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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

Mon, 13 Dec 2010 10:44:01 +0000

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/2010/Dec/13/t1292237083y3x3e2e9u273c6h.htm/, Retrieved Wed, 29 Apr 2026 19:07:26 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108815, Retrieved Wed, 29 Apr 2026 19:07:26 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

420

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 Recursive Pa...] [2010-12-13 10:44:01] [aa6b599ccd367bc74fed0d8f67004a46] [Current]
-    D      [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2010-12-13 14:00:29] [afe9379cca749d06b3d6872e02cc47ed] 
-    D      [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2010-12-13 14:00:29] [afe9379cca749d06b3d6872e02cc47ed] 
-    D      [Recursive Partitioning (Regression Trees)] [WS10 Recursive Pa...] [2010-12-13 14:00:29] [afe9379cca749d06b3d6872e02cc47ed] 
-    D        [Recursive Partitioning (Regression Trees)] [apple Inc - Recur...] [2010-12-14 15:16:31] [afe9379cca749d06b3d6872e02cc47ed] 
-    D          [Recursive Partitioning (Regression Trees)] [Paper - Recursive...] [2010-12-21 12:58:36] [18fa53e8b37a5effc0c5f8a5122cdd2d] 
-    D          [Recursive Partitioning (Regression Trees)] [Apple Inc - Recur...] [2010-12-22 09:15:38] [afe9379cca749d06b3d6872e02cc47ed] 
-   PD            [Recursive Partitioning (Regression Trees)] [Paper - Recursive...] [2010-12-22 11:34:12] [1f5baf2b24e732d76900bb8178fc04e7] 
-    D              [Recursive Partitioning (Regression Trees)] [Paper - Recursive...] [2010-12-24 12:31:43] [1f5baf2b24e732d76900bb8178fc04e7] 
-    D                [Recursive Partitioning (Regression Trees)] [Paper Recursive P...] [2010-12-26 15:33:18] [eeb33d252044f8583501f5ba0605ad6d] 
-    D            [Recursive Partitioning (Regression Trees)] [Apple Inc - Recur...] [2010-12-24 13:24:35] [afe9379cca749d06b3d6872e02cc47ed] 
-    D              [Recursive Partitioning (Regression Trees)] [Apple Inc - Recur...] [2010-12-24 13:36:44] [afe9379cca749d06b3d6872e02cc47ed] 
-    D        [Recursive Partitioning (Regression Trees)] [Apple Inc - Recur...] [2010-12-14 15:16:31] [afe9379cca749d06b3d6872e02cc47ed] 
- R  D        [Recursive Partitioning (Regression Trees)] [] [2011-12-13 16:01:24] [aba4febe8a2e49e81bdc61a6c01f5c21] 
- R P           [Recursive Partitioning (Regression Trees)] [Paper Recursive p...] [2011-12-20 10:40:06] [aba4febe8a2e49e81bdc61a6c01f5c21] 
- R PD        [Recursive Partitioning (Regression Trees)] [] [2011-12-13 16:09:07] [aba4febe8a2e49e81bdc61a6c01f5c21] 
- R  D        [Recursive Partitioning (Regression Trees)] [WS10 Recursive pa...] [2012-12-06 14:20:53] [74be16979710d4c4e7c6647856088456] 
- RM          [Recursive Partitioning (Regression Trees)] [WS 10 - Recursive...] [2012-12-10 19:17:57] [74be16979710d4c4e7c6647856088456] 
- RM          [Recursive Partitioning (Regression Trees)] [] [2012-12-11 23:35:03] [74be16979710d4c4e7c6647856088456] 
- R PD        [Recursive Partitioning (Regression Trees)] [Workshop 10: Recu...] [2012-12-12 01:17:30] [081ff4808467d7c84e980fa7f896f721] 

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Dataseries X:

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25.94	23688100	39.18	3940.35	0,02740	 144.7	5,45
28.66	13741000	35.78	4696.69	0,03220	 140.8	5,73
33.95	14143500	42.54	4572.83	0,03760	 137.1	5,85
31.01	16763800	27.92	3860.66	0,03070	 137.7	6,02
21.00	16634600	25.05	3400.91	0,03190	 144.7	6,27
26.19	13693300	32.03	3966.11	0,03730	 139.2	6,53
25.41	10545800	27.95	3766.99	0,03660	 143.0	6,54
30.47	9409900	27.95	4206.35	0,03410	 140.8	6,5
12.88	39182200	24.15	3672.82	0,03450	 142.5	6,52
9.78	37005800	27.57	3369.63	0,03450	 135.8	6,51
8.25	15818500	22.97	2597.93	0,03450	 132.6	6,51
7.44	16952000	17.37	2470.52	0,03390	 128.6	6,4
10.81	24563400	24.45	2772.73	0,03730	 115.7	5,98
9.12	14163200	23.62	2151.83	0,03530	 109.2	5,49
11.03	18184800	21.90	1840.26	0,02920	 116.9	5,31
12.74	20810300	27.12	2116.24	0,03270	 109.9	4,8
9.98	12843000	27.70	2110.49	0,03620	 116.1	4,21
11.62	13866700	29.23	2160.54	0,03250	 118.9	3,97
9.40	15119200	26.50	2027.13	0,02720	 116.3	3,77
9.27	8301600	22.84	1805.43	0,02720	 114.0	3,65
7.76	14039600	20.49	1498.80	0,02650	 97.0	3,07
8.78	12139700	23.28	1690.20	0,02130	 85.3	2,49
10.65	9649000	25.71	1930.58	0,01900	 84.9	2,09
10.95	8513600	26.52	1950.40	0,01550	 94.6	1,82
12.36	15278600	25.51	1934.03	0,01140	 97.8	1,73
10.85	15590900	23.36	1731.49	0,01140	 95.0	1,74
11.84	9691100	24.15	1845.35	0,01480	 110.7	1,73
12.14	10882700	20.92	1688.23	0,01640	 108.5	1,75
11.65	10294800	20.38	1615.73	0,01180	 110.3	1,75
8.86	16031900	21.90	1463.21	0,01070	 106.3	1,75
7.63	13683600	19.21	1328.26	0,01460	 97.4	1,73
7.38	8677200	19.65	1314.85	0,01800	 94.5	1,74
7.25	9874100	17.51	1172.06	0,01510	 93.7	1,75
8.03	10725500	21.41	1329.75	0,02030	 79.6	1,75
7.75	8348400	23.09	1478.78	0,02200	 84.9	1,34
7.16	8046200	20.70	1335.51	0,02380	 80.7	1,24
7.18	10862300	19.00	1320.91	0,02600	 78.8	1,24
7.51	8100300	19.04	1337.52	0,02980	 64.8	1,26
7.07	7287500	19.45	1341.17	0,03020	 61.4	1,25
7.11	14002500	20.54	1464.31	0,02220	 81.0	1,26
8.98	19037900	19.77	1595.91	0,02060	 83.6	1,26
9.53	10774600	20.60	1622.80	0,02110	 83.5	1,22
10.54	8960600	21.21	1735.02	0,02110	 77.0	1,01
11.31	7773300	21.30	1810.45	0,02160	 81.7	1,03
10.36	9579700	22.33	1786.94	0,02320	 77.0	1,01
11.44	11270700	21.12	1932.21	0,02040	 81.7	1,01
10.45	9492800	20.77	1960.26	0,01770	 92.5	1
10.69	9136800	22.11	2003.37	0,01880	 91.7	0,98
11.28	14487600	22.34	2066.15	0,01930	 96.4	1
11.96	10133200	21.43	2029.82	0,01690	 88.5	1,01
13.52	18659700	20.14	1994.22	0,01740	 88.5	1
12.89	15980700	21.11	1920.15	0,02290	 93.0	1
14.03	9732100	21.19	1986.74	0,03050	 93.1	1
16.27	14626300	23.07	2047.79	0,03270	 102.8	1,03
16.17	16904000	23.01	1887.36	0,02990	 105.7	1,26
17.25	13616700	22.12	1838.10	0,02650	 98.7	1,43
19.38	13772900	22.40	1896.84	0,02540	 96.7	1,61
26.20	28749200	22.66	1974.99	0,03190	 92.9	1,76
33.53	31408300	24.21	2096.81	0,03520	 92.6	1,93
32.20	26342800	24.13	2175.44	0,03260	 102.7	2,16
38.45	48909500	23.73	2062.41	0,02970	 105.1	2,28
44.86	41542400	22.79	2051.72	0,03010	 104.4	2,5
41.67	24857200	21.89	1999.23	0,03150	 103.0	2,63
36.06	34093700	22.92	1921.65	0,03510	 97.5	2,79
39.76	22555200	23.44	2068.22	0,02800	 103.1	3
36.81	19067500	22.57	2056.96	0,02530	 106.2	3,04
42.65	19029100	23.27	2184.83	0,03170	 103.6	3,26
46.89	15223200	24.95	2152.09	0,03640	 105.5	3,5
53.61	21903700	23.45	2151.69	0,04690	 87.5	3,62
57.59	33306600	23.42	2120.30	0,04350	 85.2	3,78
67.82	23898100	25.30	2232.82	0,03460	 98.3	4
71.89	23279600	23.90	2205.32	0,03420	 103.8	4,16
75.51	40699800	25.73	2305.82	0,03990	 106.8	4,29
68.49	37646000	24.64	2281.39	0,03600	 102.7	4,49
62.72	37277000	24.95	2339.79	0,03360	 107.5	4,59
70.39	39246800	22.15	2322.57	0,03550	 109.8	4,79
59.77	27418400	20.85	2178.88	0,04170	 104.7	4,94
57.27	30318700	21.45	2172.09	0,04320	 105.7	4,99
67.96	32808100	22.15	2091.47	0,04150	 107.0	5,24
67.85	28668200	23.75	2183.75	0,03820	 100.2	5,25
76.98	32370300	25.27	2258.43	0,02060	 105.9	5,25
81.08	24171100	26.53	2366.71	0,01310	 105.1	5,25
91.66	25009100	27.22	2431.77	0,01970	 105.3	5,25
84.84	32084300	27.69	2415.29	0,02540	 110.0	5,24
85.73	50117500	28.61	2463.93	0,02080	 110.2	5,25
84.61	27522200	26.21	2416.15	0,02420	 111.2	5,26
92.91	26816800	25.93	2421.64	0,02780	 108.2	5,26
99.80	25136100	27.86	2525.09	0,02570	 106.3	5,25
121.19	30295600	28.65	2604.52	0,02690	 108.5	5,25
122.04	41526100	27.51	2603.23	0,02690	 105.3	5,25
131.76	43845100	27.06	2546.27	0,02360	 111.9	5,26
138.48	39188900	26.91	2596.36	0,01970	 105.6	5,02
153.47	40496400	27.60	2701.50	0,02760	 99.5	4,94
189.95	37438400	34.48	2859.12	0,03540	 95.2	4,76
182.22	46553700	31.58	2660.96	0,04310	 87.8	4,49
198.08	31771400	33.46	2652.28	0,04080	 90.6	4,24
135.36	62108100	30.64	2389.86	0,04280	 87.9	3,94
125.02	46645400	25.66	2271.48	0,04030	 76.4	2,98
143.50	42313100	26.78	2279.10	0,03980	 65.9	2,61
173.95	38841700	26.91	2412.80	0,03940	 62.3	2,28
188.75	32650300	26.82	2522.66	0,04180	 57.2	1,98
167.44	34281100	26.05	2292.98	0,05020	 50.4	2
158.95	33096200	24.36	2325.55	0,05600	 51.9	2,01
169.53	23273800	25.94	2367.52	0,05370	 58.5	2
113.66	43697600	25.37	2091.88	0,04940	 61.4	1,81
107.59	66902300	21.23	1720.95	0,03660	 38.8	0,97
92.67	44957200	19.35	1535.57	0,01070	 44.9	0,39
85.35	33800900	18.61	1577.03	0,00090	 38.6	0,16
90.13	33487900	16.37	1476.42	0,00030	 4.0	0,15
89.31	27394900	15.56	1377.84	0,00240	 25.3	0,22
105.12	25963400	17.70	1528.59	-0,00380	 26.9	0,18
125.83	20952600	19.52	1717.30	-0,00740	 40.8	0,15
135.81	17702900	20.26	1774.33	-0,01280	 54.8	0,18
142.43	21282100	23.05	1835.04	-0,01430	 49.3	0,21
163.39	18449100	22.81	1978.50	-0,02100	 47.4	0,16
168.21	14415700	24.04	2009.06	-0,01480	 54.5	0,16
185.35	17906300	25.08	2122.42	-0,01290	 53.4	0,15
188.50	22197500	27.04	2045.11	-0,00180	 48.7	0,12
199.91	15856500	28.81	2144.60	0,01840	 50.6	0,12
210.73	19068700	29.86	2269.15	0,02720	 53.6	0,12
192.06	30855100	27.61	2147.35	0,02630	 56.5	0,11
204.62	21209000	28.22	2238.26	0,02140	 46.4	0,13
235.00	19541600	28.83	2397.96	0,02310	 52.3	0,16
261.09	21955000	30.06	2461.19	0,02240	 57.7	0,2
256.88	33725900	25.51	2257.04	0,02020	 62.7	0,2
251.53	28192800	22.75	2109.24	0,01050	 54.3	0,18
257.25	27377000	25.52	2254.70	0,01240	 51.0	0,18
243.10	16228100	23.33	2114.03	0,01150	 53.2	0,19
283.75	21278900	24.34	2368.62	0,01140	 48.6	0,19
300.98	21457400	26.51	2507.41	0,01170	 49.9	0,19

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108815&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108815&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108815&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.9268
R-squared	0.859
RMSE	28.6691

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9268 \tabularnewline
R-squared & 0.859 \tabularnewline
RMSE & 28.6691 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108815&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9268[/C][/ROW]
[ROW][C]R-squared[/C][C]0.859[/C][/ROW]
[ROW][C]RMSE[/C][C]28.6691[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108815&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108815&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	0.9268
R-squared	0.859
RMSE	28.6691

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	25.94	49.0725	-23.1325
2	28.66	26.1283333333333	2.53166666666667
3	33.95	26.1283333333333	7.82166666666667
4	31.01	26.1283333333333	4.88166666666667
5	21	26.1283333333333	-5.12833333333333
6	26.19	26.1283333333333	0.0616666666666674
7	25.41	26.1283333333333	-0.718333333333334
8	30.47	26.1283333333333	4.34166666666667
9	12.88	50.825	-37.945
10	9.78	119.258421052632	-109.478421052632
11	8.25	26.1283333333333	-17.8783333333333
12	7.44	26.1283333333333	-18.6883333333333
13	10.81	49.0725	-38.2625
14	9.12	13.544347826087	-4.42434782608696
15	11.03	13.544347826087	-2.51434782608696
16	12.74	13.544347826087	-0.804347826086957
17	9.98	13.544347826087	-3.56434782608696
18	11.62	26.1283333333333	-14.5083333333333
19	9.4	13.544347826087	-4.14434782608696
20	9.27	10.39	-1.12
21	7.76	7.71666666666667	0.043333333333333
22	8.78	10.39	-1.61
23	10.65	13.544347826087	-2.89434782608696
24	10.95	13.544347826087	-2.59434782608696
25	12.36	13.544347826087	-1.18434782608696
26	10.85	10.39	0.459999999999999
27	11.84	13.544347826087	-1.70434782608696
28	12.14	10.39	1.75
29	11.65	10.39	1.26
30	8.86	7.71666666666667	1.14333333333333
31	7.63	7.71666666666667	-0.0866666666666669
32	7.38	7.71666666666667	-0.336666666666667
33	7.25	7.71666666666667	-0.466666666666667
34	8.03	7.71666666666667	0.313333333333333
35	7.75	7.71666666666667	0.0333333333333332
36	7.16	7.71666666666667	-0.556666666666667
37	7.18	7.71666666666667	-0.536666666666667
38	7.51	7.71666666666667	-0.206666666666667
39	7.07	115.362142857143	-108.292142857143
40	7.11	7.71666666666667	-0.606666666666666
41	8.98	7.71666666666667	1.26333333333333
42	9.53	10.39	-0.860000000000001
43	10.54	10.39	0.149999999999999
44	11.31	13.544347826087	-2.23434782608696
45	10.36	10.39	-0.0300000000000011
46	11.44	13.544347826087	-2.10434782608696
47	10.45	13.544347826087	-3.09434782608696
48	10.69	13.544347826087	-2.85434782608696
49	11.28	13.544347826087	-2.26434782608696
50	11.96	13.544347826087	-1.58434782608696
51	13.52	13.544347826087	-0.0243478260869576
52	12.89	13.544347826087	-0.654347826086957
53	14.03	13.544347826087	0.485652173913042
54	16.27	13.544347826087	2.72565217391304
55	16.17	13.544347826087	2.62565217391304
56	17.25	13.544347826087	3.70565217391304
57	19.38	13.544347826087	5.83565217391304
58	26.2	50.825	-24.625
59	33.53	50.825	-17.295
60	32.2	50.825	-18.625
61	38.45	50.825	-12.375
62	44.86	50.825	-5.965
63	41.67	49.0725	-7.4025
64	36.06	50.825	-14.765
65	39.76	49.0725	-9.3125
66	36.81	13.544347826087	23.265652173913
67	42.65	26.1283333333333	16.5216666666667
68	46.89	26.1283333333333	20.7616666666667
69	53.61	49.0725	4.5375
70	57.59	50.825	6.765
71	67.82	49.0725	18.7475
72	71.89	49.0725	22.8175
73	75.51	119.258421052632	-43.7484210526316
74	68.49	50.825	17.665
75	62.72	50.825	11.895
76	70.39	50.825	19.565
77	59.77	50.825	8.945
78	57.27	50.825	6.445
79	67.96	50.825	17.135
80	67.85	50.825	17.025
81	76.98	50.825	26.155
82	81.08	49.0725	32.0075
83	91.66	119.258421052632	-27.5984210526316
84	84.84	119.258421052632	-34.4184210526316
85	85.73	119.258421052632	-33.5284210526316
86	84.61	119.258421052632	-34.6484210526316
87	92.91	119.258421052632	-26.3484210526316
88	99.8	119.258421052632	-19.4584210526316
89	121.19	119.258421052632	1.93157894736842
90	122.04	119.258421052632	2.78157894736843
91	131.76	119.258421052632	12.5015789473684
92	138.48	119.258421052632	19.2215789473684
93	153.47	119.258421052632	34.2115789473684
94	189.95	119.258421052632	70.6915789473684
95	182.22	119.258421052632	62.9615789473684
96	198.08	119.258421052632	78.8215789473684
97	135.36	119.258421052632	16.1015789473684
98	125.02	119.258421052632	5.76157894736842
99	143.5	119.258421052632	24.2415789473684
100	173.95	218.937222222222	-44.9872222222222
101	188.75	218.937222222222	-30.1872222222222
102	167.44	218.937222222222	-51.4972222222222
103	158.95	218.937222222222	-59.9872222222222
104	169.53	218.937222222222	-49.4072222222222
105	113.66	115.362142857143	-1.70214285714286
106	107.59	115.362142857143	-7.77214285714285
107	92.67	115.362142857143	-22.6921428571429
108	85.35	115.362142857143	-30.0121428571429
109	90.13	115.362142857143	-25.2321428571429
110	89.31	115.362142857143	-26.0521428571429
111	105.12	115.362142857143	-10.2421428571429
112	125.83	115.362142857143	10.4678571428571
113	135.81	115.362142857143	20.4478571428571
114	142.43	115.362142857143	27.0678571428572
115	163.39	115.362142857143	48.0278571428571
116	168.21	115.362142857143	52.8478571428572
117	185.35	218.937222222222	-33.5872222222222
118	188.5	115.362142857143	73.1378571428571
119	199.91	218.937222222222	-19.0272222222222
120	210.73	218.937222222222	-8.20722222222224
121	192.06	218.937222222222	-26.8772222222222
122	204.62	218.937222222222	-14.3172222222222
123	235	218.937222222222	16.0627777777778
124	261.09	218.937222222222	42.1527777777777
125	256.88	218.937222222222	37.9427777777778
126	251.53	218.937222222222	32.5927777777778
127	257.25	218.937222222222	38.3127777777778
128	243.1	218.937222222222	24.1627777777778
129	283.75	218.937222222222	64.8127777777778
130	300.98	218.937222222222	82.0427777777778

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 25.94 & 49.0725 & -23.1325 \tabularnewline
2 & 28.66 & 26.1283333333333 & 2.53166666666667 \tabularnewline
3 & 33.95 & 26.1283333333333 & 7.82166666666667 \tabularnewline
4 & 31.01 & 26.1283333333333 & 4.88166666666667 \tabularnewline
5 & 21 & 26.1283333333333 & -5.12833333333333 \tabularnewline
6 & 26.19 & 26.1283333333333 & 0.0616666666666674 \tabularnewline
7 & 25.41 & 26.1283333333333 & -0.718333333333334 \tabularnewline
8 & 30.47 & 26.1283333333333 & 4.34166666666667 \tabularnewline
9 & 12.88 & 50.825 & -37.945 \tabularnewline
10 & 9.78 & 119.258421052632 & -109.478421052632 \tabularnewline
11 & 8.25 & 26.1283333333333 & -17.8783333333333 \tabularnewline
12 & 7.44 & 26.1283333333333 & -18.6883333333333 \tabularnewline
13 & 10.81 & 49.0725 & -38.2625 \tabularnewline
14 & 9.12 & 13.544347826087 & -4.42434782608696 \tabularnewline
15 & 11.03 & 13.544347826087 & -2.51434782608696 \tabularnewline
16 & 12.74 & 13.544347826087 & -0.804347826086957 \tabularnewline
17 & 9.98 & 13.544347826087 & -3.56434782608696 \tabularnewline
18 & 11.62 & 26.1283333333333 & -14.5083333333333 \tabularnewline
19 & 9.4 & 13.544347826087 & -4.14434782608696 \tabularnewline
20 & 9.27 & 10.39 & -1.12 \tabularnewline
21 & 7.76 & 7.71666666666667 & 0.043333333333333 \tabularnewline
22 & 8.78 & 10.39 & -1.61 \tabularnewline
23 & 10.65 & 13.544347826087 & -2.89434782608696 \tabularnewline
24 & 10.95 & 13.544347826087 & -2.59434782608696 \tabularnewline
25 & 12.36 & 13.544347826087 & -1.18434782608696 \tabularnewline
26 & 10.85 & 10.39 & 0.459999999999999 \tabularnewline
27 & 11.84 & 13.544347826087 & -1.70434782608696 \tabularnewline
28 & 12.14 & 10.39 & 1.75 \tabularnewline
29 & 11.65 & 10.39 & 1.26 \tabularnewline
30 & 8.86 & 7.71666666666667 & 1.14333333333333 \tabularnewline
31 & 7.63 & 7.71666666666667 & -0.0866666666666669 \tabularnewline
32 & 7.38 & 7.71666666666667 & -0.336666666666667 \tabularnewline
33 & 7.25 & 7.71666666666667 & -0.466666666666667 \tabularnewline
34 & 8.03 & 7.71666666666667 & 0.313333333333333 \tabularnewline
35 & 7.75 & 7.71666666666667 & 0.0333333333333332 \tabularnewline
36 & 7.16 & 7.71666666666667 & -0.556666666666667 \tabularnewline
37 & 7.18 & 7.71666666666667 & -0.536666666666667 \tabularnewline
38 & 7.51 & 7.71666666666667 & -0.206666666666667 \tabularnewline
39 & 7.07 & 115.362142857143 & -108.292142857143 \tabularnewline
40 & 7.11 & 7.71666666666667 & -0.606666666666666 \tabularnewline
41 & 8.98 & 7.71666666666667 & 1.26333333333333 \tabularnewline
42 & 9.53 & 10.39 & -0.860000000000001 \tabularnewline
43 & 10.54 & 10.39 & 0.149999999999999 \tabularnewline
44 & 11.31 & 13.544347826087 & -2.23434782608696 \tabularnewline
45 & 10.36 & 10.39 & -0.0300000000000011 \tabularnewline
46 & 11.44 & 13.544347826087 & -2.10434782608696 \tabularnewline
47 & 10.45 & 13.544347826087 & -3.09434782608696 \tabularnewline
48 & 10.69 & 13.544347826087 & -2.85434782608696 \tabularnewline
49 & 11.28 & 13.544347826087 & -2.26434782608696 \tabularnewline
50 & 11.96 & 13.544347826087 & -1.58434782608696 \tabularnewline
51 & 13.52 & 13.544347826087 & -0.0243478260869576 \tabularnewline
52 & 12.89 & 13.544347826087 & -0.654347826086957 \tabularnewline
53 & 14.03 & 13.544347826087 & 0.485652173913042 \tabularnewline
54 & 16.27 & 13.544347826087 & 2.72565217391304 \tabularnewline
55 & 16.17 & 13.544347826087 & 2.62565217391304 \tabularnewline
56 & 17.25 & 13.544347826087 & 3.70565217391304 \tabularnewline
57 & 19.38 & 13.544347826087 & 5.83565217391304 \tabularnewline
58 & 26.2 & 50.825 & -24.625 \tabularnewline
59 & 33.53 & 50.825 & -17.295 \tabularnewline
60 & 32.2 & 50.825 & -18.625 \tabularnewline
61 & 38.45 & 50.825 & -12.375 \tabularnewline
62 & 44.86 & 50.825 & -5.965 \tabularnewline
63 & 41.67 & 49.0725 & -7.4025 \tabularnewline
64 & 36.06 & 50.825 & -14.765 \tabularnewline
65 & 39.76 & 49.0725 & -9.3125 \tabularnewline
66 & 36.81 & 13.544347826087 & 23.265652173913 \tabularnewline
67 & 42.65 & 26.1283333333333 & 16.5216666666667 \tabularnewline
68 & 46.89 & 26.1283333333333 & 20.7616666666667 \tabularnewline
69 & 53.61 & 49.0725 & 4.5375 \tabularnewline
70 & 57.59 & 50.825 & 6.765 \tabularnewline
71 & 67.82 & 49.0725 & 18.7475 \tabularnewline
72 & 71.89 & 49.0725 & 22.8175 \tabularnewline
73 & 75.51 & 119.258421052632 & -43.7484210526316 \tabularnewline
74 & 68.49 & 50.825 & 17.665 \tabularnewline
75 & 62.72 & 50.825 & 11.895 \tabularnewline
76 & 70.39 & 50.825 & 19.565 \tabularnewline
77 & 59.77 & 50.825 & 8.945 \tabularnewline
78 & 57.27 & 50.825 & 6.445 \tabularnewline
79 & 67.96 & 50.825 & 17.135 \tabularnewline
80 & 67.85 & 50.825 & 17.025 \tabularnewline
81 & 76.98 & 50.825 & 26.155 \tabularnewline
82 & 81.08 & 49.0725 & 32.0075 \tabularnewline
83 & 91.66 & 119.258421052632 & -27.5984210526316 \tabularnewline
84 & 84.84 & 119.258421052632 & -34.4184210526316 \tabularnewline
85 & 85.73 & 119.258421052632 & -33.5284210526316 \tabularnewline
86 & 84.61 & 119.258421052632 & -34.6484210526316 \tabularnewline
87 & 92.91 & 119.258421052632 & -26.3484210526316 \tabularnewline
88 & 99.8 & 119.258421052632 & -19.4584210526316 \tabularnewline
89 & 121.19 & 119.258421052632 & 1.93157894736842 \tabularnewline
90 & 122.04 & 119.258421052632 & 2.78157894736843 \tabularnewline
91 & 131.76 & 119.258421052632 & 12.5015789473684 \tabularnewline
92 & 138.48 & 119.258421052632 & 19.2215789473684 \tabularnewline
93 & 153.47 & 119.258421052632 & 34.2115789473684 \tabularnewline
94 & 189.95 & 119.258421052632 & 70.6915789473684 \tabularnewline
95 & 182.22 & 119.258421052632 & 62.9615789473684 \tabularnewline
96 & 198.08 & 119.258421052632 & 78.8215789473684 \tabularnewline
97 & 135.36 & 119.258421052632 & 16.1015789473684 \tabularnewline
98 & 125.02 & 119.258421052632 & 5.76157894736842 \tabularnewline
99 & 143.5 & 119.258421052632 & 24.2415789473684 \tabularnewline
100 & 173.95 & 218.937222222222 & -44.9872222222222 \tabularnewline
101 & 188.75 & 218.937222222222 & -30.1872222222222 \tabularnewline
102 & 167.44 & 218.937222222222 & -51.4972222222222 \tabularnewline
103 & 158.95 & 218.937222222222 & -59.9872222222222 \tabularnewline
104 & 169.53 & 218.937222222222 & -49.4072222222222 \tabularnewline
105 & 113.66 & 115.362142857143 & -1.70214285714286 \tabularnewline
106 & 107.59 & 115.362142857143 & -7.77214285714285 \tabularnewline
107 & 92.67 & 115.362142857143 & -22.6921428571429 \tabularnewline
108 & 85.35 & 115.362142857143 & -30.0121428571429 \tabularnewline
109 & 90.13 & 115.362142857143 & -25.2321428571429 \tabularnewline
110 & 89.31 & 115.362142857143 & -26.0521428571429 \tabularnewline
111 & 105.12 & 115.362142857143 & -10.2421428571429 \tabularnewline
112 & 125.83 & 115.362142857143 & 10.4678571428571 \tabularnewline
113 & 135.81 & 115.362142857143 & 20.4478571428571 \tabularnewline
114 & 142.43 & 115.362142857143 & 27.0678571428572 \tabularnewline
115 & 163.39 & 115.362142857143 & 48.0278571428571 \tabularnewline
116 & 168.21 & 115.362142857143 & 52.8478571428572 \tabularnewline
117 & 185.35 & 218.937222222222 & -33.5872222222222 \tabularnewline
118 & 188.5 & 115.362142857143 & 73.1378571428571 \tabularnewline
119 & 199.91 & 218.937222222222 & -19.0272222222222 \tabularnewline
120 & 210.73 & 218.937222222222 & -8.20722222222224 \tabularnewline
121 & 192.06 & 218.937222222222 & -26.8772222222222 \tabularnewline
122 & 204.62 & 218.937222222222 & -14.3172222222222 \tabularnewline
123 & 235 & 218.937222222222 & 16.0627777777778 \tabularnewline
124 & 261.09 & 218.937222222222 & 42.1527777777777 \tabularnewline
125 & 256.88 & 218.937222222222 & 37.9427777777778 \tabularnewline
126 & 251.53 & 218.937222222222 & 32.5927777777778 \tabularnewline
127 & 257.25 & 218.937222222222 & 38.3127777777778 \tabularnewline
128 & 243.1 & 218.937222222222 & 24.1627777777778 \tabularnewline
129 & 283.75 & 218.937222222222 & 64.8127777777778 \tabularnewline
130 & 300.98 & 218.937222222222 & 82.0427777777778 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108815&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]25.94[/C][C]49.0725[/C][C]-23.1325[/C][/ROW]
[ROW][C]2[/C][C]28.66[/C][C]26.1283333333333[/C][C]2.53166666666667[/C][/ROW]
[ROW][C]3[/C][C]33.95[/C][C]26.1283333333333[/C][C]7.82166666666667[/C][/ROW]
[ROW][C]4[/C][C]31.01[/C][C]26.1283333333333[/C][C]4.88166666666667[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]26.1283333333333[/C][C]-5.12833333333333[/C][/ROW]
[ROW][C]6[/C][C]26.19[/C][C]26.1283333333333[/C][C]0.0616666666666674[/C][/ROW]
[ROW][C]7[/C][C]25.41[/C][C]26.1283333333333[/C][C]-0.718333333333334[/C][/ROW]
[ROW][C]8[/C][C]30.47[/C][C]26.1283333333333[/C][C]4.34166666666667[/C][/ROW]
[ROW][C]9[/C][C]12.88[/C][C]50.825[/C][C]-37.945[/C][/ROW]
[ROW][C]10[/C][C]9.78[/C][C]119.258421052632[/C][C]-109.478421052632[/C][/ROW]
[ROW][C]11[/C][C]8.25[/C][C]26.1283333333333[/C][C]-17.8783333333333[/C][/ROW]
[ROW][C]12[/C][C]7.44[/C][C]26.1283333333333[/C][C]-18.6883333333333[/C][/ROW]
[ROW][C]13[/C][C]10.81[/C][C]49.0725[/C][C]-38.2625[/C][/ROW]
[ROW][C]14[/C][C]9.12[/C][C]13.544347826087[/C][C]-4.42434782608696[/C][/ROW]
[ROW][C]15[/C][C]11.03[/C][C]13.544347826087[/C][C]-2.51434782608696[/C][/ROW]
[ROW][C]16[/C][C]12.74[/C][C]13.544347826087[/C][C]-0.804347826086957[/C][/ROW]
[ROW][C]17[/C][C]9.98[/C][C]13.544347826087[/C][C]-3.56434782608696[/C][/ROW]
[ROW][C]18[/C][C]11.62[/C][C]26.1283333333333[/C][C]-14.5083333333333[/C][/ROW]
[ROW][C]19[/C][C]9.4[/C][C]13.544347826087[/C][C]-4.14434782608696[/C][/ROW]
[ROW][C]20[/C][C]9.27[/C][C]10.39[/C][C]-1.12[/C][/ROW]
[ROW][C]21[/C][C]7.76[/C][C]7.71666666666667[/C][C]0.043333333333333[/C][/ROW]
[ROW][C]22[/C][C]8.78[/C][C]10.39[/C][C]-1.61[/C][/ROW]
[ROW][C]23[/C][C]10.65[/C][C]13.544347826087[/C][C]-2.89434782608696[/C][/ROW]
[ROW][C]24[/C][C]10.95[/C][C]13.544347826087[/C][C]-2.59434782608696[/C][/ROW]
[ROW][C]25[/C][C]12.36[/C][C]13.544347826087[/C][C]-1.18434782608696[/C][/ROW]
[ROW][C]26[/C][C]10.85[/C][C]10.39[/C][C]0.459999999999999[/C][/ROW]
[ROW][C]27[/C][C]11.84[/C][C]13.544347826087[/C][C]-1.70434782608696[/C][/ROW]
[ROW][C]28[/C][C]12.14[/C][C]10.39[/C][C]1.75[/C][/ROW]
[ROW][C]29[/C][C]11.65[/C][C]10.39[/C][C]1.26[/C][/ROW]
[ROW][C]30[/C][C]8.86[/C][C]7.71666666666667[/C][C]1.14333333333333[/C][/ROW]
[ROW][C]31[/C][C]7.63[/C][C]7.71666666666667[/C][C]-0.0866666666666669[/C][/ROW]
[ROW][C]32[/C][C]7.38[/C][C]7.71666666666667[/C][C]-0.336666666666667[/C][/ROW]
[ROW][C]33[/C][C]7.25[/C][C]7.71666666666667[/C][C]-0.466666666666667[/C][/ROW]
[ROW][C]34[/C][C]8.03[/C][C]7.71666666666667[/C][C]0.313333333333333[/C][/ROW]
[ROW][C]35[/C][C]7.75[/C][C]7.71666666666667[/C][C]0.0333333333333332[/C][/ROW]
[ROW][C]36[/C][C]7.16[/C][C]7.71666666666667[/C][C]-0.556666666666667[/C][/ROW]
[ROW][C]37[/C][C]7.18[/C][C]7.71666666666667[/C][C]-0.536666666666667[/C][/ROW]
[ROW][C]38[/C][C]7.51[/C][C]7.71666666666667[/C][C]-0.206666666666667[/C][/ROW]
[ROW][C]39[/C][C]7.07[/C][C]115.362142857143[/C][C]-108.292142857143[/C][/ROW]
[ROW][C]40[/C][C]7.11[/C][C]7.71666666666667[/C][C]-0.606666666666666[/C][/ROW]
[ROW][C]41[/C][C]8.98[/C][C]7.71666666666667[/C][C]1.26333333333333[/C][/ROW]
[ROW][C]42[/C][C]9.53[/C][C]10.39[/C][C]-0.860000000000001[/C][/ROW]
[ROW][C]43[/C][C]10.54[/C][C]10.39[/C][C]0.149999999999999[/C][/ROW]
[ROW][C]44[/C][C]11.31[/C][C]13.544347826087[/C][C]-2.23434782608696[/C][/ROW]
[ROW][C]45[/C][C]10.36[/C][C]10.39[/C][C]-0.0300000000000011[/C][/ROW]
[ROW][C]46[/C][C]11.44[/C][C]13.544347826087[/C][C]-2.10434782608696[/C][/ROW]
[ROW][C]47[/C][C]10.45[/C][C]13.544347826087[/C][C]-3.09434782608696[/C][/ROW]
[ROW][C]48[/C][C]10.69[/C][C]13.544347826087[/C][C]-2.85434782608696[/C][/ROW]
[ROW][C]49[/C][C]11.28[/C][C]13.544347826087[/C][C]-2.26434782608696[/C][/ROW]
[ROW][C]50[/C][C]11.96[/C][C]13.544347826087[/C][C]-1.58434782608696[/C][/ROW]
[ROW][C]51[/C][C]13.52[/C][C]13.544347826087[/C][C]-0.0243478260869576[/C][/ROW]
[ROW][C]52[/C][C]12.89[/C][C]13.544347826087[/C][C]-0.654347826086957[/C][/ROW]
[ROW][C]53[/C][C]14.03[/C][C]13.544347826087[/C][C]0.485652173913042[/C][/ROW]
[ROW][C]54[/C][C]16.27[/C][C]13.544347826087[/C][C]2.72565217391304[/C][/ROW]
[ROW][C]55[/C][C]16.17[/C][C]13.544347826087[/C][C]2.62565217391304[/C][/ROW]
[ROW][C]56[/C][C]17.25[/C][C]13.544347826087[/C][C]3.70565217391304[/C][/ROW]
[ROW][C]57[/C][C]19.38[/C][C]13.544347826087[/C][C]5.83565217391304[/C][/ROW]
[ROW][C]58[/C][C]26.2[/C][C]50.825[/C][C]-24.625[/C][/ROW]
[ROW][C]59[/C][C]33.53[/C][C]50.825[/C][C]-17.295[/C][/ROW]
[ROW][C]60[/C][C]32.2[/C][C]50.825[/C][C]-18.625[/C][/ROW]
[ROW][C]61[/C][C]38.45[/C][C]50.825[/C][C]-12.375[/C][/ROW]
[ROW][C]62[/C][C]44.86[/C][C]50.825[/C][C]-5.965[/C][/ROW]
[ROW][C]63[/C][C]41.67[/C][C]49.0725[/C][C]-7.4025[/C][/ROW]
[ROW][C]64[/C][C]36.06[/C][C]50.825[/C][C]-14.765[/C][/ROW]
[ROW][C]65[/C][C]39.76[/C][C]49.0725[/C][C]-9.3125[/C][/ROW]
[ROW][C]66[/C][C]36.81[/C][C]13.544347826087[/C][C]23.265652173913[/C][/ROW]
[ROW][C]67[/C][C]42.65[/C][C]26.1283333333333[/C][C]16.5216666666667[/C][/ROW]
[ROW][C]68[/C][C]46.89[/C][C]26.1283333333333[/C][C]20.7616666666667[/C][/ROW]
[ROW][C]69[/C][C]53.61[/C][C]49.0725[/C][C]4.5375[/C][/ROW]
[ROW][C]70[/C][C]57.59[/C][C]50.825[/C][C]6.765[/C][/ROW]
[ROW][C]71[/C][C]67.82[/C][C]49.0725[/C][C]18.7475[/C][/ROW]
[ROW][C]72[/C][C]71.89[/C][C]49.0725[/C][C]22.8175[/C][/ROW]
[ROW][C]73[/C][C]75.51[/C][C]119.258421052632[/C][C]-43.7484210526316[/C][/ROW]
[ROW][C]74[/C][C]68.49[/C][C]50.825[/C][C]17.665[/C][/ROW]
[ROW][C]75[/C][C]62.72[/C][C]50.825[/C][C]11.895[/C][/ROW]
[ROW][C]76[/C][C]70.39[/C][C]50.825[/C][C]19.565[/C][/ROW]
[ROW][C]77[/C][C]59.77[/C][C]50.825[/C][C]8.945[/C][/ROW]
[ROW][C]78[/C][C]57.27[/C][C]50.825[/C][C]6.445[/C][/ROW]
[ROW][C]79[/C][C]67.96[/C][C]50.825[/C][C]17.135[/C][/ROW]
[ROW][C]80[/C][C]67.85[/C][C]50.825[/C][C]17.025[/C][/ROW]
[ROW][C]81[/C][C]76.98[/C][C]50.825[/C][C]26.155[/C][/ROW]
[ROW][C]82[/C][C]81.08[/C][C]49.0725[/C][C]32.0075[/C][/ROW]
[ROW][C]83[/C][C]91.66[/C][C]119.258421052632[/C][C]-27.5984210526316[/C][/ROW]
[ROW][C]84[/C][C]84.84[/C][C]119.258421052632[/C][C]-34.4184210526316[/C][/ROW]
[ROW][C]85[/C][C]85.73[/C][C]119.258421052632[/C][C]-33.5284210526316[/C][/ROW]
[ROW][C]86[/C][C]84.61[/C][C]119.258421052632[/C][C]-34.6484210526316[/C][/ROW]
[ROW][C]87[/C][C]92.91[/C][C]119.258421052632[/C][C]-26.3484210526316[/C][/ROW]
[ROW][C]88[/C][C]99.8[/C][C]119.258421052632[/C][C]-19.4584210526316[/C][/ROW]
[ROW][C]89[/C][C]121.19[/C][C]119.258421052632[/C][C]1.93157894736842[/C][/ROW]
[ROW][C]90[/C][C]122.04[/C][C]119.258421052632[/C][C]2.78157894736843[/C][/ROW]
[ROW][C]91[/C][C]131.76[/C][C]119.258421052632[/C][C]12.5015789473684[/C][/ROW]
[ROW][C]92[/C][C]138.48[/C][C]119.258421052632[/C][C]19.2215789473684[/C][/ROW]
[ROW][C]93[/C][C]153.47[/C][C]119.258421052632[/C][C]34.2115789473684[/C][/ROW]
[ROW][C]94[/C][C]189.95[/C][C]119.258421052632[/C][C]70.6915789473684[/C][/ROW]
[ROW][C]95[/C][C]182.22[/C][C]119.258421052632[/C][C]62.9615789473684[/C][/ROW]
[ROW][C]96[/C][C]198.08[/C][C]119.258421052632[/C][C]78.8215789473684[/C][/ROW]
[ROW][C]97[/C][C]135.36[/C][C]119.258421052632[/C][C]16.1015789473684[/C][/ROW]
[ROW][C]98[/C][C]125.02[/C][C]119.258421052632[/C][C]5.76157894736842[/C][/ROW]
[ROW][C]99[/C][C]143.5[/C][C]119.258421052632[/C][C]24.2415789473684[/C][/ROW]
[ROW][C]100[/C][C]173.95[/C][C]218.937222222222[/C][C]-44.9872222222222[/C][/ROW]
[ROW][C]101[/C][C]188.75[/C][C]218.937222222222[/C][C]-30.1872222222222[/C][/ROW]
[ROW][C]102[/C][C]167.44[/C][C]218.937222222222[/C][C]-51.4972222222222[/C][/ROW]
[ROW][C]103[/C][C]158.95[/C][C]218.937222222222[/C][C]-59.9872222222222[/C][/ROW]
[ROW][C]104[/C][C]169.53[/C][C]218.937222222222[/C][C]-49.4072222222222[/C][/ROW]
[ROW][C]105[/C][C]113.66[/C][C]115.362142857143[/C][C]-1.70214285714286[/C][/ROW]
[ROW][C]106[/C][C]107.59[/C][C]115.362142857143[/C][C]-7.77214285714285[/C][/ROW]
[ROW][C]107[/C][C]92.67[/C][C]115.362142857143[/C][C]-22.6921428571429[/C][/ROW]
[ROW][C]108[/C][C]85.35[/C][C]115.362142857143[/C][C]-30.0121428571429[/C][/ROW]
[ROW][C]109[/C][C]90.13[/C][C]115.362142857143[/C][C]-25.2321428571429[/C][/ROW]
[ROW][C]110[/C][C]89.31[/C][C]115.362142857143[/C][C]-26.0521428571429[/C][/ROW]
[ROW][C]111[/C][C]105.12[/C][C]115.362142857143[/C][C]-10.2421428571429[/C][/ROW]
[ROW][C]112[/C][C]125.83[/C][C]115.362142857143[/C][C]10.4678571428571[/C][/ROW]
[ROW][C]113[/C][C]135.81[/C][C]115.362142857143[/C][C]20.4478571428571[/C][/ROW]
[ROW][C]114[/C][C]142.43[/C][C]115.362142857143[/C][C]27.0678571428572[/C][/ROW]
[ROW][C]115[/C][C]163.39[/C][C]115.362142857143[/C][C]48.0278571428571[/C][/ROW]
[ROW][C]116[/C][C]168.21[/C][C]115.362142857143[/C][C]52.8478571428572[/C][/ROW]
[ROW][C]117[/C][C]185.35[/C][C]218.937222222222[/C][C]-33.5872222222222[/C][/ROW]
[ROW][C]118[/C][C]188.5[/C][C]115.362142857143[/C][C]73.1378571428571[/C][/ROW]
[ROW][C]119[/C][C]199.91[/C][C]218.937222222222[/C][C]-19.0272222222222[/C][/ROW]
[ROW][C]120[/C][C]210.73[/C][C]218.937222222222[/C][C]-8.20722222222224[/C][/ROW]
[ROW][C]121[/C][C]192.06[/C][C]218.937222222222[/C][C]-26.8772222222222[/C][/ROW]
[ROW][C]122[/C][C]204.62[/C][C]218.937222222222[/C][C]-14.3172222222222[/C][/ROW]
[ROW][C]123[/C][C]235[/C][C]218.937222222222[/C][C]16.0627777777778[/C][/ROW]
[ROW][C]124[/C][C]261.09[/C][C]218.937222222222[/C][C]42.1527777777777[/C][/ROW]
[ROW][C]125[/C][C]256.88[/C][C]218.937222222222[/C][C]37.9427777777778[/C][/ROW]
[ROW][C]126[/C][C]251.53[/C][C]218.937222222222[/C][C]32.5927777777778[/C][/ROW]
[ROW][C]127[/C][C]257.25[/C][C]218.937222222222[/C][C]38.3127777777778[/C][/ROW]
[ROW][C]128[/C][C]243.1[/C][C]218.937222222222[/C][C]24.1627777777778[/C][/ROW]
[ROW][C]129[/C][C]283.75[/C][C]218.937222222222[/C][C]64.8127777777778[/C][/ROW]
[ROW][C]130[/C][C]300.98[/C][C]218.937222222222[/C][C]82.0427777777778[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108815&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108815&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	25.94	49.0725	-23.1325
2	28.66	26.1283333333333	2.53166666666667
3	33.95	26.1283333333333	7.82166666666667
4	31.01	26.1283333333333	4.88166666666667
5	21	26.1283333333333	-5.12833333333333
6	26.19	26.1283333333333	0.0616666666666674
7	25.41	26.1283333333333	-0.718333333333334
8	30.47	26.1283333333333	4.34166666666667
9	12.88	50.825	-37.945
10	9.78	119.258421052632	-109.478421052632
11	8.25	26.1283333333333	-17.8783333333333
12	7.44	26.1283333333333	-18.6883333333333
13	10.81	49.0725	-38.2625
14	9.12	13.544347826087	-4.42434782608696
15	11.03	13.544347826087	-2.51434782608696
16	12.74	13.544347826087	-0.804347826086957
17	9.98	13.544347826087	-3.56434782608696
18	11.62	26.1283333333333	-14.5083333333333
19	9.4	13.544347826087	-4.14434782608696
20	9.27	10.39	-1.12
21	7.76	7.71666666666667	0.043333333333333
22	8.78	10.39	-1.61
23	10.65	13.544347826087	-2.89434782608696
24	10.95	13.544347826087	-2.59434782608696
25	12.36	13.544347826087	-1.18434782608696
26	10.85	10.39	0.459999999999999
27	11.84	13.544347826087	-1.70434782608696
28	12.14	10.39	1.75
29	11.65	10.39	1.26
30	8.86	7.71666666666667	1.14333333333333
31	7.63	7.71666666666667	-0.0866666666666669
32	7.38	7.71666666666667	-0.336666666666667
33	7.25	7.71666666666667	-0.466666666666667
34	8.03	7.71666666666667	0.313333333333333
35	7.75	7.71666666666667	0.0333333333333332
36	7.16	7.71666666666667	-0.556666666666667
37	7.18	7.71666666666667	-0.536666666666667
38	7.51	7.71666666666667	-0.206666666666667
39	7.07	115.362142857143	-108.292142857143
40	7.11	7.71666666666667	-0.606666666666666
41	8.98	7.71666666666667	1.26333333333333
42	9.53	10.39	-0.860000000000001
43	10.54	10.39	0.149999999999999
44	11.31	13.544347826087	-2.23434782608696
45	10.36	10.39	-0.0300000000000011
46	11.44	13.544347826087	-2.10434782608696
47	10.45	13.544347826087	-3.09434782608696
48	10.69	13.544347826087	-2.85434782608696
49	11.28	13.544347826087	-2.26434782608696
50	11.96	13.544347826087	-1.58434782608696
51	13.52	13.544347826087	-0.0243478260869576
52	12.89	13.544347826087	-0.654347826086957
53	14.03	13.544347826087	0.485652173913042
54	16.27	13.544347826087	2.72565217391304
55	16.17	13.544347826087	2.62565217391304
56	17.25	13.544347826087	3.70565217391304
57	19.38	13.544347826087	5.83565217391304
58	26.2	50.825	-24.625
59	33.53	50.825	-17.295
60	32.2	50.825	-18.625
61	38.45	50.825	-12.375
62	44.86	50.825	-5.965
63	41.67	49.0725	-7.4025
64	36.06	50.825	-14.765
65	39.76	49.0725	-9.3125
66	36.81	13.544347826087	23.265652173913
67	42.65	26.1283333333333	16.5216666666667
68	46.89	26.1283333333333	20.7616666666667
69	53.61	49.0725	4.5375
70	57.59	50.825	6.765
71	67.82	49.0725	18.7475
72	71.89	49.0725	22.8175
73	75.51	119.258421052632	-43.7484210526316
74	68.49	50.825	17.665
75	62.72	50.825	11.895
76	70.39	50.825	19.565
77	59.77	50.825	8.945
78	57.27	50.825	6.445
79	67.96	50.825	17.135
80	67.85	50.825	17.025
81	76.98	50.825	26.155
82	81.08	49.0725	32.0075
83	91.66	119.258421052632	-27.5984210526316
84	84.84	119.258421052632	-34.4184210526316
85	85.73	119.258421052632	-33.5284210526316
86	84.61	119.258421052632	-34.6484210526316
87	92.91	119.258421052632	-26.3484210526316
88	99.8	119.258421052632	-19.4584210526316
89	121.19	119.258421052632	1.93157894736842
90	122.04	119.258421052632	2.78157894736843
91	131.76	119.258421052632	12.5015789473684
92	138.48	119.258421052632	19.2215789473684
93	153.47	119.258421052632	34.2115789473684
94	189.95	119.258421052632	70.6915789473684
95	182.22	119.258421052632	62.9615789473684
96	198.08	119.258421052632	78.8215789473684
97	135.36	119.258421052632	16.1015789473684
98	125.02	119.258421052632	5.76157894736842
99	143.5	119.258421052632	24.2415789473684
100	173.95	218.937222222222	-44.9872222222222
101	188.75	218.937222222222	-30.1872222222222
102	167.44	218.937222222222	-51.4972222222222
103	158.95	218.937222222222	-59.9872222222222
104	169.53	218.937222222222	-49.4072222222222
105	113.66	115.362142857143	-1.70214285714286
106	107.59	115.362142857143	-7.77214285714285
107	92.67	115.362142857143	-22.6921428571429
108	85.35	115.362142857143	-30.0121428571429
109	90.13	115.362142857143	-25.2321428571429
110	89.31	115.362142857143	-26.0521428571429
111	105.12	115.362142857143	-10.2421428571429
112	125.83	115.362142857143	10.4678571428571
113	135.81	115.362142857143	20.4478571428571
114	142.43	115.362142857143	27.0678571428572
115	163.39	115.362142857143	48.0278571428571
116	168.21	115.362142857143	52.8478571428572
117	185.35	218.937222222222	-33.5872222222222
118	188.5	115.362142857143	73.1378571428571
119	199.91	218.937222222222	-19.0272222222222
120	210.73	218.937222222222	-8.20722222222224
121	192.06	218.937222222222	-26.8772222222222
122	204.62	218.937222222222	-14.3172222222222
123	235	218.937222222222	16.0627777777778
124	261.09	218.937222222222	42.1527777777777
125	256.88	218.937222222222	37.9427777777778
126	251.53	218.937222222222	32.5927777777778
127	257.25	218.937222222222	38.3127777777778
128	243.1	218.937222222222	24.1627777777778
129	283.75	218.937222222222	64.8127777777778
130	300.98	218.937222222222	82.0427777777778

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = ; 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