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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

Sun, 18 Dec 2011 13:12:02 -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/18/t1324231973g8qp9a7k6jdace9.htm/, Retrieved Sun, 05 May 2024 14:26:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157108, Retrieved Sun, 05 May 2024 14:26:34 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

101

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)] [WS10 PLC no categ...] [2011-12-11 15:02:15] [9d4f280afcb4ecc352d7c6f913a0a151]
-   PD    [Recursive Partitioning (Regression Trees)] [WS10 PLC no categ...] [2011-12-12 18:04:39] [9d4f280afcb4ecc352d7c6f913a0a151]
-   PD      [Recursive Partitioning (Regression Trees)] [WS10 PLC no categ...] [2011-12-12 18:38:05] [9d4f280afcb4ecc352d7c6f913a0a151]
-   PD          [Recursive Partitioning (Regression Trees)] [Paper Regression ...] [2011-12-18 18:12:02] [2a6d487209befbc7c5ce02a41ecac161] [Current]
-   P             [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2011-12-22 17:01:50] [9d4f280afcb4ecc352d7c6f913a0a151] 
-   P               [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2011-12-22 17:38:42] [9d4f280afcb4ecc352d7c6f913a0a151] 

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Dataseries X:

Download CSV

Histogram

Boxplots

272545	1747	69	483	96	38
179444	1209	64	429	71	34
222373	1844	69	673	70	42
218443	2683	104	1137	134	38
167843	1229	52	374	66	27
70849	631	28	179	8	35
506574	4627	123	2251	149	33
33186	381	19	111	1	18
216660	2063	59	740	83	34
213274	1758	44	595	82	33
307153	2132	109	800	92	46
239324	2139	116	665	117	55
166215	1702	71	648	50	37
364402	2965	79	1172	139	55
244103	2098	84	674	79	44
384448	4904	178	1692	175	59
325587	2242	68	811	114	36
323652	2978	158	1168	98	39
176082	1438	55	507	103	29
266736	2347	87	689	135	51
278265	2522	70	837	123	49
442703	2889	103	1270	87	39
180393	1447	41	462	66	25
189897	1717	54	601	103	52
234247	3363	122	1242	141	45
238002	2912	126	1031	113	38
267268	2828	127	1062	99	41
270787	1972	86	618	117	43
155915	1495	51	559	57	32
342564	2841	70	1062	127	41
282172	2299	76	913	123	47
216584	1909	76	643	44	50
318669	2101	85	782	133	48
98672	971	37	322	43	37
391593	3332	98	1249	138	43
273950	2764	56	1186	83	42
425120	3682	120	1324	112	44
227636	1918	83	640	79	36
115658	947	33	284	33	17
354670	3468	196	1222	129	42
324178	3247	80	1490	123	39
178083	1692	67	667	71	41
195153	1736	74	635	75	36
181810	1790	63	483	68	49
153778	2496	82	1022	50	45
455168	5501	151	2068	101	41
78800	918	42	330	20	26
208051	2228	76	648	101	52
348077	4051	118	1367	149	47
175523	2081	54	868	99	45
224591	1875	74	588	95	40
24188	496	24	218	8	4
372238	2539	316	833	85	44
65029	744	17	255	21	18
101097	1161	64	454	30	14
279012	3027	58	1108	97	37
317644	2527	85	662	122	61
340471	3706	186	1119	127	39
358958	2668	142	1058	159	42
252529	2175	83	822	89	36
379078	3980	142	1310	154	50
304468	3165	117	1145	139	28
270190	2954	114	1191	101	43
264889	2610	88	931	92	42
228595	1427	67	557	52	37
216027	1646	65	436	96	30
198798	1971	132	596	88	35
238146	2747	146	837	85	44
234891	2309	82	848	135	36
175816	1684	69	625	143	28
239314	2537	68	865	99	45
73566	893	32	385	22	23
242622	2195	84	718	78	45
187167	1695	53	705	79	38
209049	2061	63	732	131	38
360592	2329	86	988	140	46
342846	2695	92	1077	130	36
207650	1809	107	524	78	41
206500	2290	62	697	133	38
182357	1792	65	644	83	37
153613	1678	46	622	62	28
456979	4024	125	1227	151	45
145943	1369	69	653	30	26
280366	2309	105	656	117	44
80953	870	25	437	49	8
150216	1966	54	822	52	27
167878	1459	59	423	73	38
381493	3832	207	1493	81	37
331734	2721	117	932	136	57
179797	3086	105	1044	165	45
264350	2383	93	798	81	37
262793	2210	78	678	161	40
189142	1829	63	597	48	31
275997	3087	74	1099	149	36
328875	2559	82	966	75	40
189252	1624	36	555	83	36
222504	1607	51	552	94	35
287386	2109	79	778	159	39
392647	4027	153	1324	152	65
397681	3706	109	1415	165	30
287748	2715	137	853	117	51
294320	2325	65	848	148	41
186856	2001	181	640	73	36
43287	602	14	214	13	19
185468	2146	80	716	89	23
236654	2328	147	796	97	44
268077	2618	49	1170	129	40
305195	2688	90	1048	169	40
151344	1222	73	404	28	30
154287	3106	92	906	116	41
307000	1869	68	609	76	40
298039	2304	88	688	147	45
23623	398	11	156	12	1
195817	2205	73	779	146	40
61857	530	25	192	23	11
163766	1596	48	457	83	45
415339	3093	119	1200	151	38
21054	387	16	146	4	0
252805	2137	52	866	81	30
31961	492	22	200	18	8
317367	3450	115	1230	111	39
240153	2089	65	696	76	48
175083	1659	89	491	55	48
152043	1685	53	670	44	29
38214	568	34	276	16	8
216299	2060	43	716	73	43
357602	2792	82	1021	137	52
198104	1395	61	481	50	53
410803	3591	81	1582	137	48
316105	2388	98	820	154	48
397297	3334	124	1153	137	50
187992	1250	35	473	71	40
102424	1121	42	401	42	36
286327	2880	335	954	84	40
409878	4110	172	1449	113	46
143860	1759	54	546	63	42
391854	4139	133	1728	127	46
157429	1831	77	689	55	39
258751	1787	48	590	110	41
282399	2535	94	897	110	46
217665	1816	113	613	38	32
367246	3875	118	1548	95	39
244698	2296	91	800	127	39
173260	2035	63	716	41	21
325118	2986	101	959	145	47
168994	1915	57	720	147	50
253330	2648	86	1023	119	36
301703	2633	105	818	185	44
1	2	0	0	0	0
14688	207	10	85	4	0
98	5	1	0	0	0
455	8	2	0	0	0
0	0	0	0	0	0
0	0	0	0	0	0
246435	2117	85	737	69	37
392245	3361	158	1099	141	52
0	0	0	0	0	0
203	4	4	0	0	0
7199	151	5	74	7	0
46660	474	20	259	12	5
17547	141	5	69	0	1
116678	1047	42	285	37	43
969	29	2	0	0	0
206501	1822	68	591	52	34

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'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=157108&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=157108&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157108&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Goodness of Fit
Correlation	0.9296
R-squared	0.8641
RMSE	42312.3546

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9296 \tabularnewline
R-squared & 0.8641 \tabularnewline
RMSE & 42312.3546 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157108&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9296[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8641[/C][/ROW]
[ROW][C]RMSE[/C][C]42312.3546[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157108&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157108&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	0.9296
R-squared	0.8641
RMSE	42312.3546

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	272545	193995.24	78549.76
2	179444	193995.24	-14551.24
3	222373	193995.24	28377.76
4	218443	290227.304347826	-71784.3043478261
5	167843	193995.24	-26152.24
6	70849	90372.6	-19523.6
7	506574	389863.952380952	116710.047619048
8	33186	36003.3333333333	-2817.33333333334
9	216660	193995.24	22664.76
10	213274	193995.24	19278.76
11	307153	250593.705882353	56559.294117647
12	239324	250593.705882353	-11269.705882353
13	166215	193995.24	-27780.24
14	364402	290227.304347826	74174.6956521739
15	244103	250593.705882353	-6490.70588235295
16	384448	389863.952380952	-5415.95238095237
17	325587	250593.705882353	74993.294117647
18	323652	290227.304347826	33424.6956521739
19	176082	193995.24	-17913.24
20	266736	290227.304347826	-23491.3043478261
21	278265	290227.304347826	-11962.3043478261
22	442703	389863.952380952	52839.0476190476
23	180393	193995.24	-13602.24
24	189897	193995.24	-4098.23999999999
25	234247	389863.952380952	-155616.952380952
26	238002	290227.304347826	-52225.3043478261
27	267268	290227.304347826	-22959.3043478261
28	270787	193995.24	76791.76
29	155915	193995.24	-38080.24
30	342564	290227.304347826	52336.6956521739
31	282172	290227.304347826	-8055.30434782611
32	216584	193995.24	22588.76
33	318669	250593.705882353	68075.294117647
34	98672	90372.6	8299.39999999999
35	391593	389863.952380952	1729.04761904763
36	273950	290227.304347826	-16277.3043478261
37	425120	389863.952380952	35256.0476190476
38	227636	193995.24	33640.76
39	115658	90372.6	25285.4
40	354670	389863.952380952	-35193.9523809524
41	324178	389863.952380952	-65685.9523809524
42	178083	193995.24	-15912.24
43	195153	193995.24	1157.76000000001
44	181810	193995.24	-12185.24
45	153778	290227.304347826	-136449.304347826
46	455168	389863.952380952	65304.0476190476
47	78800	90372.6	-11572.6
48	208051	250593.705882353	-42542.705882353
49	348077	389863.952380952	-41786.9523809524
50	175523	193995.24	-18472.24
51	224591	193995.24	30595.76
52	24188	36003.3333333333	-11815.3333333333
53	372238	290227.304347826	82010.6956521739
54	65029	90372.6	-25343.6
55	101097	90372.6	10724.4
56	279012	290227.304347826	-11215.3043478261
57	317644	290227.304347826	27416.6956521739
58	340471	290227.304347826	50243.6956521739
59	358958	290227.304347826	68730.6956521739
60	252529	250593.705882353	1935.29411764705
61	379078	389863.952380952	-10785.9523809524
62	304468	290227.304347826	14240.6956521739
63	270190	290227.304347826	-20037.3043478261
64	264889	290227.304347826	-25338.3043478261
65	228595	193995.24	34599.76
66	216027	193995.24	22031.76
67	198798	193995.24	4802.76000000001
68	238146	290227.304347826	-52081.3043478261
69	234891	290227.304347826	-55336.3043478261
70	175816	193995.24	-18179.24
71	239314	290227.304347826	-50913.3043478261
72	73566	90372.6	-16806.6
73	242622	250593.705882353	-7971.70588235295
74	187167	193995.24	-6828.23999999999
75	209049	193995.24	15053.76
76	360592	290227.304347826	70364.6956521739
77	342846	290227.304347826	52618.6956521739
78	207650	193995.24	13654.76
79	206500	250593.705882353	-44093.705882353
80	182357	193995.24	-11638.24
81	153613	193995.24	-40382.24
82	456979	389863.952380952	67115.0476190476
83	145943	193995.24	-48052.24
84	280366	290227.304347826	-9861.30434782611
85	80953	90372.6	-9419.60000000001
86	150216	193995.24	-43779.24
87	167878	193995.24	-26117.24
88	381493	389863.952380952	-8370.95238095237
89	331734	290227.304347826	41506.6956521739
90	179797	290227.304347826	-110430.304347826
91	264350	290227.304347826	-25877.3043478261
92	262793	250593.705882353	12199.294117647
93	189142	193995.24	-4853.23999999999
94	275997	290227.304347826	-14230.3043478261
95	328875	290227.304347826	38647.6956521739
96	189252	193995.24	-4743.23999999999
97	222504	193995.24	28508.76
98	287386	250593.705882353	36792.294117647
99	392647	389863.952380952	2783.04761904763
100	397681	389863.952380952	7817.04761904763
101	287748	290227.304347826	-2479.30434782611
102	294320	290227.304347826	4092.69565217389
103	186856	193995.24	-7139.23999999999
104	43287	36003.3333333333	7283.66666666666
105	185468	250593.705882353	-65125.705882353
106	236654	290227.304347826	-53573.3043478261
107	268077	290227.304347826	-22150.3043478261
108	305195	290227.304347826	14967.6956521739
109	151344	193995.24	-42651.24
110	154287	290227.304347826	-135940.304347826
111	307000	193995.24	113004.76
112	298039	290227.304347826	7811.69565217389
113	23623	36003.3333333333	-12380.3333333333
114	195817	250593.705882353	-54776.705882353
115	61857	36003.3333333333	25853.6666666667
116	163766	193995.24	-30229.24
117	415339	389863.952380952	25475.0476190476
118	21054	36003.3333333333	-14949.3333333333
119	252805	250593.705882353	2211.29411764705
120	31961	36003.3333333333	-4042.33333333334
121	317367	389863.952380952	-72496.9523809524
122	240153	250593.705882353	-10440.705882353
123	175083	193995.24	-18912.24
124	152043	193995.24	-41952.24
125	38214	36003.3333333333	2210.66666666666
126	216299	193995.24	22303.76
127	357602	290227.304347826	67374.6956521739
128	198104	193995.24	4108.76000000001
129	410803	389863.952380952	20939.0476190476
130	316105	290227.304347826	25877.6956521739
131	397297	290227.304347826	107069.695652174
132	187992	193995.24	-6003.23999999999
133	102424	90372.6	12051.4
134	286327	290227.304347826	-3900.30434782611
135	409878	389863.952380952	20014.0476190476
136	143860	193995.24	-50135.24
137	391854	389863.952380952	1990.04761904763
138	157429	193995.24	-36566.24
139	258751	193995.24	64755.76
140	282399	290227.304347826	-7828.30434782611
141	217665	193995.24	23669.76
142	367246	389863.952380952	-22617.9523809524
143	244698	250593.705882353	-5895.70588235295
144	173260	193995.24	-20735.24
145	325118	290227.304347826	34890.6956521739
146	168994	193995.24	-25001.24
147	253330	290227.304347826	-36897.3043478261
148	301703	290227.304347826	11475.6956521739
149	1	3741.81818181818	-3740.81818181818
150	14688	3741.81818181818	10946.1818181818
151	98	3741.81818181818	-3643.81818181818
152	455	3741.81818181818	-3286.81818181818
153	0	3741.81818181818	-3741.81818181818
154	0	3741.81818181818	-3741.81818181818
155	246435	250593.705882353	-4158.70588235295
156	392245	290227.304347826	102017.695652174
157	0	3741.81818181818	-3741.81818181818
158	203	3741.81818181818	-3538.81818181818
159	7199	3741.81818181818	3457.18181818182
160	46660	36003.3333333333	10656.6666666667
161	17547	3741.81818181818	13805.1818181818
162	116678	90372.6	26305.4
163	969	3741.81818181818	-2772.81818181818
164	206501	193995.24	12505.76

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 272545 & 193995.24 & 78549.76 \tabularnewline
2 & 179444 & 193995.24 & -14551.24 \tabularnewline
3 & 222373 & 193995.24 & 28377.76 \tabularnewline
4 & 218443 & 290227.304347826 & -71784.3043478261 \tabularnewline
5 & 167843 & 193995.24 & -26152.24 \tabularnewline
6 & 70849 & 90372.6 & -19523.6 \tabularnewline
7 & 506574 & 389863.952380952 & 116710.047619048 \tabularnewline
8 & 33186 & 36003.3333333333 & -2817.33333333334 \tabularnewline
9 & 216660 & 193995.24 & 22664.76 \tabularnewline
10 & 213274 & 193995.24 & 19278.76 \tabularnewline
11 & 307153 & 250593.705882353 & 56559.294117647 \tabularnewline
12 & 239324 & 250593.705882353 & -11269.705882353 \tabularnewline
13 & 166215 & 193995.24 & -27780.24 \tabularnewline
14 & 364402 & 290227.304347826 & 74174.6956521739 \tabularnewline
15 & 244103 & 250593.705882353 & -6490.70588235295 \tabularnewline
16 & 384448 & 389863.952380952 & -5415.95238095237 \tabularnewline
17 & 325587 & 250593.705882353 & 74993.294117647 \tabularnewline
18 & 323652 & 290227.304347826 & 33424.6956521739 \tabularnewline
19 & 176082 & 193995.24 & -17913.24 \tabularnewline
20 & 266736 & 290227.304347826 & -23491.3043478261 \tabularnewline
21 & 278265 & 290227.304347826 & -11962.3043478261 \tabularnewline
22 & 442703 & 389863.952380952 & 52839.0476190476 \tabularnewline
23 & 180393 & 193995.24 & -13602.24 \tabularnewline
24 & 189897 & 193995.24 & -4098.23999999999 \tabularnewline
25 & 234247 & 389863.952380952 & -155616.952380952 \tabularnewline
26 & 238002 & 290227.304347826 & -52225.3043478261 \tabularnewline
27 & 267268 & 290227.304347826 & -22959.3043478261 \tabularnewline
28 & 270787 & 193995.24 & 76791.76 \tabularnewline
29 & 155915 & 193995.24 & -38080.24 \tabularnewline
30 & 342564 & 290227.304347826 & 52336.6956521739 \tabularnewline
31 & 282172 & 290227.304347826 & -8055.30434782611 \tabularnewline
32 & 216584 & 193995.24 & 22588.76 \tabularnewline
33 & 318669 & 250593.705882353 & 68075.294117647 \tabularnewline
34 & 98672 & 90372.6 & 8299.39999999999 \tabularnewline
35 & 391593 & 389863.952380952 & 1729.04761904763 \tabularnewline
36 & 273950 & 290227.304347826 & -16277.3043478261 \tabularnewline
37 & 425120 & 389863.952380952 & 35256.0476190476 \tabularnewline
38 & 227636 & 193995.24 & 33640.76 \tabularnewline
39 & 115658 & 90372.6 & 25285.4 \tabularnewline
40 & 354670 & 389863.952380952 & -35193.9523809524 \tabularnewline
41 & 324178 & 389863.952380952 & -65685.9523809524 \tabularnewline
42 & 178083 & 193995.24 & -15912.24 \tabularnewline
43 & 195153 & 193995.24 & 1157.76000000001 \tabularnewline
44 & 181810 & 193995.24 & -12185.24 \tabularnewline
45 & 153778 & 290227.304347826 & -136449.304347826 \tabularnewline
46 & 455168 & 389863.952380952 & 65304.0476190476 \tabularnewline
47 & 78800 & 90372.6 & -11572.6 \tabularnewline
48 & 208051 & 250593.705882353 & -42542.705882353 \tabularnewline
49 & 348077 & 389863.952380952 & -41786.9523809524 \tabularnewline
50 & 175523 & 193995.24 & -18472.24 \tabularnewline
51 & 224591 & 193995.24 & 30595.76 \tabularnewline
52 & 24188 & 36003.3333333333 & -11815.3333333333 \tabularnewline
53 & 372238 & 290227.304347826 & 82010.6956521739 \tabularnewline
54 & 65029 & 90372.6 & -25343.6 \tabularnewline
55 & 101097 & 90372.6 & 10724.4 \tabularnewline
56 & 279012 & 290227.304347826 & -11215.3043478261 \tabularnewline
57 & 317644 & 290227.304347826 & 27416.6956521739 \tabularnewline
58 & 340471 & 290227.304347826 & 50243.6956521739 \tabularnewline
59 & 358958 & 290227.304347826 & 68730.6956521739 \tabularnewline
60 & 252529 & 250593.705882353 & 1935.29411764705 \tabularnewline
61 & 379078 & 389863.952380952 & -10785.9523809524 \tabularnewline
62 & 304468 & 290227.304347826 & 14240.6956521739 \tabularnewline
63 & 270190 & 290227.304347826 & -20037.3043478261 \tabularnewline
64 & 264889 & 290227.304347826 & -25338.3043478261 \tabularnewline
65 & 228595 & 193995.24 & 34599.76 \tabularnewline
66 & 216027 & 193995.24 & 22031.76 \tabularnewline
67 & 198798 & 193995.24 & 4802.76000000001 \tabularnewline
68 & 238146 & 290227.304347826 & -52081.3043478261 \tabularnewline
69 & 234891 & 290227.304347826 & -55336.3043478261 \tabularnewline
70 & 175816 & 193995.24 & -18179.24 \tabularnewline
71 & 239314 & 290227.304347826 & -50913.3043478261 \tabularnewline
72 & 73566 & 90372.6 & -16806.6 \tabularnewline
73 & 242622 & 250593.705882353 & -7971.70588235295 \tabularnewline
74 & 187167 & 193995.24 & -6828.23999999999 \tabularnewline
75 & 209049 & 193995.24 & 15053.76 \tabularnewline
76 & 360592 & 290227.304347826 & 70364.6956521739 \tabularnewline
77 & 342846 & 290227.304347826 & 52618.6956521739 \tabularnewline
78 & 207650 & 193995.24 & 13654.76 \tabularnewline
79 & 206500 & 250593.705882353 & -44093.705882353 \tabularnewline
80 & 182357 & 193995.24 & -11638.24 \tabularnewline
81 & 153613 & 193995.24 & -40382.24 \tabularnewline
82 & 456979 & 389863.952380952 & 67115.0476190476 \tabularnewline
83 & 145943 & 193995.24 & -48052.24 \tabularnewline
84 & 280366 & 290227.304347826 & -9861.30434782611 \tabularnewline
85 & 80953 & 90372.6 & -9419.60000000001 \tabularnewline
86 & 150216 & 193995.24 & -43779.24 \tabularnewline
87 & 167878 & 193995.24 & -26117.24 \tabularnewline
88 & 381493 & 389863.952380952 & -8370.95238095237 \tabularnewline
89 & 331734 & 290227.304347826 & 41506.6956521739 \tabularnewline
90 & 179797 & 290227.304347826 & -110430.304347826 \tabularnewline
91 & 264350 & 290227.304347826 & -25877.3043478261 \tabularnewline
92 & 262793 & 250593.705882353 & 12199.294117647 \tabularnewline
93 & 189142 & 193995.24 & -4853.23999999999 \tabularnewline
94 & 275997 & 290227.304347826 & -14230.3043478261 \tabularnewline
95 & 328875 & 290227.304347826 & 38647.6956521739 \tabularnewline
96 & 189252 & 193995.24 & -4743.23999999999 \tabularnewline
97 & 222504 & 193995.24 & 28508.76 \tabularnewline
98 & 287386 & 250593.705882353 & 36792.294117647 \tabularnewline
99 & 392647 & 389863.952380952 & 2783.04761904763 \tabularnewline
100 & 397681 & 389863.952380952 & 7817.04761904763 \tabularnewline
101 & 287748 & 290227.304347826 & -2479.30434782611 \tabularnewline
102 & 294320 & 290227.304347826 & 4092.69565217389 \tabularnewline
103 & 186856 & 193995.24 & -7139.23999999999 \tabularnewline
104 & 43287 & 36003.3333333333 & 7283.66666666666 \tabularnewline
105 & 185468 & 250593.705882353 & -65125.705882353 \tabularnewline
106 & 236654 & 290227.304347826 & -53573.3043478261 \tabularnewline
107 & 268077 & 290227.304347826 & -22150.3043478261 \tabularnewline
108 & 305195 & 290227.304347826 & 14967.6956521739 \tabularnewline
109 & 151344 & 193995.24 & -42651.24 \tabularnewline
110 & 154287 & 290227.304347826 & -135940.304347826 \tabularnewline
111 & 307000 & 193995.24 & 113004.76 \tabularnewline
112 & 298039 & 290227.304347826 & 7811.69565217389 \tabularnewline
113 & 23623 & 36003.3333333333 & -12380.3333333333 \tabularnewline
114 & 195817 & 250593.705882353 & -54776.705882353 \tabularnewline
115 & 61857 & 36003.3333333333 & 25853.6666666667 \tabularnewline
116 & 163766 & 193995.24 & -30229.24 \tabularnewline
117 & 415339 & 389863.952380952 & 25475.0476190476 \tabularnewline
118 & 21054 & 36003.3333333333 & -14949.3333333333 \tabularnewline
119 & 252805 & 250593.705882353 & 2211.29411764705 \tabularnewline
120 & 31961 & 36003.3333333333 & -4042.33333333334 \tabularnewline
121 & 317367 & 389863.952380952 & -72496.9523809524 \tabularnewline
122 & 240153 & 250593.705882353 & -10440.705882353 \tabularnewline
123 & 175083 & 193995.24 & -18912.24 \tabularnewline
124 & 152043 & 193995.24 & -41952.24 \tabularnewline
125 & 38214 & 36003.3333333333 & 2210.66666666666 \tabularnewline
126 & 216299 & 193995.24 & 22303.76 \tabularnewline
127 & 357602 & 290227.304347826 & 67374.6956521739 \tabularnewline
128 & 198104 & 193995.24 & 4108.76000000001 \tabularnewline
129 & 410803 & 389863.952380952 & 20939.0476190476 \tabularnewline
130 & 316105 & 290227.304347826 & 25877.6956521739 \tabularnewline
131 & 397297 & 290227.304347826 & 107069.695652174 \tabularnewline
132 & 187992 & 193995.24 & -6003.23999999999 \tabularnewline
133 & 102424 & 90372.6 & 12051.4 \tabularnewline
134 & 286327 & 290227.304347826 & -3900.30434782611 \tabularnewline
135 & 409878 & 389863.952380952 & 20014.0476190476 \tabularnewline
136 & 143860 & 193995.24 & -50135.24 \tabularnewline
137 & 391854 & 389863.952380952 & 1990.04761904763 \tabularnewline
138 & 157429 & 193995.24 & -36566.24 \tabularnewline
139 & 258751 & 193995.24 & 64755.76 \tabularnewline
140 & 282399 & 290227.304347826 & -7828.30434782611 \tabularnewline
141 & 217665 & 193995.24 & 23669.76 \tabularnewline
142 & 367246 & 389863.952380952 & -22617.9523809524 \tabularnewline
143 & 244698 & 250593.705882353 & -5895.70588235295 \tabularnewline
144 & 173260 & 193995.24 & -20735.24 \tabularnewline
145 & 325118 & 290227.304347826 & 34890.6956521739 \tabularnewline
146 & 168994 & 193995.24 & -25001.24 \tabularnewline
147 & 253330 & 290227.304347826 & -36897.3043478261 \tabularnewline
148 & 301703 & 290227.304347826 & 11475.6956521739 \tabularnewline
149 & 1 & 3741.81818181818 & -3740.81818181818 \tabularnewline
150 & 14688 & 3741.81818181818 & 10946.1818181818 \tabularnewline
151 & 98 & 3741.81818181818 & -3643.81818181818 \tabularnewline
152 & 455 & 3741.81818181818 & -3286.81818181818 \tabularnewline
153 & 0 & 3741.81818181818 & -3741.81818181818 \tabularnewline
154 & 0 & 3741.81818181818 & -3741.81818181818 \tabularnewline
155 & 246435 & 250593.705882353 & -4158.70588235295 \tabularnewline
156 & 392245 & 290227.304347826 & 102017.695652174 \tabularnewline
157 & 0 & 3741.81818181818 & -3741.81818181818 \tabularnewline
158 & 203 & 3741.81818181818 & -3538.81818181818 \tabularnewline
159 & 7199 & 3741.81818181818 & 3457.18181818182 \tabularnewline
160 & 46660 & 36003.3333333333 & 10656.6666666667 \tabularnewline
161 & 17547 & 3741.81818181818 & 13805.1818181818 \tabularnewline
162 & 116678 & 90372.6 & 26305.4 \tabularnewline
163 & 969 & 3741.81818181818 & -2772.81818181818 \tabularnewline
164 & 206501 & 193995.24 & 12505.76 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157108&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]272545[/C][C]193995.24[/C][C]78549.76[/C][/ROW]
[ROW][C]2[/C][C]179444[/C][C]193995.24[/C][C]-14551.24[/C][/ROW]
[ROW][C]3[/C][C]222373[/C][C]193995.24[/C][C]28377.76[/C][/ROW]
[ROW][C]4[/C][C]218443[/C][C]290227.304347826[/C][C]-71784.3043478261[/C][/ROW]
[ROW][C]5[/C][C]167843[/C][C]193995.24[/C][C]-26152.24[/C][/ROW]
[ROW][C]6[/C][C]70849[/C][C]90372.6[/C][C]-19523.6[/C][/ROW]
[ROW][C]7[/C][C]506574[/C][C]389863.952380952[/C][C]116710.047619048[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]36003.3333333333[/C][C]-2817.33333333334[/C][/ROW]
[ROW][C]9[/C][C]216660[/C][C]193995.24[/C][C]22664.76[/C][/ROW]
[ROW][C]10[/C][C]213274[/C][C]193995.24[/C][C]19278.76[/C][/ROW]
[ROW][C]11[/C][C]307153[/C][C]250593.705882353[/C][C]56559.294117647[/C][/ROW]
[ROW][C]12[/C][C]239324[/C][C]250593.705882353[/C][C]-11269.705882353[/C][/ROW]
[ROW][C]13[/C][C]166215[/C][C]193995.24[/C][C]-27780.24[/C][/ROW]
[ROW][C]14[/C][C]364402[/C][C]290227.304347826[/C][C]74174.6956521739[/C][/ROW]
[ROW][C]15[/C][C]244103[/C][C]250593.705882353[/C][C]-6490.70588235295[/C][/ROW]
[ROW][C]16[/C][C]384448[/C][C]389863.952380952[/C][C]-5415.95238095237[/C][/ROW]
[ROW][C]17[/C][C]325587[/C][C]250593.705882353[/C][C]74993.294117647[/C][/ROW]
[ROW][C]18[/C][C]323652[/C][C]290227.304347826[/C][C]33424.6956521739[/C][/ROW]
[ROW][C]19[/C][C]176082[/C][C]193995.24[/C][C]-17913.24[/C][/ROW]
[ROW][C]20[/C][C]266736[/C][C]290227.304347826[/C][C]-23491.3043478261[/C][/ROW]
[ROW][C]21[/C][C]278265[/C][C]290227.304347826[/C][C]-11962.3043478261[/C][/ROW]
[ROW][C]22[/C][C]442703[/C][C]389863.952380952[/C][C]52839.0476190476[/C][/ROW]
[ROW][C]23[/C][C]180393[/C][C]193995.24[/C][C]-13602.24[/C][/ROW]
[ROW][C]24[/C][C]189897[/C][C]193995.24[/C][C]-4098.23999999999[/C][/ROW]
[ROW][C]25[/C][C]234247[/C][C]389863.952380952[/C][C]-155616.952380952[/C][/ROW]
[ROW][C]26[/C][C]238002[/C][C]290227.304347826[/C][C]-52225.3043478261[/C][/ROW]
[ROW][C]27[/C][C]267268[/C][C]290227.304347826[/C][C]-22959.3043478261[/C][/ROW]
[ROW][C]28[/C][C]270787[/C][C]193995.24[/C][C]76791.76[/C][/ROW]
[ROW][C]29[/C][C]155915[/C][C]193995.24[/C][C]-38080.24[/C][/ROW]
[ROW][C]30[/C][C]342564[/C][C]290227.304347826[/C][C]52336.6956521739[/C][/ROW]
[ROW][C]31[/C][C]282172[/C][C]290227.304347826[/C][C]-8055.30434782611[/C][/ROW]
[ROW][C]32[/C][C]216584[/C][C]193995.24[/C][C]22588.76[/C][/ROW]
[ROW][C]33[/C][C]318669[/C][C]250593.705882353[/C][C]68075.294117647[/C][/ROW]
[ROW][C]34[/C][C]98672[/C][C]90372.6[/C][C]8299.39999999999[/C][/ROW]
[ROW][C]35[/C][C]391593[/C][C]389863.952380952[/C][C]1729.04761904763[/C][/ROW]
[ROW][C]36[/C][C]273950[/C][C]290227.304347826[/C][C]-16277.3043478261[/C][/ROW]
[ROW][C]37[/C][C]425120[/C][C]389863.952380952[/C][C]35256.0476190476[/C][/ROW]
[ROW][C]38[/C][C]227636[/C][C]193995.24[/C][C]33640.76[/C][/ROW]
[ROW][C]39[/C][C]115658[/C][C]90372.6[/C][C]25285.4[/C][/ROW]
[ROW][C]40[/C][C]354670[/C][C]389863.952380952[/C][C]-35193.9523809524[/C][/ROW]
[ROW][C]41[/C][C]324178[/C][C]389863.952380952[/C][C]-65685.9523809524[/C][/ROW]
[ROW][C]42[/C][C]178083[/C][C]193995.24[/C][C]-15912.24[/C][/ROW]
[ROW][C]43[/C][C]195153[/C][C]193995.24[/C][C]1157.76000000001[/C][/ROW]
[ROW][C]44[/C][C]181810[/C][C]193995.24[/C][C]-12185.24[/C][/ROW]
[ROW][C]45[/C][C]153778[/C][C]290227.304347826[/C][C]-136449.304347826[/C][/ROW]
[ROW][C]46[/C][C]455168[/C][C]389863.952380952[/C][C]65304.0476190476[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]90372.6[/C][C]-11572.6[/C][/ROW]
[ROW][C]48[/C][C]208051[/C][C]250593.705882353[/C][C]-42542.705882353[/C][/ROW]
[ROW][C]49[/C][C]348077[/C][C]389863.952380952[/C][C]-41786.9523809524[/C][/ROW]
[ROW][C]50[/C][C]175523[/C][C]193995.24[/C][C]-18472.24[/C][/ROW]
[ROW][C]51[/C][C]224591[/C][C]193995.24[/C][C]30595.76[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]36003.3333333333[/C][C]-11815.3333333333[/C][/ROW]
[ROW][C]53[/C][C]372238[/C][C]290227.304347826[/C][C]82010.6956521739[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]90372.6[/C][C]-25343.6[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]90372.6[/C][C]10724.4[/C][/ROW]
[ROW][C]56[/C][C]279012[/C][C]290227.304347826[/C][C]-11215.3043478261[/C][/ROW]
[ROW][C]57[/C][C]317644[/C][C]290227.304347826[/C][C]27416.6956521739[/C][/ROW]
[ROW][C]58[/C][C]340471[/C][C]290227.304347826[/C][C]50243.6956521739[/C][/ROW]
[ROW][C]59[/C][C]358958[/C][C]290227.304347826[/C][C]68730.6956521739[/C][/ROW]
[ROW][C]60[/C][C]252529[/C][C]250593.705882353[/C][C]1935.29411764705[/C][/ROW]
[ROW][C]61[/C][C]379078[/C][C]389863.952380952[/C][C]-10785.9523809524[/C][/ROW]
[ROW][C]62[/C][C]304468[/C][C]290227.304347826[/C][C]14240.6956521739[/C][/ROW]
[ROW][C]63[/C][C]270190[/C][C]290227.304347826[/C][C]-20037.3043478261[/C][/ROW]
[ROW][C]64[/C][C]264889[/C][C]290227.304347826[/C][C]-25338.3043478261[/C][/ROW]
[ROW][C]65[/C][C]228595[/C][C]193995.24[/C][C]34599.76[/C][/ROW]
[ROW][C]66[/C][C]216027[/C][C]193995.24[/C][C]22031.76[/C][/ROW]
[ROW][C]67[/C][C]198798[/C][C]193995.24[/C][C]4802.76000000001[/C][/ROW]
[ROW][C]68[/C][C]238146[/C][C]290227.304347826[/C][C]-52081.3043478261[/C][/ROW]
[ROW][C]69[/C][C]234891[/C][C]290227.304347826[/C][C]-55336.3043478261[/C][/ROW]
[ROW][C]70[/C][C]175816[/C][C]193995.24[/C][C]-18179.24[/C][/ROW]
[ROW][C]71[/C][C]239314[/C][C]290227.304347826[/C][C]-50913.3043478261[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]90372.6[/C][C]-16806.6[/C][/ROW]
[ROW][C]73[/C][C]242622[/C][C]250593.705882353[/C][C]-7971.70588235295[/C][/ROW]
[ROW][C]74[/C][C]187167[/C][C]193995.24[/C][C]-6828.23999999999[/C][/ROW]
[ROW][C]75[/C][C]209049[/C][C]193995.24[/C][C]15053.76[/C][/ROW]
[ROW][C]76[/C][C]360592[/C][C]290227.304347826[/C][C]70364.6956521739[/C][/ROW]
[ROW][C]77[/C][C]342846[/C][C]290227.304347826[/C][C]52618.6956521739[/C][/ROW]
[ROW][C]78[/C][C]207650[/C][C]193995.24[/C][C]13654.76[/C][/ROW]
[ROW][C]79[/C][C]206500[/C][C]250593.705882353[/C][C]-44093.705882353[/C][/ROW]
[ROW][C]80[/C][C]182357[/C][C]193995.24[/C][C]-11638.24[/C][/ROW]
[ROW][C]81[/C][C]153613[/C][C]193995.24[/C][C]-40382.24[/C][/ROW]
[ROW][C]82[/C][C]456979[/C][C]389863.952380952[/C][C]67115.0476190476[/C][/ROW]
[ROW][C]83[/C][C]145943[/C][C]193995.24[/C][C]-48052.24[/C][/ROW]
[ROW][C]84[/C][C]280366[/C][C]290227.304347826[/C][C]-9861.30434782611[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]90372.6[/C][C]-9419.60000000001[/C][/ROW]
[ROW][C]86[/C][C]150216[/C][C]193995.24[/C][C]-43779.24[/C][/ROW]
[ROW][C]87[/C][C]167878[/C][C]193995.24[/C][C]-26117.24[/C][/ROW]
[ROW][C]88[/C][C]381493[/C][C]389863.952380952[/C][C]-8370.95238095237[/C][/ROW]
[ROW][C]89[/C][C]331734[/C][C]290227.304347826[/C][C]41506.6956521739[/C][/ROW]
[ROW][C]90[/C][C]179797[/C][C]290227.304347826[/C][C]-110430.304347826[/C][/ROW]
[ROW][C]91[/C][C]264350[/C][C]290227.304347826[/C][C]-25877.3043478261[/C][/ROW]
[ROW][C]92[/C][C]262793[/C][C]250593.705882353[/C][C]12199.294117647[/C][/ROW]
[ROW][C]93[/C][C]189142[/C][C]193995.24[/C][C]-4853.23999999999[/C][/ROW]
[ROW][C]94[/C][C]275997[/C][C]290227.304347826[/C][C]-14230.3043478261[/C][/ROW]
[ROW][C]95[/C][C]328875[/C][C]290227.304347826[/C][C]38647.6956521739[/C][/ROW]
[ROW][C]96[/C][C]189252[/C][C]193995.24[/C][C]-4743.23999999999[/C][/ROW]
[ROW][C]97[/C][C]222504[/C][C]193995.24[/C][C]28508.76[/C][/ROW]
[ROW][C]98[/C][C]287386[/C][C]250593.705882353[/C][C]36792.294117647[/C][/ROW]
[ROW][C]99[/C][C]392647[/C][C]389863.952380952[/C][C]2783.04761904763[/C][/ROW]
[ROW][C]100[/C][C]397681[/C][C]389863.952380952[/C][C]7817.04761904763[/C][/ROW]
[ROW][C]101[/C][C]287748[/C][C]290227.304347826[/C][C]-2479.30434782611[/C][/ROW]
[ROW][C]102[/C][C]294320[/C][C]290227.304347826[/C][C]4092.69565217389[/C][/ROW]
[ROW][C]103[/C][C]186856[/C][C]193995.24[/C][C]-7139.23999999999[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]36003.3333333333[/C][C]7283.66666666666[/C][/ROW]
[ROW][C]105[/C][C]185468[/C][C]250593.705882353[/C][C]-65125.705882353[/C][/ROW]
[ROW][C]106[/C][C]236654[/C][C]290227.304347826[/C][C]-53573.3043478261[/C][/ROW]
[ROW][C]107[/C][C]268077[/C][C]290227.304347826[/C][C]-22150.3043478261[/C][/ROW]
[ROW][C]108[/C][C]305195[/C][C]290227.304347826[/C][C]14967.6956521739[/C][/ROW]
[ROW][C]109[/C][C]151344[/C][C]193995.24[/C][C]-42651.24[/C][/ROW]
[ROW][C]110[/C][C]154287[/C][C]290227.304347826[/C][C]-135940.304347826[/C][/ROW]
[ROW][C]111[/C][C]307000[/C][C]193995.24[/C][C]113004.76[/C][/ROW]
[ROW][C]112[/C][C]298039[/C][C]290227.304347826[/C][C]7811.69565217389[/C][/ROW]
[ROW][C]113[/C][C]23623[/C][C]36003.3333333333[/C][C]-12380.3333333333[/C][/ROW]
[ROW][C]114[/C][C]195817[/C][C]250593.705882353[/C][C]-54776.705882353[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]36003.3333333333[/C][C]25853.6666666667[/C][/ROW]
[ROW][C]116[/C][C]163766[/C][C]193995.24[/C][C]-30229.24[/C][/ROW]
[ROW][C]117[/C][C]415339[/C][C]389863.952380952[/C][C]25475.0476190476[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]36003.3333333333[/C][C]-14949.3333333333[/C][/ROW]
[ROW][C]119[/C][C]252805[/C][C]250593.705882353[/C][C]2211.29411764705[/C][/ROW]
[ROW][C]120[/C][C]31961[/C][C]36003.3333333333[/C][C]-4042.33333333334[/C][/ROW]
[ROW][C]121[/C][C]317367[/C][C]389863.952380952[/C][C]-72496.9523809524[/C][/ROW]
[ROW][C]122[/C][C]240153[/C][C]250593.705882353[/C][C]-10440.705882353[/C][/ROW]
[ROW][C]123[/C][C]175083[/C][C]193995.24[/C][C]-18912.24[/C][/ROW]
[ROW][C]124[/C][C]152043[/C][C]193995.24[/C][C]-41952.24[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]36003.3333333333[/C][C]2210.66666666666[/C][/ROW]
[ROW][C]126[/C][C]216299[/C][C]193995.24[/C][C]22303.76[/C][/ROW]
[ROW][C]127[/C][C]357602[/C][C]290227.304347826[/C][C]67374.6956521739[/C][/ROW]
[ROW][C]128[/C][C]198104[/C][C]193995.24[/C][C]4108.76000000001[/C][/ROW]
[ROW][C]129[/C][C]410803[/C][C]389863.952380952[/C][C]20939.0476190476[/C][/ROW]
[ROW][C]130[/C][C]316105[/C][C]290227.304347826[/C][C]25877.6956521739[/C][/ROW]
[ROW][C]131[/C][C]397297[/C][C]290227.304347826[/C][C]107069.695652174[/C][/ROW]
[ROW][C]132[/C][C]187992[/C][C]193995.24[/C][C]-6003.23999999999[/C][/ROW]
[ROW][C]133[/C][C]102424[/C][C]90372.6[/C][C]12051.4[/C][/ROW]
[ROW][C]134[/C][C]286327[/C][C]290227.304347826[/C][C]-3900.30434782611[/C][/ROW]
[ROW][C]135[/C][C]409878[/C][C]389863.952380952[/C][C]20014.0476190476[/C][/ROW]
[ROW][C]136[/C][C]143860[/C][C]193995.24[/C][C]-50135.24[/C][/ROW]
[ROW][C]137[/C][C]391854[/C][C]389863.952380952[/C][C]1990.04761904763[/C][/ROW]
[ROW][C]138[/C][C]157429[/C][C]193995.24[/C][C]-36566.24[/C][/ROW]
[ROW][C]139[/C][C]258751[/C][C]193995.24[/C][C]64755.76[/C][/ROW]
[ROW][C]140[/C][C]282399[/C][C]290227.304347826[/C][C]-7828.30434782611[/C][/ROW]
[ROW][C]141[/C][C]217665[/C][C]193995.24[/C][C]23669.76[/C][/ROW]
[ROW][C]142[/C][C]367246[/C][C]389863.952380952[/C][C]-22617.9523809524[/C][/ROW]
[ROW][C]143[/C][C]244698[/C][C]250593.705882353[/C][C]-5895.70588235295[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]193995.24[/C][C]-20735.24[/C][/ROW]
[ROW][C]145[/C][C]325118[/C][C]290227.304347826[/C][C]34890.6956521739[/C][/ROW]
[ROW][C]146[/C][C]168994[/C][C]193995.24[/C][C]-25001.24[/C][/ROW]
[ROW][C]147[/C][C]253330[/C][C]290227.304347826[/C][C]-36897.3043478261[/C][/ROW]
[ROW][C]148[/C][C]301703[/C][C]290227.304347826[/C][C]11475.6956521739[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]3741.81818181818[/C][C]-3740.81818181818[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]3741.81818181818[/C][C]10946.1818181818[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]3741.81818181818[/C][C]-3643.81818181818[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]3741.81818181818[/C][C]-3286.81818181818[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]3741.81818181818[/C][C]-3741.81818181818[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]3741.81818181818[/C][C]-3741.81818181818[/C][/ROW]
[ROW][C]155[/C][C]246435[/C][C]250593.705882353[/C][C]-4158.70588235295[/C][/ROW]
[ROW][C]156[/C][C]392245[/C][C]290227.304347826[/C][C]102017.695652174[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]3741.81818181818[/C][C]-3741.81818181818[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]3741.81818181818[/C][C]-3538.81818181818[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]3741.81818181818[/C][C]3457.18181818182[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]36003.3333333333[/C][C]10656.6666666667[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]3741.81818181818[/C][C]13805.1818181818[/C][/ROW]
[ROW][C]162[/C][C]116678[/C][C]90372.6[/C][C]26305.4[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]3741.81818181818[/C][C]-2772.81818181818[/C][/ROW]
[ROW][C]164[/C][C]206501[/C][C]193995.24[/C][C]12505.76[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157108&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157108&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	272545	193995.24	78549.76
2	179444	193995.24	-14551.24
3	222373	193995.24	28377.76
4	218443	290227.304347826	-71784.3043478261
5	167843	193995.24	-26152.24
6	70849	90372.6	-19523.6
7	506574	389863.952380952	116710.047619048
8	33186	36003.3333333333	-2817.33333333334
9	216660	193995.24	22664.76
10	213274	193995.24	19278.76
11	307153	250593.705882353	56559.294117647
12	239324	250593.705882353	-11269.705882353
13	166215	193995.24	-27780.24
14	364402	290227.304347826	74174.6956521739
15	244103	250593.705882353	-6490.70588235295
16	384448	389863.952380952	-5415.95238095237
17	325587	250593.705882353	74993.294117647
18	323652	290227.304347826	33424.6956521739
19	176082	193995.24	-17913.24
20	266736	290227.304347826	-23491.3043478261
21	278265	290227.304347826	-11962.3043478261
22	442703	389863.952380952	52839.0476190476
23	180393	193995.24	-13602.24
24	189897	193995.24	-4098.23999999999
25	234247	389863.952380952	-155616.952380952
26	238002	290227.304347826	-52225.3043478261
27	267268	290227.304347826	-22959.3043478261
28	270787	193995.24	76791.76
29	155915	193995.24	-38080.24
30	342564	290227.304347826	52336.6956521739
31	282172	290227.304347826	-8055.30434782611
32	216584	193995.24	22588.76
33	318669	250593.705882353	68075.294117647
34	98672	90372.6	8299.39999999999
35	391593	389863.952380952	1729.04761904763
36	273950	290227.304347826	-16277.3043478261
37	425120	389863.952380952	35256.0476190476
38	227636	193995.24	33640.76
39	115658	90372.6	25285.4
40	354670	389863.952380952	-35193.9523809524
41	324178	389863.952380952	-65685.9523809524
42	178083	193995.24	-15912.24
43	195153	193995.24	1157.76000000001
44	181810	193995.24	-12185.24
45	153778	290227.304347826	-136449.304347826
46	455168	389863.952380952	65304.0476190476
47	78800	90372.6	-11572.6
48	208051	250593.705882353	-42542.705882353
49	348077	389863.952380952	-41786.9523809524
50	175523	193995.24	-18472.24
51	224591	193995.24	30595.76
52	24188	36003.3333333333	-11815.3333333333
53	372238	290227.304347826	82010.6956521739
54	65029	90372.6	-25343.6
55	101097	90372.6	10724.4
56	279012	290227.304347826	-11215.3043478261
57	317644	290227.304347826	27416.6956521739
58	340471	290227.304347826	50243.6956521739
59	358958	290227.304347826	68730.6956521739
60	252529	250593.705882353	1935.29411764705
61	379078	389863.952380952	-10785.9523809524
62	304468	290227.304347826	14240.6956521739
63	270190	290227.304347826	-20037.3043478261
64	264889	290227.304347826	-25338.3043478261
65	228595	193995.24	34599.76
66	216027	193995.24	22031.76
67	198798	193995.24	4802.76000000001
68	238146	290227.304347826	-52081.3043478261
69	234891	290227.304347826	-55336.3043478261
70	175816	193995.24	-18179.24
71	239314	290227.304347826	-50913.3043478261
72	73566	90372.6	-16806.6
73	242622	250593.705882353	-7971.70588235295
74	187167	193995.24	-6828.23999999999
75	209049	193995.24	15053.76
76	360592	290227.304347826	70364.6956521739
77	342846	290227.304347826	52618.6956521739
78	207650	193995.24	13654.76
79	206500	250593.705882353	-44093.705882353
80	182357	193995.24	-11638.24
81	153613	193995.24	-40382.24
82	456979	389863.952380952	67115.0476190476
83	145943	193995.24	-48052.24
84	280366	290227.304347826	-9861.30434782611
85	80953	90372.6	-9419.60000000001
86	150216	193995.24	-43779.24
87	167878	193995.24	-26117.24
88	381493	389863.952380952	-8370.95238095237
89	331734	290227.304347826	41506.6956521739
90	179797	290227.304347826	-110430.304347826
91	264350	290227.304347826	-25877.3043478261
92	262793	250593.705882353	12199.294117647
93	189142	193995.24	-4853.23999999999
94	275997	290227.304347826	-14230.3043478261
95	328875	290227.304347826	38647.6956521739
96	189252	193995.24	-4743.23999999999
97	222504	193995.24	28508.76
98	287386	250593.705882353	36792.294117647
99	392647	389863.952380952	2783.04761904763
100	397681	389863.952380952	7817.04761904763
101	287748	290227.304347826	-2479.30434782611
102	294320	290227.304347826	4092.69565217389
103	186856	193995.24	-7139.23999999999
104	43287	36003.3333333333	7283.66666666666
105	185468	250593.705882353	-65125.705882353
106	236654	290227.304347826	-53573.3043478261
107	268077	290227.304347826	-22150.3043478261
108	305195	290227.304347826	14967.6956521739
109	151344	193995.24	-42651.24
110	154287	290227.304347826	-135940.304347826
111	307000	193995.24	113004.76
112	298039	290227.304347826	7811.69565217389
113	23623	36003.3333333333	-12380.3333333333
114	195817	250593.705882353	-54776.705882353
115	61857	36003.3333333333	25853.6666666667
116	163766	193995.24	-30229.24
117	415339	389863.952380952	25475.0476190476
118	21054	36003.3333333333	-14949.3333333333
119	252805	250593.705882353	2211.29411764705
120	31961	36003.3333333333	-4042.33333333334
121	317367	389863.952380952	-72496.9523809524
122	240153	250593.705882353	-10440.705882353
123	175083	193995.24	-18912.24
124	152043	193995.24	-41952.24
125	38214	36003.3333333333	2210.66666666666
126	216299	193995.24	22303.76
127	357602	290227.304347826	67374.6956521739
128	198104	193995.24	4108.76000000001
129	410803	389863.952380952	20939.0476190476
130	316105	290227.304347826	25877.6956521739
131	397297	290227.304347826	107069.695652174
132	187992	193995.24	-6003.23999999999
133	102424	90372.6	12051.4
134	286327	290227.304347826	-3900.30434782611
135	409878	389863.952380952	20014.0476190476
136	143860	193995.24	-50135.24
137	391854	389863.952380952	1990.04761904763
138	157429	193995.24	-36566.24
139	258751	193995.24	64755.76
140	282399	290227.304347826	-7828.30434782611
141	217665	193995.24	23669.76
142	367246	389863.952380952	-22617.9523809524
143	244698	250593.705882353	-5895.70588235295
144	173260	193995.24	-20735.24
145	325118	290227.304347826	34890.6956521739
146	168994	193995.24	-25001.24
147	253330	290227.304347826	-36897.3043478261
148	301703	290227.304347826	11475.6956521739
149	1	3741.81818181818	-3740.81818181818
150	14688	3741.81818181818	10946.1818181818
151	98	3741.81818181818	-3643.81818181818
152	455	3741.81818181818	-3286.81818181818
153	0	3741.81818181818	-3741.81818181818
154	0	3741.81818181818	-3741.81818181818
155	246435	250593.705882353	-4158.70588235295
156	392245	290227.304347826	102017.695652174
157	0	3741.81818181818	-3741.81818181818
158	203	3741.81818181818	-3538.81818181818
159	7199	3741.81818181818	3457.18181818182
160	46660	36003.3333333333	10656.6666666667
161	17547	3741.81818181818	13805.1818181818
162	116678	90372.6	26305.4
163	969	3741.81818181818	-2772.81818181818
164	206501	193995.24	12505.76

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 = 2 ; par3 = Pearson Chi-Squared ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 5 ; 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