857[/C][/ROW]
[ROW][C]71[/C][C]2566[/C][C]2589.67857142857[/C][C]-23.6785714285716[/C][/ROW]
[ROW][C]72[/C][C]893[/C][C]1071.81818181818[/C][C]-178.818181818182[/C][/ROW]
[ROW][C]73[/C][C]2227[/C][C]2029.08695652174[/C][C]197.913043478261[/C][/ROW]
[ROW][C]74[/C][C]1878[/C][C]2029.08695652174[/C][C]-151.086956521739[/C][/ROW]
[ROW][C]75[/C][C]2177[/C][C]2290.76923076923[/C][C]-113.769230769231[/C][/ROW]
[ROW][C]76[/C][C]2352[/C][C]2589.67857142857[/C][C]-237.678571428572[/C][/ROW]
[ROW][C]77[/C][C]2981[/C][C]3053[/C][C]-72[/C][/ROW]
[ROW][C]78[/C][C]1926[/C][C]1868[/C][C]58[/C][/ROW]
[ROW][C]79[/C][C]2400[/C][C]2290.76923076923[/C][C]109.230769230769[/C][/ROW]
[ROW][C]80[/C][C]2034[/C][C]2029.08695652174[/C][C]4.91304347826076[/C][/ROW]
[ROW][C]81[/C][C]1800[/C][C]2029.08695652174[/C][C]-229.086956521739[/C][/ROW]
[ROW][C]82[/C][C]4168[/C][C]3692.07692307692[/C][C]475.923076923077[/C][/ROW]
[ROW][C]83[/C][C]1369[/C][C]1604.35714285714[/C][C]-235.357142857143[/C][/ROW]
[ROW][C]84[/C][C]2403[/C][C]2290.76923076923[/C][C]112.230769230769[/C][/ROW]
[ROW][C]85[/C][C]870[/C][C]1071.81818181818[/C][C]-201.818181818182[/C][/ROW]
[ROW][C]86[/C][C]1968[/C][C]2029.08695652174[/C][C]-61.0869565217392[/C][/ROW]
[ROW][C]87[/C][C]1569[/C][C]1604.35714285714[/C][C]-35.3571428571429[/C][/ROW]
[ROW][C]88[/C][C]3962[/C][C]4483.375[/C][C]-521.375[/C][/ROW]
[ROW][C]89[/C][C]2928[/C][C]2589.67857142857[/C][C]338.321428571428[/C][/ROW]
[ROW][C]90[/C][C]3098[/C][C]3053[/C][C]45[/C][/ROW]
[ROW][C]91[/C][C]2557[/C][C]2589.67857142857[/C][C]-32.6785714285716[/C][/ROW]
[ROW][C]92[/C][C]2329[/C][C]2290.76923076923[/C][C]38.2307692307691[/C][/ROW]
[ROW][C]93[/C][C]1858[/C][C]1604.35714285714[/C][C]253.642857142857[/C][/ROW]
[ROW][C]94[/C][C]3146[/C][C]3053[/C][C]93[/C][/ROW]
[ROW][C]95[/C][C]2581[/C][C]2589.67857142857[/C][C]-8.67857142857156[/C][/ROW]
[ROW][C]96[/C][C]1824[/C][C]1868[/C][C]-44[/C][/ROW]
[ROW][C]97[/C][C]1914[/C][C]1868[/C][C]46[/C][/ROW]
[ROW][C]98[/C][C]2210[/C][C]2290.76923076923[/C][C]-80.7692307692309[/C][/ROW]
[ROW][C]99[/C][C]4118[/C][C]3692.07692307692[/C][C]425.923076923077[/C][/ROW]
[ROW][C]100[/C][C]3844[/C][C]3692.07692307692[/C][C]151.923076923077[/C][/ROW]
[ROW][C]101[/C][C]2791[/C][C]2589.67857142857[/C][C]201.321428571428[/C][/ROW]
[ROW][C]102[/C][C]2425[/C][C]2589.67857142857[/C][C]-164.678571428572[/C][/ROW]
[ROW][C]103[/C][C]2052[/C][C]2029.08695652174[/C][C]22.9130434782608[/C][/ROW]
[ROW][C]104[/C][C]602[/C][C]518.454545454545[/C][C]83.5454545454545[/C][/ROW]
[ROW][C]105[/C][C]2195[/C][C]2029.08695652174[/C][C]165.913043478261[/C][/ROW]
[ROW][C]106[/C][C]2412[/C][C]2589.67857142857[/C][C]-177.678571428572[/C][/ROW]
[ROW][C]107[/C][C]2628[/C][C]3053[/C][C]-425[/C][/ROW]
[ROW][C]108[/C][C]2758[/C][C]3053[/C][C]-295[/C][/ROW]
[ROW][C]109[/C][C]1268[/C][C]1071.81818181818[/C][C]196.181818181818[/C][/ROW]
[ROW][C]110[/C][C]3233[/C][C]2589.67857142857[/C][C]643.321428571428[/C][/ROW]
[ROW][C]111[/C][C]2025[/C][C]2029.08695652174[/C][C]-4.08695652173924[/C][/ROW]
[ROW][C]112[/C][C]2310[/C][C]2290.76923076923[/C][C]19.2307692307691[/C][/ROW]
[ROW][C]113[/C][C]398[/C][C]518.454545454545[/C][C]-120.454545454545[/C][/ROW]
[ROW][C]114[/C][C]2205[/C][C]2290.76923076923[/C][C]-85.7692307692309[/C][/ROW]
[ROW][C]115[/C][C]530[/C][C]518.454545454545[/C][C]11.5454545454545[/C][/ROW]
[ROW][C]116[/C][C]1610[/C][C]1604.35714285714[/C][C]5.64285714285711[/C][/ROW]
[ROW][C]117[/C][C]3153[/C][C]3053[/C][C]100[/C][/ROW]
[ROW][C]118[/C][C]387[/C][C]518.454545454545[/C][C]-131.454545454545[/C][/ROW]
[ROW][C]119[/C][C]2137[/C][C]2589.67857142857[/C][C]-452.678571428572[/C][/ROW]
[ROW][C]120[/C][C]492[/C][C]518.454545454545[/C][C]-26.4545454545455[/C][/ROW]
[ROW][C]121[/C][C]3674[/C][C]3692.07692307692[/C][C]-18.0769230769229[/C][/ROW]
[ROW][C]122[/C][C]2136[/C][C]2029.08695652174[/C][C]106.913043478261[/C][/ROW]
[ROW][C]123[/C][C]1748[/C][C]1604.35714285714[/C][C]143.642857142857[/C][/ROW]
[ROW][C]124[/C][C]1790[/C][C]2029.08695652174[/C][C]-239.086956521739[/C][/ROW]
[ROW][C]125[/C][C]568[/C][C]518.454545454545[/C][C]49.5454545454545[/C][/ROW]
[ROW][C]126[/C][C]2191[/C][C]2029.08695652174[/C][C]161.913043478261[/C][/ROW]
[ROW][C]127[/C][C]2792[/C][C]2589.67857142857[/C][C]202.321428571428[/C][/ROW]
[ROW][C]128[/C][C]1396[/C][C]1604.35714285714[/C][C]-208.357142857143[/C][/ROW]
[ROW][C]129[/C][C]3718[/C][C]4483.375[/C][C]-765.375[/C][/ROW]
[ROW][C]130[/C][C]2554[/C][C]2589.67857142857[/C][C]-35.6785714285716[/C][/ROW]
[ROW][C]131[/C][C]3460[/C][C]3053[/C][C]407[/C][/ROW]
[ROW][C]132[/C][C]1283[/C][C]1604.35714285714[/C][C]-321.357142857143[/C][/ROW]
[ROW][C]133[/C][C]1130[/C][C]1071.81818181818[/C][C]58.1818181818182[/C][/ROW]
[ROW][C]134[/C][C]2974[/C][C]2589.67857142857[/C][C]384.321428571428[/C][/ROW]
[ROW][C]135[/C][C]4346[/C][C]4483.375[/C][C]-137.375[/C][/ROW]
[ROW][C]136[/C][C]1785[/C][C]1604.35714285714[/C][C]180.642857142857[/C][/ROW]
[ROW][C]137[/C][C]4349[/C][C]4483.375[/C][C]-134.375[/C][/ROW]
[ROW][C]138[/C][C]1920[/C][C]2029.08695652174[/C][C]-109.086956521739[/C][/ROW]
[ROW][C]139[/C][C]1923[/C][C]1868[/C][C]55[/C][/ROW]
[ROW][C]140[/C][C]2541[/C][C]2589.67857142857[/C][C]-48.6785714285716[/C][/ROW]
[ROW][C]141[/C][C]1844[/C][C]1868[/C][C]-24[/C][/ROW]
[ROW][C]142[/C][C]4081[/C][C]4483.375[/C][C]-402.375[/C][/ROW]
[ROW][C]143[/C][C]2339[/C][C]2290.76923076923[/C][C]48.2307692307691[/C][/ROW]
[ROW][C]144[/C][C]2035[/C][C]2029.08695652174[/C][C]5.91304347826076[/C][/ROW]
[ROW][C]145[/C][C]3108[/C][C]2589.67857142857[/C][C]518.321428571428[/C][/ROW]
[ROW][C]146[/C][C]1974[/C][C]2290.76923076923[/C][C]-316.769230769231[/C][/ROW]
[ROW][C]147[/C][C]2651[/C][C]2589.67857142857[/C][C]61.3214285714284[/C][/ROW]
[ROW][C]148[/C][C]2702[/C][C]2589.67857142857[/C][C]112.321428571428[/C][/ROW]
[ROW][C]149[/C][C]2[/C][C]49.7272727272727[/C][C]-47.7272727272727[/C][/ROW]
[ROW][C]150[/C][C]207[/C][C]49.7272727272727[/C][C]157.272727272727[/C][/ROW]
[ROW][C]151[/C][C]5[/C][C]49.7272727272727[/C][C]-44.7272727272727[/C][/ROW]
[ROW][C]152[/C][C]8[/C][C]49.7272727272727[/C][C]-41.7272727272727[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]49.7272727272727[/C][C]-49.7272727272727[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]49.7272727272727[/C][C]-49.7272727272727[/C][/ROW]
[ROW][C]155[/C][C]2255[/C][C]2029.08695652174[/C][C]225.913043478261[/C][/ROW]
[ROW][C]156[/C][C]3447[/C][C]3053[/C][C]394[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]49.7272727272727[/C][C]-49.7272727272727[/C][/ROW]
[ROW][C]158[/C][C]4[/C][C]49.7272727272727[/C][C]-45.7272727272727[/C][/ROW]
[ROW][C]159[/C][C]151[/C][C]49.7272727272727[/C][C]101.272727272727[/C][/ROW]
[ROW][C]160[/C][C]474[/C][C]518.454545454545[/C][C]-44.4545454545455[/C][/ROW]
[ROW][C]161[/C][C]141[/C][C]49.7272727272727[/C][C]91.2727272727273[/C][/ROW]
[ROW][C]162[/C][C]1111[/C][C]1071.81818181818[/C][C]39.1818181818182[/C][/ROW]
[ROW][C]163[/C][C]29[/C][C]49.7272727272727[/C][C]-20.7272727272727[/C][/ROW]
[ROW][C]164[/C][C]2011[/C][C]2029.08695652174[/C][C]-18.0869565217392[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158387&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158387&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1785	1868	-83
2	1227	1071.81818181818	155.181818181818
3	1931	2029.08695652174	-98.0869565217392
4	2683	3053	-370
5	1247	1071.81818181818	175.181818181818
6	631	518.454545454545	112.545454545455
7	4845	4483.375	361.625
8	381	518.454545454545	-137.454545454545
9	2066	2029.08695652174	36.9130434782608
10	1759	1868	-109
11	2157	2029.08695652174	127.913043478261
12	2211	2290.76923076923	-79.7692307692309
13	1967	2029.08695652174	-62.0869565217392
14	3005	3053	-48
15	2228	2029.08695652174	198.913043478261
16	5009	4483.375	525.625
17	2353	2589.67857142857	-236.678571428572
18	3278	3692.07692307692	-414.076923076923
19	1473	1604.35714285714	-131.357142857143
20	2347	2290.76923076923	56.2307692307691
21	2522	2589.67857142857	-67.6785714285716
22	2987	3692.07692307692	-705.076923076923
23	1523	1604.35714285714	-81.3571428571429
24	1761	1604.35714285714	156.642857142857
25	3660	3692.07692307692	-32.0769230769229
26	2937	3053	-116
27	2914	3053	-139
28	2009	1868	141
29	1555	1604.35714285714	-49.3571428571429
30	3049	3053	-4
31	2430	2589.67857142857	-159.678571428572
32	2092	2029.08695652174	62.9130434782608
33	2404	2589.67857142857	-185.678571428572
34	1018	1071.81818181818	-53.8181818181818
35	3462	3692.07692307692	-230.076923076923
36	2764	3053	-289
37	3710	3692.07692307692	17.9230769230771
38	1947	1868	79
39	947	1071.81818181818	-124.818181818182
40	3608	3692.07692307692	-84.0769230769229
41	3324	3692.07692307692	-368.076923076923
42	1842	2029.08695652174	-187.086956521739
43	1869	2029.08695652174	-160.086956521739
44	1813	1604.35714285714	208.642857142857
45	2496	2589.67857142857	-93.6785714285716
46	5557	4483.375	1073.625
47	918	1071.81818181818	-153.818181818182
48	2254	2290.76923076923	-36.7692307692309
49	4103	3692.07692307692	410.923076923077
50	2241	2589.67857142857	-348.678571428572
51	1942	1868	74
52	496	518.454545454545	-22.4545454545455
53	2594	2589.67857142857	4.32142857142844
54	744	518.454545454545	225.545454545455
55	1161	1071.81818181818	89.1818181818182
56	3121	3053	68
57	2621	2290.76923076923	330.230769230769
58	3905	3053	852
59	2722	3053	-331
60	2312	2589.67857142857	-277.678571428572
61	4061	3692.07692307692	368.923076923077
62	3195	3053	142
63	3041	3053	-12
64	2643	2589.67857142857	53.3214285714284
65	1496	1868	-372
66	1835	1868	-33
67	2080	1868	212
68	2772	2589.67857142857	182.321428571428
69	2440	2589.67857142857	-149.678571428572
70	1718	1604.35714285714	113.642857142857
71	2566	2589.67857142857	-23.6785714285716
72	893	1071.81818181818	-178.818181818182
73	2227	2029.08695652174	197.913043478261
74	1878	2029.08695652174	-151.086956521739
75	2177	2290.76923076923	-113.769230769231
76	2352	2589.67857142857	-237.678571428572
77	2981	3053	-72
78	1926	1868	58
79	2400	2290.76923076923	109.230769230769
80	2034	2029.08695652174	4.91304347826076
81	1800	2029.08695652174	-229.086956521739
82	4168	3692.07692307692	475.923076923077
83	1369	1604.35714285714	-235.357142857143
84	2403	2290.76923076923	112.230769230769
85	870	1071.81818181818	-201.818181818182
86	1968	2029.08695652174	-61.0869565217392
87	1569	1604.35714285714	-35.3571428571429
88	3962	4483.375	-521.375
89	2928	2589.67857142857	338.321428571428
90	3098	3053	45
91	2557	2589.67857142857	-32.6785714285716
92	2329	2290.76923076923	38.2307692307691
93	1858	1604.35714285714	253.642857142857
94	3146	3053	93
95	2581	2589.67857142857	-8.67857142857156
96	1824	1868	-44
97	1914	1868	46
98	2210	2290.76923076923	-80.7692307692309
99	4118	3692.07692307692	425.923076923077
100	3844	3692.07692307692	151.923076923077
101	2791	2589.67857142857	201.321428571428
102	2425	2589.67857142857	-164.678571428572
103	2052	2029.08695652174	22.9130434782608
104	602	518.454545454545	83.5454545454545
105	2195	2029.08695652174	165.913043478261
106	2412	2589.67857142857	-177.678571428572
107	2628	3053	-425
108	2758	3053	-295
109	1268	1071.81818181818	196.181818181818
110	3233	2589.67857142857	643.321428571428
111	2025	2029.08695652174	-4.08695652173924
112	2310	2290.76923076923	19.2307692307691
113	398	518.454545454545	-120.454545454545
114	2205	2290.76923076923	-85.7692307692309
115	530	518.454545454545	11.5454545454545
116	1610	1604.35714285714	5.64285714285711
117	3153	3053	100
118	387	518.454545454545	-131.454545454545
119	2137	2589.67857142857	-452.678571428572
120	492	518.454545454545	-26.4545454545455
121	3674	3692.07692307692	-18.0769230769229
122	2136	2029.08695652174	106.913043478261
123	1748	1604.35714285714	143.642857142857
124	1790	2029.08695652174	-239.086956521739
125	568	518.454545454545	49.5454545454545
126	2191	2029.08695652174	161.913043478261
127	2792	2589.67857142857	202.321428571428
128	1396	1604.35714285714	-208.357142857143
129	3718	4483.375	-765.375
130	2554	2589.67857142857	-35.6785714285716
131	3460	3053	407
132	1283	1604.35714285714	-321.357142857143
133	1130	1071.81818181818	58.1818181818182
134	2974	2589.67857142857	384.321428571428
135	4346	4483.375	-137.375
136	1785	1604.35714285714	180.642857142857
137	4349	4483.375	-134.375
138	1920	2029.08695652174	-109.086956521739
139	1923	1868	55
140	2541	2589.67857142857	-48.6785714285716
141	1844	1868	-24
142	4081	4483.375	-402.375
143	2339	2290.76923076923	48.2307692307691
144	2035	2029.08695652174	5.91304347826076
145	3108	2589.67857142857	518.321428571428
146	1974	2290.76923076923	-316.769230769231
147	2651	2589.67857142857	61.3214285714284
148	2702	2589.67857142857	112.321428571428
149	2	49.7272727272727	-47.7272727272727
150	207	49.7272727272727	157.272727272727
151	5	49.7272727272727	-44.7272727272727
152	8	49.7272727272727	-41.7272727272727
153	0	49.7272727272727	-49.7272727272727
154	0	49.7272727272727	-49.7272727272727
155	2255	2029.08695652174	225.913043478261
156	3447	3053	394
157	0	49.7272727272727	-49.7272727272727
158	4	49.7272727272727	-45.7272727272727
159	151	49.7272727272727	101.272727272727
160	474	518.454545454545	-44.4545454545455
161	141	49.7272727272727	91.2727272727273
162	1111	1071.81818181818	39.1818181818182
163	29	49.7272727272727	-20.7272727272727
164	2011	2029.08695652174	-18.0869565217392

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

R code (references can be found in the software module):

library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code