Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Fri, 23 Dec 2011 09:13:57 -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/23/t1324649668g2tecit55v3nb5t.htm/, Retrieved Mon, 29 Apr 2024 18:21:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160430, Retrieved Mon, 29 Apr 2024 18:21:12 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [WS 10 Recursive p...] [2011-12-09 12:55:25] [60c0c94f647e2c90e494ab0f2a2f1926]
-   PD    [Recursive Partitioning (Regression Trees)] [WS 10 Recursive p...] [2011-12-09 13:55:20] [60c0c94f647e2c90e494ab0f2a2f1926]
-   PD        [Recursive Partitioning (Regression Trees)] [] [2011-12-23 14:13:57] [7e9b6bd31a62815918579b1facd0f368] [Current]
-   P           [Recursive Partitioning (Regression Trees)] [] [2011-12-23 14:37:55] [60c0c94f647e2c90e494ab0f2a2f1926] 
-   P             [Recursive Partitioning (Regression Trees)] [] [2011-12-23 14:57:43] [60c0c94f647e2c90e494ab0f2a2f1926] 

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

279055	73	96	130	140824
212408	75	75	143	110459
233939	83	70	118	105079
222117	106	134	146	112098
189911	56	83	73	43929
70849	28	8	89	76173
605767	135	173	146	187326
33186	19	1	22	22807
227332	62	88	132	144408
258874	48	98	92	66485
369815	121	114	147	79089
264989	131	125	203	81625
212638	87	57	113	68788
368577	85	139	171	103297
269455	88	87	87	69446
398124	191	176	208	114948
335567	77	114	153	167949
428322	172	121	97	125081
182016	58	103	95	125818
267365	89	135	197	136588
279428	73	123	160	112431
508849	111	99	148	103037
206722	47	74	84	82317
200004	58	103	227	118906
257139	133	158	154	83515
270941	138	116	151	104581
324969	134	114	142	103129
329962	92	150	148	83243
190867	60	64	110	37110
393860	79	150	149	113344
327660	89	143	179	139165
269239	83	50	149	86652
396136	106	145	187	112302
130446	49	56	153	69652
430118	104	141	163	119442
273950	56	83	127	69867
428077	128	112	151	101629
254312	93	79	100	70168
120351	35	33	46	31081
395658	212	152	156	103925
345875	86	126	128	92622
216827	82	97	111	79011
224524	83	84	119	93487
182485	69	68	148	64520
157164	85	50	65	93473
459455	157	101	134	114360
78800	42	20	66	33032
255072	85	107	201	96125
368086	123	150	177	151911
230299	70	129	156	89256
244782	81	99	158	95676
24188	24	8	7	5950
400109	334	88	175	149695
65029	17	21	61	32551
101097	64	30	41	31701
309810	67	102	133	100087
375638	91	166	228	169707
367127	204	132	140	150491
381998	155	161	155	120192
280106	90	90	141	95893
400971	153	160	181	151715
315924	122	139	75	176225
291391	124	104	97	59900
295075	93	103	142	104767
280018	81	66	136	114799
267432	71	163	87	72128
217181	141	93	140	143592
258166	159	85	169	89626
260919	87	150	129	131072
182961	73	143	92	126817
256967	74	107	160	81351
73566	32	22	67	22618
272362	93	85	179	88977
229056	62	101	90	92059
229851	70	131	144	81897
371391	91	140	144	108146
398210	104	156	144	126372
220419	111	81	134	249771
231884	72	137	146	71154
217714	72	102	121	71571
206169	54	74	112	55918
483074	131	161	145	160141
146100	72	30	99	38692
295224	109	120	96	102812
80953	25	49	27	56622
217384	63	121	77	15986
179344	62	76	137	123534
415550	222	85	151	108535
389059	129	151	126	93879
180679	106	165	159	144551
299505	104	89	101	56750
292260	84	168	144	127654
199481	68	48	102	65594
282361	78	149	135	59938
329281	89	75	147	146975
234577	48	107	155	165904
297995	67	116	138	169265
342490	90	181	113	183500
416463	163	155	248	165986
415683	119	165	116	184923
297080	142	121	176	140358
331792	71	176	140	149959
229772	202	86	59	57224
43287	14	13	64	43750
238089	87	120	40	48029
263322	160	117	98	104978
302082	61	133	139	100046
321797	95	169	135	101047
193926	96	39	97	197426
175138	105	125	142	160902
354041	78	82	155	147172
303273	91	148	115	109432
23668	13	12	0	1168
196743	79	146	103	83248
61857	25	23	30	25162
217543	54	87	130	45724
440711	128	164	102	110529
21054	16	4	0	855
252805	52	81	77	101382
31961	22	18	9	14116
360436	125	118	150	89506
251948	77	76	163	135356
187320	97	55	148	116066
180842	58	62	94	144244
38214	34	16	21	8773
280392	56	98	151	102153
358276	84	137	187	117440
211775	67	50	171	104128
447335	90	152	170	134238
348017	99	163	145	134047
441946	133	142	198	279488
215177	43	80	152	79756
130177	47	59	112	66089
318037	365	94	173	102070
466139	198	128	177	146760
162279	62	63	153	154771
416643	140	127	161	165933
178322	86	60	115	64593
292443	54	118	147	92280
283913	100	110	124	67150
244802	126	45	57	128692
387072	125	96	144	124089
246963	92	128	126	125386
173260	63	41	78	37238
346748	108	146	153	140015
178402	60	147	196	150047
268750	96	121	130	154451
314070	112	185	159	156349
1	0	0	0	0
14688	10	4	0	6023
98	1	0	0	0
455	2	0	0	0
0	0	0	0	0
0	0	0	0	0
291650	94	85	94	84601
415421	168	164	129	68946
0	0	0	0	0
203	4	0	0	0
7199	5	7	0	1644
46660	20	12	13	6179
17547	5	0	4	3926
121550	46	37	89	52789
969	2	0	0	0
242774	75	62	71	100350

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160430&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]3 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=160430&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160430&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	3 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Goodness of Fit
Correlation	0.7761
R-squared	0.6023
RMSE	32803.2887

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7761 \tabularnewline
R-squared & 0.6023 \tabularnewline
RMSE & 32803.2887 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160430&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7761[/C][/ROW]
[ROW][C]R-squared[/C][C]0.6023[/C][/ROW]
[ROW][C]RMSE[/C][C]32803.2887[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160430&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160430&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	0.7761
R-squared	0.6023
RMSE	32803.2887

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	140824	112702.836734694	28121.1632653061
2	110459	112702.836734694	-2243.83673469388
3	105079	80265.5	24813.5
4	112098	112702.836734694	-604.836734693876
5	43929	80265.5	-36336.5
6	76173	80265.5	-4092.5
7	187326	132242.224489796	55083.7755102041
8	22807	26956.1428571429	-4149.14285714286
9	144408	112702.836734694	31705.1632653061
10	66485	80265.5	-13780.5
11	79089	132242.224489796	-53153.2244897959
12	81625	112702.836734694	-31077.8367346939
13	68788	80265.5	-11477.5
14	103297	132242.224489796	-28945.2244897959
15	69446	80265.5	-10819.5
16	114948	132242.224489796	-17294.2244897959
17	167949	132242.224489796	35706.7755102041
18	125081	132242.224489796	-7161.22448979592
19	125818	80265.5	45552.5
20	136588	112702.836734694	23885.1632653061
21	112431	112702.836734694	-271.836734693876
22	103037	132242.224489796	-29205.2244897959
23	82317	80265.5	2051.5
24	118906	112702.836734694	6203.16326530612
25	83515	112702.836734694	-29187.8367346939
26	104581	112702.836734694	-8121.83673469388
27	103129	132242.224489796	-29113.2244897959
28	83243	132242.224489796	-48999.2244897959
29	37110	80265.5	-43155.5
30	113344	132242.224489796	-18898.2244897959
31	139165	132242.224489796	6922.77551020408
32	86652	112702.836734694	-26050.8367346939
33	112302	132242.224489796	-19940.2244897959
34	69652	112702.836734694	-43050.8367346939
35	119442	132242.224489796	-12800.2244897959
36	69867	112702.836734694	-42835.8367346939
37	101629	132242.224489796	-30613.2244897959
38	70168	80265.5	-10097.5
39	31081	63831.7142857143	-32750.7142857143
40	103925	132242.224489796	-28317.2244897959
41	92622	132242.224489796	-39620.2244897959
42	79011	80265.5	-1254.5
43	93487	80265.5	13221.5
44	64520	112702.836734694	-48182.8367346939
45	93473	63831.7142857143	29641.2857142857
46	114360	132242.224489796	-17882.2244897959
47	33032	26956.1428571429	6075.85714285714
48	96125	112702.836734694	-16577.8367346939
49	151911	132242.224489796	19668.7755102041
50	89256	112702.836734694	-23446.8367346939
51	95676	112702.836734694	-17026.8367346939
52	5950	2491.3125	3458.6875
53	149695	132242.224489796	17452.7755102041
54	32551	26956.1428571429	5594.85714285714
55	31701	63831.7142857143	-32130.7142857143
56	100087	112702.836734694	-12615.8367346939
57	169707	132242.224489796	37464.7755102041
58	150491	132242.224489796	18248.7755102041
59	120192	132242.224489796	-12050.2244897959
60	95893	112702.836734694	-16809.8367346939
61	151715	132242.224489796	19472.7755102041
62	176225	132242.224489796	43982.7755102041
63	59900	80265.5	-20365.5
64	104767	112702.836734694	-7935.83673469388
65	114799	112702.836734694	2096.16326530612
66	72128	80265.5	-8137.5
67	143592	112702.836734694	30889.1632653061
68	89626	112702.836734694	-23076.8367346939
69	131072	112702.836734694	18369.1632653061
70	126817	80265.5	46551.5
71	81351	112702.836734694	-31351.8367346939
72	22618	26956.1428571429	-4338.14285714286
73	88977	112702.836734694	-23725.8367346939
74	92059	80265.5	11793.5
75	81897	112702.836734694	-30805.8367346939
76	108146	132242.224489796	-24096.2244897959
77	126372	132242.224489796	-5870.22448979592
78	249771	112702.836734694	137068.163265306
79	71154	112702.836734694	-41548.8367346939
80	71571	80265.5	-8694.5
81	55918	80265.5	-24347.5
82	160141	132242.224489796	27898.7755102041
83	38692	80265.5	-41573.5
84	102812	80265.5	22546.5
85	56622	63831.7142857143	-7209.71428571428
86	15986	80265.5	-64279.5
87	123534	112702.836734694	10831.1632653061
88	108535	132242.224489796	-23707.2244897959
89	93879	132242.224489796	-38363.2244897959
90	144551	112702.836734694	31848.1632653061
91	56750	80265.5	-23515.5
92	127654	112702.836734694	14951.1632653061
93	65594	80265.5	-14671.5
94	59938	112702.836734694	-52764.8367346939
95	146975	132242.224489796	14732.7755102041
96	165904	112702.836734694	53201.1632653061
97	169265	112702.836734694	56562.1632653061
98	183500	132242.224489796	51257.7755102041
99	165986	132242.224489796	33743.7755102041
100	184923	132242.224489796	52680.7755102041
101	140358	112702.836734694	27655.1632653061
102	149959	132242.224489796	17716.7755102041
103	57224	63831.7142857143	-6607.71428571428
104	43750	26956.1428571429	16793.8571428571
105	48029	63831.7142857143	-15802.7142857143
106	104978	80265.5	24712.5
107	100046	112702.836734694	-12656.8367346939
108	101047	132242.224489796	-31195.2244897959
109	197426	80265.5	117160.5
110	160902	112702.836734694	48199.1632653061
111	147172	132242.224489796	14929.7755102041
112	109432	80265.5	29166.5
113	1168	2491.3125	-1323.3125
114	83248	80265.5	2982.5
115	25162	26956.1428571429	-1794.14285714286
116	45724	112702.836734694	-66978.8367346939
117	110529	132242.224489796	-21713.2244897959
118	855	2491.3125	-1636.3125
119	101382	80265.5	21116.5
120	14116	2491.3125	11624.6875
121	89506	132242.224489796	-42736.2244897959
122	135356	112702.836734694	22653.1632653061
123	116066	112702.836734694	3363.16326530612
124	144244	80265.5	63978.5
125	8773	26956.1428571429	-18183.1428571429
126	102153	112702.836734694	-10549.8367346939
127	117440	132242.224489796	-14802.2244897959
128	104128	112702.836734694	-8574.83673469388
129	134238	132242.224489796	1995.77551020408
130	134047	132242.224489796	1804.77551020408
131	279488	132242.224489796	147245.775510204
132	79756	112702.836734694	-32946.8367346939
133	66089	80265.5	-14176.5
134	102070	132242.224489796	-30172.2244897959
135	146760	132242.224489796	14517.7755102041
136	154771	112702.836734694	42068.1632653061
137	165933	132242.224489796	33690.7755102041
138	64593	80265.5	-15672.5
139	92280	112702.836734694	-20422.8367346939
140	67150	80265.5	-13115.5
141	128692	63831.7142857143	64860.2857142857
142	124089	132242.224489796	-8153.22448979592
143	125386	112702.836734694	12683.1632653061
144	37238	80265.5	-43027.5
145	140015	132242.224489796	7772.77551020408
146	150047	112702.836734694	37344.1632653061
147	154451	112702.836734694	41748.1632653061
148	156349	132242.224489796	24106.7755102041
149	0	2491.3125	-2491.3125
150	6023	2491.3125	3531.6875
151	0	2491.3125	-2491.3125
152	0	2491.3125	-2491.3125
153	0	2491.3125	-2491.3125
154	0	2491.3125	-2491.3125
155	84601	80265.5	4335.5
156	68946	132242.224489796	-63296.2244897959
157	0	2491.3125	-2491.3125
158	0	2491.3125	-2491.3125
159	1644	2491.3125	-847.3125
160	6179	2491.3125	3687.6875
161	3926	2491.3125	1434.6875
162	52789	80265.5	-27476.5
163	0	2491.3125	-2491.3125
164	100350	80265.5	20084.5

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 140824 & 112702.836734694 & 28121.1632653061 \tabularnewline
2 & 110459 & 112702.836734694 & -2243.83673469388 \tabularnewline
3 & 105079 & 80265.5 & 24813.5 \tabularnewline
4 & 112098 & 112702.836734694 & -604.836734693876 \tabularnewline
5 & 43929 & 80265.5 & -36336.5 \tabularnewline
6 & 76173 & 80265.5 & -4092.5 \tabularnewline
7 & 187326 & 132242.224489796 & 55083.7755102041 \tabularnewline
8 & 22807 & 26956.1428571429 & -4149.14285714286 \tabularnewline
9 & 144408 & 112702.836734694 & 31705.1632653061 \tabularnewline
10 & 66485 & 80265.5 & -13780.5 \tabularnewline
11 & 79089 & 132242.224489796 & -53153.2244897959 \tabularnewline
12 & 81625 & 112702.836734694 & -31077.8367346939 \tabularnewline
13 & 68788 & 80265.5 & -11477.5 \tabularnewline
14 & 103297 & 132242.224489796 & -28945.2244897959 \tabularnewline
15 & 69446 & 80265.5 & -10819.5 \tabularnewline
16 & 114948 & 132242.224489796 & -17294.2244897959 \tabularnewline
17 & 167949 & 132242.224489796 & 35706.7755102041 \tabularnewline
18 & 125081 & 132242.224489796 & -7161.22448979592 \tabularnewline
19 & 125818 & 80265.5 & 45552.5 \tabularnewline
20 & 136588 & 112702.836734694 & 23885.1632653061 \tabularnewline
21 & 112431 & 112702.836734694 & -271.836734693876 \tabularnewline
22 & 103037 & 132242.224489796 & -29205.2244897959 \tabularnewline
23 & 82317 & 80265.5 & 2051.5 \tabularnewline
24 & 118906 & 112702.836734694 & 6203.16326530612 \tabularnewline
25 & 83515 & 112702.836734694 & -29187.8367346939 \tabularnewline
26 & 104581 & 112702.836734694 & -8121.83673469388 \tabularnewline
27 & 103129 & 132242.224489796 & -29113.2244897959 \tabularnewline
28 & 83243 & 132242.224489796 & -48999.2244897959 \tabularnewline
29 & 37110 & 80265.5 & -43155.5 \tabularnewline
30 & 113344 & 132242.224489796 & -18898.2244897959 \tabularnewline
31 & 139165 & 132242.224489796 & 6922.77551020408 \tabularnewline
32 & 86652 & 112702.836734694 & -26050.8367346939 \tabularnewline
33 & 112302 & 132242.224489796 & -19940.2244897959 \tabularnewline
34 & 69652 & 112702.836734694 & -43050.8367346939 \tabularnewline
35 & 119442 & 132242.224489796 & -12800.2244897959 \tabularnewline
36 & 69867 & 112702.836734694 & -42835.8367346939 \tabularnewline
37 & 101629 & 132242.224489796 & -30613.2244897959 \tabularnewline
38 & 70168 & 80265.5 & -10097.5 \tabularnewline
39 & 31081 & 63831.7142857143 & -32750.7142857143 \tabularnewline
40 & 103925 & 132242.224489796 & -28317.2244897959 \tabularnewline
41 & 92622 & 132242.224489796 & -39620.2244897959 \tabularnewline
42 & 79011 & 80265.5 & -1254.5 \tabularnewline
43 & 93487 & 80265.5 & 13221.5 \tabularnewline
44 & 64520 & 112702.836734694 & -48182.8367346939 \tabularnewline
45 & 93473 & 63831.7142857143 & 29641.2857142857 \tabularnewline
46 & 114360 & 132242.224489796 & -17882.2244897959 \tabularnewline
47 & 33032 & 26956.1428571429 & 6075.85714285714 \tabularnewline
48 & 96125 & 112702.836734694 & -16577.8367346939 \tabularnewline
49 & 151911 & 132242.224489796 & 19668.7755102041 \tabularnewline
50 & 89256 & 112702.836734694 & -23446.8367346939 \tabularnewline
51 & 95676 & 112702.836734694 & -17026.8367346939 \tabularnewline
52 & 5950 & 2491.3125 & 3458.6875 \tabularnewline
53 & 149695 & 132242.224489796 & 17452.7755102041 \tabularnewline
54 & 32551 & 26956.1428571429 & 5594.85714285714 \tabularnewline
55 & 31701 & 63831.7142857143 & -32130.7142857143 \tabularnewline
56 & 100087 & 112702.836734694 & -12615.8367346939 \tabularnewline
57 & 169707 & 132242.224489796 & 37464.7755102041 \tabularnewline
58 & 150491 & 132242.224489796 & 18248.7755102041 \tabularnewline
59 & 120192 & 132242.224489796 & -12050.2244897959 \tabularnewline
60 & 95893 & 112702.836734694 & -16809.8367346939 \tabularnewline
61 & 151715 & 132242.224489796 & 19472.7755102041 \tabularnewline
62 & 176225 & 132242.224489796 & 43982.7755102041 \tabularnewline
63 & 59900 & 80265.5 & -20365.5 \tabularnewline
64 & 104767 & 112702.836734694 & -7935.83673469388 \tabularnewline
65 & 114799 & 112702.836734694 & 2096.16326530612 \tabularnewline
66 & 72128 & 80265.5 & -8137.5 \tabularnewline
67 & 143592 & 112702.836734694 & 30889.1632653061 \tabularnewline
68 & 89626 & 112702.836734694 & -23076.8367346939 \tabularnewline
69 & 131072 & 112702.836734694 & 18369.1632653061 \tabularnewline
70 & 126817 & 80265.5 & 46551.5 \tabularnewline
71 & 81351 & 112702.836734694 & -31351.8367346939 \tabularnewline
72 & 22618 & 26956.1428571429 & -4338.14285714286 \tabularnewline
73 & 88977 & 112702.836734694 & -23725.8367346939 \tabularnewline
74 & 92059 & 80265.5 & 11793.5 \tabularnewline
75 & 81897 & 112702.836734694 & -30805.8367346939 \tabularnewline
76 & 108146 & 132242.224489796 & -24096.2244897959 \tabularnewline
77 & 126372 & 132242.224489796 & -5870.22448979592 \tabularnewline
78 & 249771 & 112702.836734694 & 137068.163265306 \tabularnewline
79 & 71154 & 112702.836734694 & -41548.8367346939 \tabularnewline
80 & 71571 & 80265.5 & -8694.5 \tabularnewline
81 & 55918 & 80265.5 & -24347.5 \tabularnewline
82 & 160141 & 132242.224489796 & 27898.7755102041 \tabularnewline
83 & 38692 & 80265.5 & -41573.5 \tabularnewline
84 & 102812 & 80265.5 & 22546.5 \tabularnewline
85 & 56622 & 63831.7142857143 & -7209.71428571428 \tabularnewline
86 & 15986 & 80265.5 & -64279.5 \tabularnewline
87 & 123534 & 112702.836734694 & 10831.1632653061 \tabularnewline
88 & 108535 & 132242.224489796 & -23707.2244897959 \tabularnewline
89 & 93879 & 132242.224489796 & -38363.2244897959 \tabularnewline
90 & 144551 & 112702.836734694 & 31848.1632653061 \tabularnewline
91 & 56750 & 80265.5 & -23515.5 \tabularnewline
92 & 127654 & 112702.836734694 & 14951.1632653061 \tabularnewline
93 & 65594 & 80265.5 & -14671.5 \tabularnewline
94 & 59938 & 112702.836734694 & -52764.8367346939 \tabularnewline
95 & 146975 & 132242.224489796 & 14732.7755102041 \tabularnewline
96 & 165904 & 112702.836734694 & 53201.1632653061 \tabularnewline
97 & 169265 & 112702.836734694 & 56562.1632653061 \tabularnewline
98 & 183500 & 132242.224489796 & 51257.7755102041 \tabularnewline
99 & 165986 & 132242.224489796 & 33743.7755102041 \tabularnewline
100 & 184923 & 132242.224489796 & 52680.7755102041 \tabularnewline
101 & 140358 & 112702.836734694 & 27655.1632653061 \tabularnewline
102 & 149959 & 132242.224489796 & 17716.7755102041 \tabularnewline
103 & 57224 & 63831.7142857143 & -6607.71428571428 \tabularnewline
104 & 43750 & 26956.1428571429 & 16793.8571428571 \tabularnewline
105 & 48029 & 63831.7142857143 & -15802.7142857143 \tabularnewline
106 & 104978 & 80265.5 & 24712.5 \tabularnewline
107 & 100046 & 112702.836734694 & -12656.8367346939 \tabularnewline
108 & 101047 & 132242.224489796 & -31195.2244897959 \tabularnewline
109 & 197426 & 80265.5 & 117160.5 \tabularnewline
110 & 160902 & 112702.836734694 & 48199.1632653061 \tabularnewline
111 & 147172 & 132242.224489796 & 14929.7755102041 \tabularnewline
112 & 109432 & 80265.5 & 29166.5 \tabularnewline
113 & 1168 & 2491.3125 & -1323.3125 \tabularnewline
114 & 83248 & 80265.5 & 2982.5 \tabularnewline
115 & 25162 & 26956.1428571429 & -1794.14285714286 \tabularnewline
116 & 45724 & 112702.836734694 & -66978.8367346939 \tabularnewline
117 & 110529 & 132242.224489796 & -21713.2244897959 \tabularnewline
118 & 855 & 2491.3125 & -1636.3125 \tabularnewline
119 & 101382 & 80265.5 & 21116.5 \tabularnewline
120 & 14116 & 2491.3125 & 11624.6875 \tabularnewline
121 & 89506 & 132242.224489796 & -42736.2244897959 \tabularnewline
122 & 135356 & 112702.836734694 & 22653.1632653061 \tabularnewline
123 & 116066 & 112702.836734694 & 3363.16326530612 \tabularnewline
124 & 144244 & 80265.5 & 63978.5 \tabularnewline
125 & 8773 & 26956.1428571429 & -18183.1428571429 \tabularnewline
126 & 102153 & 112702.836734694 & -10549.8367346939 \tabularnewline
127 & 117440 & 132242.224489796 & -14802.2244897959 \tabularnewline
128 & 104128 & 112702.836734694 & -8574.83673469388 \tabularnewline
129 & 134238 & 132242.224489796 & 1995.77551020408 \tabularnewline
130 & 134047 & 132242.224489796 & 1804.77551020408 \tabularnewline
131 & 279488 & 132242.224489796 & 147245.775510204 \tabularnewline
132 & 79756 & 112702.836734694 & -32946.8367346939 \tabularnewline
133 & 66089 & 80265.5 & -14176.5 \tabularnewline
134 & 102070 & 132242.224489796 & -30172.2244897959 \tabularnewline
135 & 146760 & 132242.224489796 & 14517.7755102041 \tabularnewline
136 & 154771 & 112702.836734694 & 42068.1632653061 \tabularnewline
137 & 165933 & 132242.224489796 & 33690.7755102041 \tabularnewline
138 & 64593 & 80265.5 & -15672.5 \tabularnewline
139 & 92280 & 112702.836734694 & -20422.8367346939 \tabularnewline
140 & 67150 & 80265.5 & -13115.5 \tabularnewline
141 & 128692 & 63831.7142857143 & 64860.2857142857 \tabularnewline
142 & 124089 & 132242.224489796 & -8153.22448979592 \tabularnewline
143 & 125386 & 112702.836734694 & 12683.1632653061 \tabularnewline
144 & 37238 & 80265.5 & -43027.5 \tabularnewline
145 & 140015 & 132242.224489796 & 7772.77551020408 \tabularnewline
146 & 150047 & 112702.836734694 & 37344.1632653061 \tabularnewline
147 & 154451 & 112702.836734694 & 41748.1632653061 \tabularnewline
148 & 156349 & 132242.224489796 & 24106.7755102041 \tabularnewline
149 & 0 & 2491.3125 & -2491.3125 \tabularnewline
150 & 6023 & 2491.3125 & 3531.6875 \tabularnewline
151 & 0 & 2491.3125 & -2491.3125 \tabularnewline
152 & 0 & 2491.3125 & -2491.3125 \tabularnewline
153 & 0 & 2491.3125 & -2491.3125 \tabularnewline
154 & 0 & 2491.3125 & -2491.3125 \tabularnewline
155 & 84601 & 80265.5 & 4335.5 \tabularnewline
156 & 68946 & 132242.224489796 & -63296.2244897959 \tabularnewline
157 & 0 & 2491.3125 & -2491.3125 \tabularnewline
158 & 0 & 2491.3125 & -2491.3125 \tabularnewline
159 & 1644 & 2491.3125 & -847.3125 \tabularnewline
160 & 6179 & 2491.3125 & 3687.6875 \tabularnewline
161 & 3926 & 2491.3125 & 1434.6875 \tabularnewline
162 & 52789 & 80265.5 & -27476.5 \tabularnewline
163 & 0 & 2491.3125 & -2491.3125 \tabularnewline
164 & 100350 & 80265.5 & 20084.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160430&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]140824[/C][C]112702.836734694[/C][C]28121.1632653061[/C][/ROW]
[ROW][C]2[/C][C]110459[/C][C]112702.836734694[/C][C]-2243.83673469388[/C][/ROW]
[ROW][C]3[/C][C]105079[/C][C]80265.5[/C][C]24813.5[/C][/ROW]
[ROW][C]4[/C][C]112098[/C][C]112702.836734694[/C][C]-604.836734693876[/C][/ROW]
[ROW][C]5[/C][C]43929[/C][C]80265.5[/C][C]-36336.5[/C][/ROW]
[ROW][C]6[/C][C]76173[/C][C]80265.5[/C][C]-4092.5[/C][/ROW]
[ROW][C]7[/C][C]187326[/C][C]132242.224489796[/C][C]55083.7755102041[/C][/ROW]
[ROW][C]8[/C][C]22807[/C][C]26956.1428571429[/C][C]-4149.14285714286[/C][/ROW]
[ROW][C]9[/C][C]144408[/C][C]112702.836734694[/C][C]31705.1632653061[/C][/ROW]
[ROW][C]10[/C][C]66485[/C][C]80265.5[/C][C]-13780.5[/C][/ROW]
[ROW][C]11[/C][C]79089[/C][C]132242.224489796[/C][C]-53153.2244897959[/C][/ROW]
[ROW][C]12[/C][C]81625[/C][C]112702.836734694[/C][C]-31077.8367346939[/C][/ROW]
[ROW][C]13[/C][C]68788[/C][C]80265.5[/C][C]-11477.5[/C][/ROW]
[ROW][C]14[/C][C]103297[/C][C]132242.224489796[/C][C]-28945.2244897959[/C][/ROW]
[ROW][C]15[/C][C]69446[/C][C]80265.5[/C][C]-10819.5[/C][/ROW]
[ROW][C]16[/C][C]114948[/C][C]132242.224489796[/C][C]-17294.2244897959[/C][/ROW]
[ROW][C]17[/C][C]167949[/C][C]132242.224489796[/C][C]35706.7755102041[/C][/ROW]
[ROW][C]18[/C][C]125081[/C][C]132242.224489796[/C][C]-7161.22448979592[/C][/ROW]
[ROW][C]19[/C][C]125818[/C][C]80265.5[/C][C]45552.5[/C][/ROW]
[ROW][C]20[/C][C]136588[/C][C]112702.836734694[/C][C]23885.1632653061[/C][/ROW]
[ROW][C]21[/C][C]112431[/C][C]112702.836734694[/C][C]-271.836734693876[/C][/ROW]
[ROW][C]22[/C][C]103037[/C][C]132242.224489796[/C][C]-29205.2244897959[/C][/ROW]
[ROW][C]23[/C][C]82317[/C][C]80265.5[/C][C]2051.5[/C][/ROW]
[ROW][C]24[/C][C]118906[/C][C]112702.836734694[/C][C]6203.16326530612[/C][/ROW]
[ROW][C]25[/C][C]83515[/C][C]112702.836734694[/C][C]-29187.8367346939[/C][/ROW]
[ROW][C]26[/C][C]104581[/C][C]112702.836734694[/C][C]-8121.83673469388[/C][/ROW]
[ROW][C]27[/C][C]103129[/C][C]132242.224489796[/C][C]-29113.2244897959[/C][/ROW]
[ROW][C]28[/C][C]83243[/C][C]132242.224489796[/C][C]-48999.2244897959[/C][/ROW]
[ROW][C]29[/C][C]37110[/C][C]80265.5[/C][C]-43155.5[/C][/ROW]
[ROW][C]30[/C][C]113344[/C][C]132242.224489796[/C][C]-18898.2244897959[/C][/ROW]
[ROW][C]31[/C][C]139165[/C][C]132242.224489796[/C][C]6922.77551020408[/C][/ROW]
[ROW][C]32[/C][C]86652[/C][C]112702.836734694[/C][C]-26050.8367346939[/C][/ROW]
[ROW][C]33[/C][C]112302[/C][C]132242.224489796[/C][C]-19940.2244897959[/C][/ROW]
[ROW][C]34[/C][C]69652[/C][C]112702.836734694[/C][C]-43050.8367346939[/C][/ROW]
[ROW][C]35[/C][C]119442[/C][C]132242.224489796[/C][C]-12800.2244897959[/C][/ROW]
[ROW][C]36[/C][C]69867[/C][C]112702.836734694[/C][C]-42835.8367346939[/C][/ROW]
[ROW][C]37[/C][C]101629[/C][C]132242.224489796[/C][C]-30613.2244897959[/C][/ROW]
[ROW][C]38[/C][C]70168[/C][C]80265.5[/C][C]-10097.5[/C][/ROW]
[ROW][C]39[/C][C]31081[/C][C]63831.7142857143[/C][C]-32750.7142857143[/C][/ROW]
[ROW][C]40[/C][C]103925[/C][C]132242.224489796[/C][C]-28317.2244897959[/C][/ROW]
[ROW][C]41[/C][C]92622[/C][C]132242.224489796[/C][C]-39620.2244897959[/C][/ROW]
[ROW][C]42[/C][C]79011[/C][C]80265.5[/C][C]-1254.5[/C][/ROW]
[ROW][C]43[/C][C]93487[/C][C]80265.5[/C][C]13221.5[/C][/ROW]
[ROW][C]44[/C][C]64520[/C][C]112702.836734694[/C][C]-48182.8367346939[/C][/ROW]
[ROW][C]45[/C][C]93473[/C][C]63831.7142857143[/C][C]29641.2857142857[/C][/ROW]
[ROW][C]46[/C][C]114360[/C][C]132242.224489796[/C][C]-17882.2244897959[/C][/ROW]
[ROW][C]47[/C][C]33032[/C][C]26956.1428571429[/C][C]6075.85714285714[/C][/ROW]
[ROW][C]48[/C][C]96125[/C][C]112702.836734694[/C][C]-16577.8367346939[/C][/ROW]
[ROW][C]49[/C][C]151911[/C][C]132242.224489796[/C][C]19668.7755102041[/C][/ROW]
[ROW][C]50[/C][C]89256[/C][C]112702.836734694[/C][C]-23446.8367346939[/C][/ROW]
[ROW][C]51[/C][C]95676[/C][C]112702.836734694[/C][C]-17026.8367346939[/C][/ROW]
[ROW][C]52[/C][C]5950[/C][C]2491.3125[/C][C]3458.6875[/C][/ROW]
[ROW][C]53[/C][C]149695[/C][C]132242.224489796[/C][C]17452.7755102041[/C][/ROW]
[ROW][C]54[/C][C]32551[/C][C]26956.1428571429[/C][C]5594.85714285714[/C][/ROW]
[ROW][C]55[/C][C]31701[/C][C]63831.7142857143[/C][C]-32130.7142857143[/C][/ROW]
[ROW][C]56[/C][C]100087[/C][C]112702.836734694[/C][C]-12615.8367346939[/C][/ROW]
[ROW][C]57[/C][C]169707[/C][C]132242.224489796[/C][C]37464.7755102041[/C][/ROW]
[ROW][C]58[/C][C]150491[/C][C]132242.224489796[/C][C]18248.7755102041[/C][/ROW]
[ROW][C]59[/C][C]120192[/C][C]132242.224489796[/C][C]-12050.2244897959[/C][/ROW]
[ROW][C]60[/C][C]95893[/C][C]112702.836734694[/C][C]-16809.8367346939[/C][/ROW]
[ROW][C]61[/C][C]151715[/C][C]132242.224489796[/C][C]19472.7755102041[/C][/ROW]
[ROW][C]62[/C][C]176225[/C][C]132242.224489796[/C][C]43982.7755102041[/C][/ROW]
[ROW][C]63[/C][C]59900[/C][C]80265.5[/C][C]-20365.5[/C][/ROW]
[ROW][C]64[/C][C]104767[/C][C]112702.836734694[/C][C]-7935.83673469388[/C][/ROW]
[ROW][C]65[/C][C]114799[/C][C]112702.836734694[/C][C]2096.16326530612[/C][/ROW]
[ROW][C]66[/C][C]72128[/C][C]80265.5[/C][C]-8137.5[/C][/ROW]
[ROW][C]67[/C][C]143592[/C][C]112702.836734694[/C][C]30889.1632653061[/C][/ROW]
[ROW][C]68[/C][C]89626[/C][C]112702.836734694[/C][C]-23076.8367346939[/C][/ROW]
[ROW][C]69[/C][C]131072[/C][C]112702.836734694[/C][C]18369.1632653061[/C][/ROW]
[ROW][C]70[/C][C]126817[/C][C]80265.5[/C][C]46551.5[/C][/ROW]
[ROW][C]71[/C][C]81351[/C][C]112702.836734694[/C][C]-31351.8367346939[/C][/ROW]
[ROW][C]72[/C][C]22618[/C][C]26956.1428571429[/C][C]-4338.14285714286[/C][/ROW]
[ROW][C]73[/C][C]88977[/C][C]112702.836734694[/C][C]-23725.8367346939[/C][/ROW]
[ROW][C]74[/C][C]92059[/C][C]80265.5[/C][C]11793.5[/C][/ROW]
[ROW][C]75[/C][C]81897[/C][C]112702.836734694[/C][C]-30805.8367346939[/C][/ROW]
[ROW][C]76[/C][C]108146[/C][C]132242.224489796[/C][C]-24096.2244897959[/C][/ROW]
[ROW][C]77[/C][C]126372[/C][C]132242.224489796[/C][C]-5870.22448979592[/C][/ROW]
[ROW][C]78[/C][C]249771[/C][C]112702.836734694[/C][C]137068.163265306[/C][/ROW]
[ROW][C]79[/C][C]71154[/C][C]112702.836734694[/C][C]-41548.8367346939[/C][/ROW]
[ROW][C]80[/C][C]71571[/C][C]80265.5[/C][C]-8694.5[/C][/ROW]
[ROW][C]81[/C][C]55918[/C][C]80265.5[/C][C]-24347.5[/C][/ROW]
[ROW][C]82[/C][C]160141[/C][C]132242.224489796[/C][C]27898.7755102041[/C][/ROW]
[ROW][C]83[/C][C]38692[/C][C]80265.5[/C][C]-41573.5[/C][/ROW]
[ROW][C]84[/C][C]102812[/C][C]80265.5[/C][C]22546.5[/C][/ROW]
[ROW][C]85[/C][C]56622[/C][C]63831.7142857143[/C][C]-7209.71428571428[/C][/ROW]
[ROW][C]86[/C][C]15986[/C][C]80265.5[/C][C]-64279.5[/C][/ROW]
[ROW][C]87[/C][C]123534[/C][C]112702.836734694[/C][C]10831.1632653061[/C][/ROW]
[ROW][C]88[/C][C]108535[/C][C]132242.224489796[/C][C]-23707.2244897959[/C][/ROW]
[ROW][C]89[/C][C]93879[/C][C]132242.224489796[/C][C]-38363.2244897959[/C][/ROW]
[ROW][C]90[/C][C]144551[/C][C]112702.836734694[/C][C]31848.1632653061[/C][/ROW]
[ROW][C]91[/C][C]56750[/C][C]80265.5[/C][C]-23515.5[/C][/ROW]
[ROW][C]92[/C][C]127654[/C][C]112702.836734694[/C][C]14951.1632653061[/C][/ROW]
[ROW][C]93[/C][C]65594[/C][C]80265.5[/C][C]-14671.5[/C][/ROW]
[ROW][C]94[/C][C]59938[/C][C]112702.836734694[/C][C]-52764.8367346939[/C][/ROW]
[ROW][C]95[/C][C]146975[/C][C]132242.224489796[/C][C]14732.7755102041[/C][/ROW]
[ROW][C]96[/C][C]165904[/C][C]112702.836734694[/C][C]53201.1632653061[/C][/ROW]
[ROW][C]97[/C][C]169265[/C][C]112702.836734694[/C][C]56562.1632653061[/C][/ROW]
[ROW][C]98[/C][C]183500[/C][C]132242.224489796[/C][C]51257.7755102041[/C][/ROW]
[ROW][C]99[/C][C]165986[/C][C]132242.224489796[/C][C]33743.7755102041[/C][/ROW]
[ROW][C]100[/C][C]184923[/C][C]132242.224489796[/C][C]52680.7755102041[/C][/ROW]
[ROW][C]101[/C][C]140358[/C][C]112702.836734694[/C][C]27655.1632653061[/C][/ROW]
[ROW][C]102[/C][C]149959[/C][C]132242.224489796[/C][C]17716.7755102041[/C][/ROW]
[ROW][C]103[/C][C]57224[/C][C]63831.7142857143[/C][C]-6607.71428571428[/C][/ROW]
[ROW][C]104[/C][C]43750[/C][C]26956.1428571429[/C][C]16793.8571428571[/C][/ROW]
[ROW][C]105[/C][C]48029[/C][C]63831.7142857143[/C][C]-15802.7142857143[/C][/ROW]
[ROW][C]106[/C][C]104978[/C][C]80265.5[/C][C]24712.5[/C][/ROW]
[ROW][C]107[/C][C]100046[/C][C]112702.836734694[/C][C]-12656.8367346939[/C][/ROW]
[ROW][C]108[/C][C]101047[/C][C]132242.224489796[/C][C]-31195.2244897959[/C][/ROW]
[ROW][C]109[/C][C]197426[/C][C]80265.5[/C][C]117160.5[/C][/ROW]
[ROW][C]110[/C][C]160902[/C][C]112702.836734694[/C][C]48199.1632653061[/C][/ROW]
[ROW][C]111[/C][C]147172[/C][C]132242.224489796[/C][C]14929.7755102041[/C][/ROW]
[ROW][C]112[/C][C]109432[/C][C]80265.5[/C][C]29166.5[/C][/ROW]
[ROW][C]113[/C][C]1168[/C][C]2491.3125[/C][C]-1323.3125[/C][/ROW]
[ROW][C]114[/C][C]83248[/C][C]80265.5[/C][C]2982.5[/C][/ROW]
[ROW][C]115[/C][C]25162[/C][C]26956.1428571429[/C][C]-1794.14285714286[/C][/ROW]
[ROW][C]116[/C][C]45724[/C][C]112702.836734694[/C][C]-66978.8367346939[/C][/ROW]
[ROW][C]117[/C][C]110529[/C][C]132242.224489796[/C][C]-21713.2244897959[/C][/ROW]
[ROW][C]118[/C][C]855[/C][C]2491.3125[/C][C]-1636.3125[/C][/ROW]
[ROW][C]119[/C][C]101382[/C][C]80265.5[/C][C]21116.5[/C][/ROW]
[ROW][C]120[/C][C]14116[/C][C]2491.3125[/C][C]11624.6875[/C][/ROW]
[ROW][C]121[/C][C]89506[/C][C]132242.224489796[/C][C]-42736.2244897959[/C][/ROW]
[ROW][C]122[/C][C]135356[/C][C]112702.836734694[/C][C]22653.1632653061[/C][/ROW]
[ROW][C]123[/C][C]116066[/C][C]112702.836734694[/C][C]3363.16326530612[/C][/ROW]
[ROW][C]124[/C][C]144244[/C][C]80265.5[/C][C]63978.5[/C][/ROW]
[ROW][C]125[/C][C]8773[/C][C]26956.1428571429[/C][C]-18183.1428571429[/C][/ROW]
[ROW][C]126[/C][C]102153[/C][C]112702.836734694[/C][C]-10549.8367346939[/C][/ROW]
[ROW][C]127[/C][C]117440[/C][C]132242.224489796[/C][C]-14802.2244897959[/C][/ROW]
[ROW][C]128[/C][C]104128[/C][C]112702.836734694[/C][C]-8574.83673469388[/C][/ROW]
[ROW][C]129[/C][C]134238[/C][C]132242.224489796[/C][C]1995.77551020408[/C][/ROW]
[ROW][C]130[/C][C]134047[/C][C]132242.224489796[/C][C]1804.77551020408[/C][/ROW]
[ROW][C]131[/C][C]279488[/C][C]132242.224489796[/C][C]147245.775510204[/C][/ROW]
[ROW][C]132[/C][C]79756[/C][C]112702.836734694[/C][C]-32946.8367346939[/C][/ROW]
[ROW][C]133[/C][C]66089[/C][C]80265.5[/C][C]-14176.5[/C][/ROW]
[ROW][C]134[/C][C]102070[/C][C]132242.224489796[/C][C]-30172.2244897959[/C][/ROW]
[ROW][C]135[/C][C]146760[/C][C]132242.224489796[/C][C]14517.7755102041[/C][/ROW]
[ROW][C]136[/C][C]154771[/C][C]112702.836734694[/C][C]42068.1632653061[/C][/ROW]
[ROW][C]137[/C][C]165933[/C][C]132242.224489796[/C][C]33690.7755102041[/C][/ROW]
[ROW][C]138[/C][C]64593[/C][C]80265.5[/C][C]-15672.5[/C][/ROW]
[ROW][C]139[/C][C]92280[/C][C]112702.836734694[/C][C]-20422.8367346939[/C][/ROW]
[ROW][C]140[/C][C]67150[/C][C]80265.5[/C][C]-13115.5[/C][/ROW]
[ROW][C]141[/C][C]128692[/C][C]63831.7142857143[/C][C]64860.2857142857[/C][/ROW]
[ROW][C]142[/C][C]124089[/C][C]132242.224489796[/C][C]-8153.22448979592[/C][/ROW]
[ROW][C]143[/C][C]125386[/C][C]112702.836734694[/C][C]12683.1632653061[/C][/ROW]
[ROW][C]144[/C][C]37238[/C][C]80265.5[/C][C]-43027.5[/C][/ROW]
[ROW][C]145[/C][C]140015[/C][C]132242.224489796[/C][C]7772.77551020408[/C][/ROW]
[ROW][C]146[/C][C]150047[/C][C]112702.836734694[/C][C]37344.1632653061[/C][/ROW]
[ROW][C]147[/C][C]154451[/C][C]112702.836734694[/C][C]41748.1632653061[/C][/ROW]
[ROW][C]148[/C][C]156349[/C][C]132242.224489796[/C][C]24106.7755102041[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]2491.3125[/C][C]-2491.3125[/C][/ROW]
[ROW][C]150[/C][C]6023[/C][C]2491.3125[/C][C]3531.6875[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]2491.3125[/C][C]-2491.3125[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]2491.3125[/C][C]-2491.3125[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]2491.3125[/C][C]-2491.3125[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]2491.3125[/C][C]-2491.3125[/C][/ROW]
[ROW][C]155[/C][C]84601[/C][C]80265.5[/C][C]4335.5[/C][/ROW]
[ROW][C]156[/C][C]68946[/C][C]132242.224489796[/C][C]-63296.2244897959[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]2491.3125[/C][C]-2491.3125[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]2491.3125[/C][C]-2491.3125[/C][/ROW]
[ROW][C]159[/C][C]1644[/C][C]2491.3125[/C][C]-847.3125[/C][/ROW]
[ROW][C]160[/C][C]6179[/C][C]2491.3125[/C][C]3687.6875[/C][/ROW]
[ROW][C]161[/C][C]3926[/C][C]2491.3125[/C][C]1434.6875[/C][/ROW]
[ROW][C]162[/C][C]52789[/C][C]80265.5[/C][C]-27476.5[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]2491.3125[/C][C]-2491.3125[/C][/ROW]
[ROW][C]164[/C][C]100350[/C][C]80265.5[/C][C]20084.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160430&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160430&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	140824	112702.836734694	28121.1632653061
2	110459	112702.836734694	-2243.83673469388
3	105079	80265.5	24813.5
4	112098	112702.836734694	-604.836734693876
5	43929	80265.5	-36336.5
6	76173	80265.5	-4092.5
7	187326	132242.224489796	55083.7755102041
8	22807	26956.1428571429	-4149.14285714286
9	144408	112702.836734694	31705.1632653061
10	66485	80265.5	-13780.5
11	79089	132242.224489796	-53153.2244897959
12	81625	112702.836734694	-31077.8367346939
13	68788	80265.5	-11477.5
14	103297	132242.224489796	-28945.2244897959
15	69446	80265.5	-10819.5
16	114948	132242.224489796	-17294.2244897959
17	167949	132242.224489796	35706.7755102041
18	125081	132242.224489796	-7161.22448979592
19	125818	80265.5	45552.5
20	136588	112702.836734694	23885.1632653061
21	112431	112702.836734694	-271.836734693876
22	103037	132242.224489796	-29205.2244897959
23	82317	80265.5	2051.5
24	118906	112702.836734694	6203.16326530612
25	83515	112702.836734694	-29187.8367346939
26	104581	112702.836734694	-8121.83673469388
27	103129	132242.224489796	-29113.2244897959
28	83243	132242.224489796	-48999.2244897959
29	37110	80265.5	-43155.5
30	113344	132242.224489796	-18898.2244897959
31	139165	132242.224489796	6922.77551020408
32	86652	112702.836734694	-26050.8367346939
33	112302	132242.224489796	-19940.2244897959
34	69652	112702.836734694	-43050.8367346939
35	119442	132242.224489796	-12800.2244897959
36	69867	112702.836734694	-42835.8367346939
37	101629	132242.224489796	-30613.2244897959
38	70168	80265.5	-10097.5
39	31081	63831.7142857143	-32750.7142857143
40	103925	132242.224489796	-28317.2244897959
41	92622	132242.224489796	-39620.2244897959
42	79011	80265.5	-1254.5
43	93487	80265.5	13221.5
44	64520	112702.836734694	-48182.8367346939
45	93473	63831.7142857143	29641.2857142857
46	114360	132242.224489796	-17882.2244897959
47	33032	26956.1428571429	6075.85714285714
48	96125	112702.836734694	-16577.8367346939
49	151911	132242.224489796	19668.7755102041
50	89256	112702.836734694	-23446.8367346939
51	95676	112702.836734694	-17026.8367346939
52	5950	2491.3125	3458.6875
53	149695	132242.224489796	17452.7755102041
54	32551	26956.1428571429	5594.85714285714
55	31701	63831.7142857143	-32130.7142857143
56	100087	112702.836734694	-12615.8367346939
57	169707	132242.224489796	37464.7755102041
58	150491	132242.224489796	18248.7755102041
59	120192	132242.224489796	-12050.2244897959
60	95893	112702.836734694	-16809.8367346939
61	151715	132242.224489796	19472.7755102041
62	176225	132242.224489796	43982.7755102041
63	59900	80265.5	-20365.5
64	104767	112702.836734694	-7935.83673469388
65	114799	112702.836734694	2096.16326530612
66	72128	80265.5	-8137.5
67	143592	112702.836734694	30889.1632653061
68	89626	112702.836734694	-23076.8367346939
69	131072	112702.836734694	18369.1632653061
70	126817	80265.5	46551.5
71	81351	112702.836734694	-31351.8367346939
72	22618	26956.1428571429	-4338.14285714286
73	88977	112702.836734694	-23725.8367346939
74	92059	80265.5	11793.5
75	81897	112702.836734694	-30805.8367346939
76	108146	132242.224489796	-24096.2244897959
77	126372	132242.224489796	-5870.22448979592
78	249771	112702.836734694	137068.163265306
79	71154	112702.836734694	-41548.8367346939
80	71571	80265.5	-8694.5
81	55918	80265.5	-24347.5
82	160141	132242.224489796	27898.7755102041
83	38692	80265.5	-41573.5
84	102812	80265.5	22546.5
85	56622	63831.7142857143	-7209.71428571428
86	15986	80265.5	-64279.5
87	123534	112702.836734694	10831.1632653061
88	108535	132242.224489796	-23707.2244897959
89	93879	132242.224489796	-38363.2244897959
90	144551	112702.836734694	31848.1632653061
91	56750	80265.5	-23515.5
92	127654	112702.836734694	14951.1632653061
93	65594	80265.5	-14671.5
94	59938	112702.836734694	-52764.8367346939
95	146975	132242.224489796	14732.7755102041
96	165904	112702.836734694	53201.1632653061
97	169265	112702.836734694	56562.1632653061
98	183500	132242.224489796	51257.7755102041
99	165986	132242.224489796	33743.7755102041
100	184923	132242.224489796	52680.7755102041
101	140358	112702.836734694	27655.1632653061
102	149959	132242.224489796	17716.7755102041
103	57224	63831.7142857143	-6607.71428571428
104	43750	26956.1428571429	16793.8571428571
105	48029	63831.7142857143	-15802.7142857143
106	104978	80265.5	24712.5
107	100046	112702.836734694	-12656.8367346939
108	101047	132242.224489796	-31195.2244897959
109	197426	80265.5	117160.5
110	160902	112702.836734694	48199.1632653061
111	147172	132242.224489796	14929.7755102041
112	109432	80265.5	29166.5
113	1168	2491.3125	-1323.3125
114	83248	80265.5	2982.5
115	25162	26956.1428571429	-1794.14285714286
116	45724	112702.836734694	-66978.8367346939
117	110529	132242.224489796	-21713.2244897959
118	855	2491.3125	-1636.3125
119	101382	80265.5	21116.5
120	14116	2491.3125	11624.6875
121	89506	132242.224489796	-42736.2244897959
122	135356	112702.836734694	22653.1632653061
123	116066	112702.836734694	3363.16326530612
124	144244	80265.5	63978.5
125	8773	26956.1428571429	-18183.1428571429
126	102153	112702.836734694	-10549.8367346939
127	117440	132242.224489796	-14802.2244897959
128	104128	112702.836734694	-8574.83673469388
129	134238	132242.224489796	1995.77551020408
130	134047	132242.224489796	1804.77551020408
131	279488	132242.224489796	147245.775510204
132	79756	112702.836734694	-32946.8367346939
133	66089	80265.5	-14176.5
134	102070	132242.224489796	-30172.2244897959
135	146760	132242.224489796	14517.7755102041
136	154771	112702.836734694	42068.1632653061
137	165933	132242.224489796	33690.7755102041
138	64593	80265.5	-15672.5
139	92280	112702.836734694	-20422.8367346939
140	67150	80265.5	-13115.5
141	128692	63831.7142857143	64860.2857142857
142	124089	132242.224489796	-8153.22448979592
143	125386	112702.836734694	12683.1632653061
144	37238	80265.5	-43027.5
145	140015	132242.224489796	7772.77551020408
146	150047	112702.836734694	37344.1632653061
147	154451	112702.836734694	41748.1632653061
148	156349	132242.224489796	24106.7755102041
149	0	2491.3125	-2491.3125
150	6023	2491.3125	3531.6875
151	0	2491.3125	-2491.3125
152	0	2491.3125	-2491.3125
153	0	2491.3125	-2491.3125
154	0	2491.3125	-2491.3125
155	84601	80265.5	4335.5
156	68946	132242.224489796	-63296.2244897959
157	0	2491.3125	-2491.3125
158	0	2491.3125	-2491.3125
159	1644	2491.3125	-847.3125
160	6179	2491.3125	3687.6875
161	3926	2491.3125	1434.6875
162	52789	80265.5	-27476.5
163	0	2491.3125	-2491.3125
164	100350	80265.5	20084.5

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 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 5 ; 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