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Author

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R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Wed, 21 Dec 2011 09:21:32 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/21/t1324477648xvb8pabam6o1sbg.htm/, Retrieved Mon, 29 Apr 2024 06:20:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158738, Retrieved Mon, 29 Apr 2024 06:20:13 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

114

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [Tutorial 2-1] [2011-11-21 19:39:55] [9e469a83342941fcd5c6dffbf184cd3a]
-   PD    [Multiple Regression] [Tutorial2-1] [2011-11-21 20:30:04] [9e469a83342941fcd5c6dffbf184cd3a]
- R PD      [Multiple Regression] [multiple regressi...] [2011-12-19 21:00:38] [2e8e2c135ae7a1d1ed044e87454acf31]
- RMP         [Kendall tau Correlation Matrix] [pearson correlatie] [2011-12-21 13:23:03] [2e8e2c135ae7a1d1ed044e87454acf31]
- RMP             [Recursive Partitioning (Regression Trees)] [Regression trees] [2011-12-21 14:21:32] [aeb40720a676d7d277b8965d0f8ecacb] [Current]
- R P               [Recursive Partitioning (Regression Trees)] [regression trees ] [2011-12-21 14:30:40] [2e8e2c135ae7a1d1ed044e87454acf31] 

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Dataseries X:

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Histogram

Boxplots

158147	1760	89	48	18	20465
182462	1609	56	52	20	33629
7215	192	18	0	0	1423
122259	2182	92	49	26	25629
222405	3367	131	76	31	54002
485890	6658	253	124	36	151036
150777	1548	54	42	23	33287
160529	1507	56	68	30	31172
133238	1682	42	52	30	28113
275326	2811	91	67	26	57803
121821	1943	74	50	24	49830
172489	2017	66	71	30	52143
89942	1702	96	41	21	21055
208851	3034	110	79	25	47007
151886	1379	55	49	18	28735
145427	1517	79	54	19	59147
134153	1637	53	75	33	78950
64149	1077	53	0	15	13497
122417	2384	84	54	34	46154
27997	726	24	13	18	53249
65004	993	55	17	15	10726
205417	2446	91	83	27	83700
188103	1713	70	37	25	40400
118698	2027	50	44	34	33797
143682	1818	81	50	21	36205
140172	1393	28	39	21	30165
186377	2000	154	59	25	58534
174870	1346	85	79	31	44663
317699	2522	114	55	30	92556
192335	2106	43	52	20	40078
151621	1515	42	50	28	34711
167466	1519	43	54	20	31076
125909	2165	100	53	17	74608
221896	2959	120	76	25	58092
217447	2364	52	60	24	42009
0	1	1	0	0	0
207163	2009	59	53	27	36022
93107	1564	50	44	14	23333
133763	2072	47	36	32	53349
246427	2106	63	83	31	92596
224097	2270	69	100	21	49598
142057	1643	56	37	34	44093
94332	957	29	25	23	84205
171724	2025	77	59	24	63369
101683	1115	45	44	26	60132
156753	1176	90	41	22	37403
81293	744	31	23	35	24460
201984	1974	91	63	21	46456
219875	2224	85	54	31	66616
156589	2561	56	67	26	41554
48188	658	28	12	22	22346
138146	1716	64	82	21	30874
279590	2355	71	64	27	68701
234829	2017	77	56	30	35728
181731	1686	57	54	33	29010
141014	1675	54	35	11	23110
189220	1760	62	52	26	38844
76419	875	23	25	26	27084
151898	1169	65	67	23	35139
189402	2789	93	36	38	57476
140189	1606	56	50	29	33277
123181	1986	75	48	19	31141
124234	1300	58	46	19	61281
107277	1176	34	53	24	25820
153813	1215	32	27	26	23284
94982	1230	38	38	29	35378
178613	2226	67	68	36	74990
138708	2897	65	93	25	29653
102378	1008	37	48	24	64622
31970	340	15	5	21	4157
211635	2704	110	53	19	29245
111885	1209	63	36	12	50008
99687	1290	63	62	30	52338
102900	1535	68	46	21	13310
156475	2585	65	73	34	92901
74513	1315	41	2	32	10956
159186	2142	57	76	27	34241
155818	2513	94	71	28	75043
60138	817	24	16	21	21152
84971	1234	71	34	31	42249
80478	917	66	54	26	42005
244325	1924	59	39	29	41152
56486	853	27	26	23	14399
110743	1398	34	40	25	28263
75092	986	44	35	22	17215
148286	1608	47	32	26	48140
222914	2576	219	55	33	62897
115019	1201	108	58	24	22883
93083	1189	56	39	24	41622
143258	1383	49	33	21	40715
117794	1563	39	45	28	65897
158586	2185	74	72	27	76542
151465	1228	56	39	25	37477
124626	1266	58	27	15	53216
51801	830	36	22	13	40911
223020	2217	110	47	36	57021
188957	1787	68	95	24	73116
19349	223	12	13	1	3895
188069	2170	99	27	24	46609
150561	1927	74	40	31	29351
53921	665	28	22	4	2325
58280	804	22	41	20	31747
124951	1211	49	55	23	32665
112263	1143	57	28	23	19249
72904	710	38	30	12	15292
27676	596	22	2	16	5842
131274	1353	44	79	29	33994
117451	971	32	18	10	13018
0	0	0	0	0	0
85610	1030	31	46	25	98177
107175	1130	66	25	21	37941
133024	1284	44	50	23	31032
136473	1438	61	59	21	32683
71894	849	57	36	21	34545
3616	78	5	0	0	0
0	0	0	0	0	0
154806	925	39	35	23	27525
137977	1518	78	68	29	66856
149846	1946	95	26	28	28549
113245	914	37	36	23	38610
43410	778	19	7	1	2781
170330	1713	71	67	29	41211
89410	895	40	30	17	22698
112749	1756	52	55	29	41194
60373	701	40	3	12	32689
19764	285	12	10	2	5752
160995	1774	55	46	21	26757
121052	1021	28	26	25	22527
150039	1582	46	49	29	44810
11796	256	9	1	2	0
10674	98	9	0	0	0
134836	1358	55	33	18	100674
6836	41	3	0	1	0
153278	1770	57	48	21	57786
5118	42	3	5	0	0
40248	528	16	8	4	5444
0	0	0	0	0	0
117842	1026	45	35	25	28470
87635	1296	38	21	26	61849
7131	81	4	0	0	0
8812	257	13	0	4	2179
68916	914	23	15	17	8019
132686	1178	50	50	21	39644
94127	1080	19	17	22	23494

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158738&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158738&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158738&T=0

As an alternative you can also use a QR Code:

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

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

Goodness of Fit
Correlation	0.863
R-squared	0.7447
RMSE	36152.6036

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.863 \tabularnewline
R-squared & 0.7447 \tabularnewline
RMSE & 36152.6036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158738&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.863[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7447[/C][/ROW]
[ROW][C]RMSE[/C][C]36152.6036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158738&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158738&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	0.863
R-squared	0.7447
RMSE	36152.6036

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	158147	167104.633333333	-8957.63333333333
2	182462	142903.5	39558.5
3	7215	11776.7647058824	-4561.76470588235
4	122259	167104.633333333	-44845.6333333333
5	222405	248925.428571429	-26520.4285714286
6	485890	248925.428571429	236964.571428571
7	150777	120212.875	30564.125
8	160529	142903.5	17625.5
9	133238	142903.5	-9665.5
10	275326	248925.428571429	26400.5714285714
11	121821	167104.633333333	-45283.6333333333
12	172489	167104.633333333	5384.36666666667
13	89942	120212.875	-30270.875
14	208851	248925.428571429	-40074.4285714286
15	151886	142903.5	8982.5
16	145427	142903.5	2523.5
17	134153	142903.5	-8750.5
18	64149	97220.5882352941	-33071.5882352941
19	122417	206543.285714286	-84126.2857142857
20	27997	58700.3076923077	-30703.3076923077
21	65004	97220.5882352941	-32216.5882352941
22	205417	206543.285714286	-1126.28571428571
23	188103	167104.633333333	20998.3666666667
24	118698	167104.633333333	-48406.6333333333
25	143682	167104.633333333	-23422.6333333333
26	140172	120212.875	19959.125
27	186377	167104.633333333	19272.3666666667
28	174870	142903.5	31966.5
29	317699	206543.285714286	111155.714285714
30	192335	167104.633333333	25230.3666666667
31	151621	142903.5	8717.5
32	167466	142903.5	24562.5
33	125909	167104.633333333	-41195.6333333333
34	221896	248925.428571429	-27029.4285714286
35	217447	206543.285714286	10903.7142857143
36	0	11776.7647058824	-11776.7647058824
37	207163	167104.633333333	40058.3666666667
38	93107	120212.875	-27105.875
39	133763	167104.633333333	-33341.6333333333
40	246427	167104.633333333	79322.3666666667
41	224097	206543.285714286	17553.7142857143
42	142057	120212.875	21844.125
43	94332	97220.5882352941	-2888.58823529411
44	171724	167104.633333333	4619.36666666667
45	101683	97220.5882352941	4462.41176470589
46	156753	120212.875	36540.125
47	81293	58700.3076923077	22592.6923076923
48	201984	167104.633333333	34879.3666666667
49	219875	206543.285714286	13331.7142857143
50	156589	206543.285714286	-49954.2857142857
51	48188	58700.3076923077	-10512.3076923077
52	138146	167104.633333333	-28958.6333333333
53	279590	206543.285714286	73046.7142857143
54	234829	167104.633333333	67724.3666666667
55	181731	142903.5	38827.5
56	141014	120212.875	20801.125
57	189220	167104.633333333	22115.3666666667
58	76419	58700.3076923077	17718.6923076923
59	151898	142903.5	8994.5
60	189402	248925.428571429	-59523.4285714286
61	140189	142903.5	-2714.5
62	123181	167104.633333333	-43923.6333333333
63	124234	120212.875	4021.125
64	107277	142903.5	-35626.5
65	153813	120212.875	33600.125
66	94982	120212.875	-25230.875
67	178613	206543.285714286	-27930.2857142857
68	138708	248925.428571429	-110217.428571429
69	102378	97220.5882352941	5157.41176470589
70	31970	11776.7647058824	20193.2352941176
71	211635	206543.285714286	5091.71428571429
72	111885	120212.875	-8327.875
73	99687	142903.5	-43216.5
74	102900	120212.875	-17312.875
75	156475	206543.285714286	-50068.2857142857
76	74513	120212.875	-45699.875
77	159186	167104.633333333	-7918.63333333333
78	155818	206543.285714286	-50725.2857142857
79	60138	58700.3076923077	1437.6923076923
80	84971	120212.875	-35241.875
81	80478	97220.5882352941	-16742.5882352941
82	244325	167104.633333333	77220.3666666667
83	56486	58700.3076923077	-2214.3076923077
84	110743	120212.875	-9469.875
85	75092	97220.5882352941	-22128.5882352941
86	148286	120212.875	28073.125
87	222914	206543.285714286	16370.7142857143
88	115019	142903.5	-27884.5
89	93083	120212.875	-27129.875
90	143258	120212.875	23045.125
91	117794	120212.875	-2418.875
92	158586	167104.633333333	-8518.63333333333
93	151465	120212.875	31252.125
94	124626	120212.875	4413.125
95	51801	58700.3076923077	-6899.3076923077
96	223020	206543.285714286	16476.7142857143
97	188957	167104.633333333	21852.3666666667
98	19349	11776.7647058824	7572.23529411765
99	188069	167104.633333333	20964.3666666667
100	150561	167104.633333333	-16543.6333333333
101	53921	58700.3076923077	-4779.3076923077
102	58280	58700.3076923077	-420.307692307695
103	124951	142903.5	-17952.5
104	112263	120212.875	-7949.875
105	72904	58700.3076923077	14203.6923076923
106	27676	11776.7647058824	15899.2352941176
107	131274	142903.5	-11629.5
108	117451	97220.5882352941	20230.4117647059
109	0	11776.7647058824	-11776.7647058824
110	85610	97220.5882352941	-11610.5882352941
111	107175	97220.5882352941	9954.41176470589
112	133024	142903.5	-9879.5
113	136473	142903.5	-6430.5
114	71894	58700.3076923077	13193.6923076923
115	3616	11776.7647058824	-8160.76470588235
116	0	11776.7647058824	-11776.7647058824
117	154806	97220.5882352941	57585.4117647059
118	137977	142903.5	-4926.5
119	149846	167104.633333333	-17258.6333333333
120	113245	97220.5882352941	16024.4117647059
121	43410	58700.3076923077	-15290.3076923077
122	170330	167104.633333333	3225.36666666667
123	89410	97220.5882352941	-7810.58823529411
124	112749	167104.633333333	-54355.6333333333
125	60373	58700.3076923077	1672.6923076923
126	19764	11776.7647058824	7987.23529411765
127	160995	167104.633333333	-6109.63333333333
128	121052	97220.5882352941	23831.4117647059
129	150039	142903.5	7135.5
130	11796	11776.7647058824	19.2352941176468
131	10674	11776.7647058824	-1102.76470588235
132	134836	120212.875	14623.125
133	6836	11776.7647058824	-4940.76470588235
134	153278	167104.633333333	-13826.6333333333
135	5118	11776.7647058824	-6658.76470588235
136	40248	11776.7647058824	28471.2352941176
137	0	11776.7647058824	-11776.7647058824
138	117842	97220.5882352941	20621.4117647059
139	87635	120212.875	-32577.875
140	7131	11776.7647058824	-4645.76470588235
141	8812	11776.7647058824	-2964.76470588235
142	68916	97220.5882352941	-28304.5882352941
143	132686	142903.5	-10217.5
144	94127	97220.5882352941	-3093.58823529411

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 158147 & 167104.633333333 & -8957.63333333333 \tabularnewline
2 & 182462 & 142903.5 & 39558.5 \tabularnewline
3 & 7215 & 11776.7647058824 & -4561.76470588235 \tabularnewline
4 & 122259 & 167104.633333333 & -44845.6333333333 \tabularnewline
5 & 222405 & 248925.428571429 & -26520.4285714286 \tabularnewline
6 & 485890 & 248925.428571429 & 236964.571428571 \tabularnewline
7 & 150777 & 120212.875 & 30564.125 \tabularnewline
8 & 160529 & 142903.5 & 17625.5 \tabularnewline
9 & 133238 & 142903.5 & -9665.5 \tabularnewline
10 & 275326 & 248925.428571429 & 26400.5714285714 \tabularnewline
11 & 121821 & 167104.633333333 & -45283.6333333333 \tabularnewline
12 & 172489 & 167104.633333333 & 5384.36666666667 \tabularnewline
13 & 89942 & 120212.875 & -30270.875 \tabularnewline
14 & 208851 & 248925.428571429 & -40074.4285714286 \tabularnewline
15 & 151886 & 142903.5 & 8982.5 \tabularnewline
16 & 145427 & 142903.5 & 2523.5 \tabularnewline
17 & 134153 & 142903.5 & -8750.5 \tabularnewline
18 & 64149 & 97220.5882352941 & -33071.5882352941 \tabularnewline
19 & 122417 & 206543.285714286 & -84126.2857142857 \tabularnewline
20 & 27997 & 58700.3076923077 & -30703.3076923077 \tabularnewline
21 & 65004 & 97220.5882352941 & -32216.5882352941 \tabularnewline
22 & 205417 & 206543.285714286 & -1126.28571428571 \tabularnewline
23 & 188103 & 167104.633333333 & 20998.3666666667 \tabularnewline
24 & 118698 & 167104.633333333 & -48406.6333333333 \tabularnewline
25 & 143682 & 167104.633333333 & -23422.6333333333 \tabularnewline
26 & 140172 & 120212.875 & 19959.125 \tabularnewline
27 & 186377 & 167104.633333333 & 19272.3666666667 \tabularnewline
28 & 174870 & 142903.5 & 31966.5 \tabularnewline
29 & 317699 & 206543.285714286 & 111155.714285714 \tabularnewline
30 & 192335 & 167104.633333333 & 25230.3666666667 \tabularnewline
31 & 151621 & 142903.5 & 8717.5 \tabularnewline
32 & 167466 & 142903.5 & 24562.5 \tabularnewline
33 & 125909 & 167104.633333333 & -41195.6333333333 \tabularnewline
34 & 221896 & 248925.428571429 & -27029.4285714286 \tabularnewline
35 & 217447 & 206543.285714286 & 10903.7142857143 \tabularnewline
36 & 0 & 11776.7647058824 & -11776.7647058824 \tabularnewline
37 & 207163 & 167104.633333333 & 40058.3666666667 \tabularnewline
38 & 93107 & 120212.875 & -27105.875 \tabularnewline
39 & 133763 & 167104.633333333 & -33341.6333333333 \tabularnewline
40 & 246427 & 167104.633333333 & 79322.3666666667 \tabularnewline
41 & 224097 & 206543.285714286 & 17553.7142857143 \tabularnewline
42 & 142057 & 120212.875 & 21844.125 \tabularnewline
43 & 94332 & 97220.5882352941 & -2888.58823529411 \tabularnewline
44 & 171724 & 167104.633333333 & 4619.36666666667 \tabularnewline
45 & 101683 & 97220.5882352941 & 4462.41176470589 \tabularnewline
46 & 156753 & 120212.875 & 36540.125 \tabularnewline
47 & 81293 & 58700.3076923077 & 22592.6923076923 \tabularnewline
48 & 201984 & 167104.633333333 & 34879.3666666667 \tabularnewline
49 & 219875 & 206543.285714286 & 13331.7142857143 \tabularnewline
50 & 156589 & 206543.285714286 & -49954.2857142857 \tabularnewline
51 & 48188 & 58700.3076923077 & -10512.3076923077 \tabularnewline
52 & 138146 & 167104.633333333 & -28958.6333333333 \tabularnewline
53 & 279590 & 206543.285714286 & 73046.7142857143 \tabularnewline
54 & 234829 & 167104.633333333 & 67724.3666666667 \tabularnewline
55 & 181731 & 142903.5 & 38827.5 \tabularnewline
56 & 141014 & 120212.875 & 20801.125 \tabularnewline
57 & 189220 & 167104.633333333 & 22115.3666666667 \tabularnewline
58 & 76419 & 58700.3076923077 & 17718.6923076923 \tabularnewline
59 & 151898 & 142903.5 & 8994.5 \tabularnewline
60 & 189402 & 248925.428571429 & -59523.4285714286 \tabularnewline
61 & 140189 & 142903.5 & -2714.5 \tabularnewline
62 & 123181 & 167104.633333333 & -43923.6333333333 \tabularnewline
63 & 124234 & 120212.875 & 4021.125 \tabularnewline
64 & 107277 & 142903.5 & -35626.5 \tabularnewline
65 & 153813 & 120212.875 & 33600.125 \tabularnewline
66 & 94982 & 120212.875 & -25230.875 \tabularnewline
67 & 178613 & 206543.285714286 & -27930.2857142857 \tabularnewline
68 & 138708 & 248925.428571429 & -110217.428571429 \tabularnewline
69 & 102378 & 97220.5882352941 & 5157.41176470589 \tabularnewline
70 & 31970 & 11776.7647058824 & 20193.2352941176 \tabularnewline
71 & 211635 & 206543.285714286 & 5091.71428571429 \tabularnewline
72 & 111885 & 120212.875 & -8327.875 \tabularnewline
73 & 99687 & 142903.5 & -43216.5 \tabularnewline
74 & 102900 & 120212.875 & -17312.875 \tabularnewline
75 & 156475 & 206543.285714286 & -50068.2857142857 \tabularnewline
76 & 74513 & 120212.875 & -45699.875 \tabularnewline
77 & 159186 & 167104.633333333 & -7918.63333333333 \tabularnewline
78 & 155818 & 206543.285714286 & -50725.2857142857 \tabularnewline
79 & 60138 & 58700.3076923077 & 1437.6923076923 \tabularnewline
80 & 84971 & 120212.875 & -35241.875 \tabularnewline
81 & 80478 & 97220.5882352941 & -16742.5882352941 \tabularnewline
82 & 244325 & 167104.633333333 & 77220.3666666667 \tabularnewline
83 & 56486 & 58700.3076923077 & -2214.3076923077 \tabularnewline
84 & 110743 & 120212.875 & -9469.875 \tabularnewline
85 & 75092 & 97220.5882352941 & -22128.5882352941 \tabularnewline
86 & 148286 & 120212.875 & 28073.125 \tabularnewline
87 & 222914 & 206543.285714286 & 16370.7142857143 \tabularnewline
88 & 115019 & 142903.5 & -27884.5 \tabularnewline
89 & 93083 & 120212.875 & -27129.875 \tabularnewline
90 & 143258 & 120212.875 & 23045.125 \tabularnewline
91 & 117794 & 120212.875 & -2418.875 \tabularnewline
92 & 158586 & 167104.633333333 & -8518.63333333333 \tabularnewline
93 & 151465 & 120212.875 & 31252.125 \tabularnewline
94 & 124626 & 120212.875 & 4413.125 \tabularnewline
95 & 51801 & 58700.3076923077 & -6899.3076923077 \tabularnewline
96 & 223020 & 206543.285714286 & 16476.7142857143 \tabularnewline
97 & 188957 & 167104.633333333 & 21852.3666666667 \tabularnewline
98 & 19349 & 11776.7647058824 & 7572.23529411765 \tabularnewline
99 & 188069 & 167104.633333333 & 20964.3666666667 \tabularnewline
100 & 150561 & 167104.633333333 & -16543.6333333333 \tabularnewline
101 & 53921 & 58700.3076923077 & -4779.3076923077 \tabularnewline
102 & 58280 & 58700.3076923077 & -420.307692307695 \tabularnewline
103 & 124951 & 142903.5 & -17952.5 \tabularnewline
104 & 112263 & 120212.875 & -7949.875 \tabularnewline
105 & 72904 & 58700.3076923077 & 14203.6923076923 \tabularnewline
106 & 27676 & 11776.7647058824 & 15899.2352941176 \tabularnewline
107 & 131274 & 142903.5 & -11629.5 \tabularnewline
108 & 117451 & 97220.5882352941 & 20230.4117647059 \tabularnewline
109 & 0 & 11776.7647058824 & -11776.7647058824 \tabularnewline
110 & 85610 & 97220.5882352941 & -11610.5882352941 \tabularnewline
111 & 107175 & 97220.5882352941 & 9954.41176470589 \tabularnewline
112 & 133024 & 142903.5 & -9879.5 \tabularnewline
113 & 136473 & 142903.5 & -6430.5 \tabularnewline
114 & 71894 & 58700.3076923077 & 13193.6923076923 \tabularnewline
115 & 3616 & 11776.7647058824 & -8160.76470588235 \tabularnewline
116 & 0 & 11776.7647058824 & -11776.7647058824 \tabularnewline
117 & 154806 & 97220.5882352941 & 57585.4117647059 \tabularnewline
118 & 137977 & 142903.5 & -4926.5 \tabularnewline
119 & 149846 & 167104.633333333 & -17258.6333333333 \tabularnewline
120 & 113245 & 97220.5882352941 & 16024.4117647059 \tabularnewline
121 & 43410 & 58700.3076923077 & -15290.3076923077 \tabularnewline
122 & 170330 & 167104.633333333 & 3225.36666666667 \tabularnewline
123 & 89410 & 97220.5882352941 & -7810.58823529411 \tabularnewline
124 & 112749 & 167104.633333333 & -54355.6333333333 \tabularnewline
125 & 60373 & 58700.3076923077 & 1672.6923076923 \tabularnewline
126 & 19764 & 11776.7647058824 & 7987.23529411765 \tabularnewline
127 & 160995 & 167104.633333333 & -6109.63333333333 \tabularnewline
128 & 121052 & 97220.5882352941 & 23831.4117647059 \tabularnewline
129 & 150039 & 142903.5 & 7135.5 \tabularnewline
130 & 11796 & 11776.7647058824 & 19.2352941176468 \tabularnewline
131 & 10674 & 11776.7647058824 & -1102.76470588235 \tabularnewline
132 & 134836 & 120212.875 & 14623.125 \tabularnewline
133 & 6836 & 11776.7647058824 & -4940.76470588235 \tabularnewline
134 & 153278 & 167104.633333333 & -13826.6333333333 \tabularnewline
135 & 5118 & 11776.7647058824 & -6658.76470588235 \tabularnewline
136 & 40248 & 11776.7647058824 & 28471.2352941176 \tabularnewline
137 & 0 & 11776.7647058824 & -11776.7647058824 \tabularnewline
138 & 117842 & 97220.5882352941 & 20621.4117647059 \tabularnewline
139 & 87635 & 120212.875 & -32577.875 \tabularnewline
140 & 7131 & 11776.7647058824 & -4645.76470588235 \tabularnewline
141 & 8812 & 11776.7647058824 & -2964.76470588235 \tabularnewline
142 & 68916 & 97220.5882352941 & -28304.5882352941 \tabularnewline
143 & 132686 & 142903.5 & -10217.5 \tabularnewline
144 & 94127 & 97220.5882352941 & -3093.58823529411 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158738&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]158147[/C][C]167104.633333333[/C][C]-8957.63333333333[/C][/ROW]
[ROW][C]2[/C][C]182462[/C][C]142903.5[/C][C]39558.5[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]11776.7647058824[/C][C]-4561.76470588235[/C][/ROW]
[ROW][C]4[/C][C]122259[/C][C]167104.633333333[/C][C]-44845.6333333333[/C][/ROW]
[ROW][C]5[/C][C]222405[/C][C]248925.428571429[/C][C]-26520.4285714286[/C][/ROW]
[ROW][C]6[/C][C]485890[/C][C]248925.428571429[/C][C]236964.571428571[/C][/ROW]
[ROW][C]7[/C][C]150777[/C][C]120212.875[/C][C]30564.125[/C][/ROW]
[ROW][C]8[/C][C]160529[/C][C]142903.5[/C][C]17625.5[/C][/ROW]
[ROW][C]9[/C][C]133238[/C][C]142903.5[/C][C]-9665.5[/C][/ROW]
[ROW][C]10[/C][C]275326[/C][C]248925.428571429[/C][C]26400.5714285714[/C][/ROW]
[ROW][C]11[/C][C]121821[/C][C]167104.633333333[/C][C]-45283.6333333333[/C][/ROW]
[ROW][C]12[/C][C]172489[/C][C]167104.633333333[/C][C]5384.36666666667[/C][/ROW]
[ROW][C]13[/C][C]89942[/C][C]120212.875[/C][C]-30270.875[/C][/ROW]
[ROW][C]14[/C][C]208851[/C][C]248925.428571429[/C][C]-40074.4285714286[/C][/ROW]
[ROW][C]15[/C][C]151886[/C][C]142903.5[/C][C]8982.5[/C][/ROW]
[ROW][C]16[/C][C]145427[/C][C]142903.5[/C][C]2523.5[/C][/ROW]
[ROW][C]17[/C][C]134153[/C][C]142903.5[/C][C]-8750.5[/C][/ROW]
[ROW][C]18[/C][C]64149[/C][C]97220.5882352941[/C][C]-33071.5882352941[/C][/ROW]
[ROW][C]19[/C][C]122417[/C][C]206543.285714286[/C][C]-84126.2857142857[/C][/ROW]
[ROW][C]20[/C][C]27997[/C][C]58700.3076923077[/C][C]-30703.3076923077[/C][/ROW]
[ROW][C]21[/C][C]65004[/C][C]97220.5882352941[/C][C]-32216.5882352941[/C][/ROW]
[ROW][C]22[/C][C]205417[/C][C]206543.285714286[/C][C]-1126.28571428571[/C][/ROW]
[ROW][C]23[/C][C]188103[/C][C]167104.633333333[/C][C]20998.3666666667[/C][/ROW]
[ROW][C]24[/C][C]118698[/C][C]167104.633333333[/C][C]-48406.6333333333[/C][/ROW]
[ROW][C]25[/C][C]143682[/C][C]167104.633333333[/C][C]-23422.6333333333[/C][/ROW]
[ROW][C]26[/C][C]140172[/C][C]120212.875[/C][C]19959.125[/C][/ROW]
[ROW][C]27[/C][C]186377[/C][C]167104.633333333[/C][C]19272.3666666667[/C][/ROW]
[ROW][C]28[/C][C]174870[/C][C]142903.5[/C][C]31966.5[/C][/ROW]
[ROW][C]29[/C][C]317699[/C][C]206543.285714286[/C][C]111155.714285714[/C][/ROW]
[ROW][C]30[/C][C]192335[/C][C]167104.633333333[/C][C]25230.3666666667[/C][/ROW]
[ROW][C]31[/C][C]151621[/C][C]142903.5[/C][C]8717.5[/C][/ROW]
[ROW][C]32[/C][C]167466[/C][C]142903.5[/C][C]24562.5[/C][/ROW]
[ROW][C]33[/C][C]125909[/C][C]167104.633333333[/C][C]-41195.6333333333[/C][/ROW]
[ROW][C]34[/C][C]221896[/C][C]248925.428571429[/C][C]-27029.4285714286[/C][/ROW]
[ROW][C]35[/C][C]217447[/C][C]206543.285714286[/C][C]10903.7142857143[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]11776.7647058824[/C][C]-11776.7647058824[/C][/ROW]
[ROW][C]37[/C][C]207163[/C][C]167104.633333333[/C][C]40058.3666666667[/C][/ROW]
[ROW][C]38[/C][C]93107[/C][C]120212.875[/C][C]-27105.875[/C][/ROW]
[ROW][C]39[/C][C]133763[/C][C]167104.633333333[/C][C]-33341.6333333333[/C][/ROW]
[ROW][C]40[/C][C]246427[/C][C]167104.633333333[/C][C]79322.3666666667[/C][/ROW]
[ROW][C]41[/C][C]224097[/C][C]206543.285714286[/C][C]17553.7142857143[/C][/ROW]
[ROW][C]42[/C][C]142057[/C][C]120212.875[/C][C]21844.125[/C][/ROW]
[ROW][C]43[/C][C]94332[/C][C]97220.5882352941[/C][C]-2888.58823529411[/C][/ROW]
[ROW][C]44[/C][C]171724[/C][C]167104.633333333[/C][C]4619.36666666667[/C][/ROW]
[ROW][C]45[/C][C]101683[/C][C]97220.5882352941[/C][C]4462.41176470589[/C][/ROW]
[ROW][C]46[/C][C]156753[/C][C]120212.875[/C][C]36540.125[/C][/ROW]
[ROW][C]47[/C][C]81293[/C][C]58700.3076923077[/C][C]22592.6923076923[/C][/ROW]
[ROW][C]48[/C][C]201984[/C][C]167104.633333333[/C][C]34879.3666666667[/C][/ROW]
[ROW][C]49[/C][C]219875[/C][C]206543.285714286[/C][C]13331.7142857143[/C][/ROW]
[ROW][C]50[/C][C]156589[/C][C]206543.285714286[/C][C]-49954.2857142857[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]58700.3076923077[/C][C]-10512.3076923077[/C][/ROW]
[ROW][C]52[/C][C]138146[/C][C]167104.633333333[/C][C]-28958.6333333333[/C][/ROW]
[ROW][C]53[/C][C]279590[/C][C]206543.285714286[/C][C]73046.7142857143[/C][/ROW]
[ROW][C]54[/C][C]234829[/C][C]167104.633333333[/C][C]67724.3666666667[/C][/ROW]
[ROW][C]55[/C][C]181731[/C][C]142903.5[/C][C]38827.5[/C][/ROW]
[ROW][C]56[/C][C]141014[/C][C]120212.875[/C][C]20801.125[/C][/ROW]
[ROW][C]57[/C][C]189220[/C][C]167104.633333333[/C][C]22115.3666666667[/C][/ROW]
[ROW][C]58[/C][C]76419[/C][C]58700.3076923077[/C][C]17718.6923076923[/C][/ROW]
[ROW][C]59[/C][C]151898[/C][C]142903.5[/C][C]8994.5[/C][/ROW]
[ROW][C]60[/C][C]189402[/C][C]248925.428571429[/C][C]-59523.4285714286[/C][/ROW]
[ROW][C]61[/C][C]140189[/C][C]142903.5[/C][C]-2714.5[/C][/ROW]
[ROW][C]62[/C][C]123181[/C][C]167104.633333333[/C][C]-43923.6333333333[/C][/ROW]
[ROW][C]63[/C][C]124234[/C][C]120212.875[/C][C]4021.125[/C][/ROW]
[ROW][C]64[/C][C]107277[/C][C]142903.5[/C][C]-35626.5[/C][/ROW]
[ROW][C]65[/C][C]153813[/C][C]120212.875[/C][C]33600.125[/C][/ROW]
[ROW][C]66[/C][C]94982[/C][C]120212.875[/C][C]-25230.875[/C][/ROW]
[ROW][C]67[/C][C]178613[/C][C]206543.285714286[/C][C]-27930.2857142857[/C][/ROW]
[ROW][C]68[/C][C]138708[/C][C]248925.428571429[/C][C]-110217.428571429[/C][/ROW]
[ROW][C]69[/C][C]102378[/C][C]97220.5882352941[/C][C]5157.41176470589[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]11776.7647058824[/C][C]20193.2352941176[/C][/ROW]
[ROW][C]71[/C][C]211635[/C][C]206543.285714286[/C][C]5091.71428571429[/C][/ROW]
[ROW][C]72[/C][C]111885[/C][C]120212.875[/C][C]-8327.875[/C][/ROW]
[ROW][C]73[/C][C]99687[/C][C]142903.5[/C][C]-43216.5[/C][/ROW]
[ROW][C]74[/C][C]102900[/C][C]120212.875[/C][C]-17312.875[/C][/ROW]
[ROW][C]75[/C][C]156475[/C][C]206543.285714286[/C][C]-50068.2857142857[/C][/ROW]
[ROW][C]76[/C][C]74513[/C][C]120212.875[/C][C]-45699.875[/C][/ROW]
[ROW][C]77[/C][C]159186[/C][C]167104.633333333[/C][C]-7918.63333333333[/C][/ROW]
[ROW][C]78[/C][C]155818[/C][C]206543.285714286[/C][C]-50725.2857142857[/C][/ROW]
[ROW][C]79[/C][C]60138[/C][C]58700.3076923077[/C][C]1437.6923076923[/C][/ROW]
[ROW][C]80[/C][C]84971[/C][C]120212.875[/C][C]-35241.875[/C][/ROW]
[ROW][C]81[/C][C]80478[/C][C]97220.5882352941[/C][C]-16742.5882352941[/C][/ROW]
[ROW][C]82[/C][C]244325[/C][C]167104.633333333[/C][C]77220.3666666667[/C][/ROW]
[ROW][C]83[/C][C]56486[/C][C]58700.3076923077[/C][C]-2214.3076923077[/C][/ROW]
[ROW][C]84[/C][C]110743[/C][C]120212.875[/C][C]-9469.875[/C][/ROW]
[ROW][C]85[/C][C]75092[/C][C]97220.5882352941[/C][C]-22128.5882352941[/C][/ROW]
[ROW][C]86[/C][C]148286[/C][C]120212.875[/C][C]28073.125[/C][/ROW]
[ROW][C]87[/C][C]222914[/C][C]206543.285714286[/C][C]16370.7142857143[/C][/ROW]
[ROW][C]88[/C][C]115019[/C][C]142903.5[/C][C]-27884.5[/C][/ROW]
[ROW][C]89[/C][C]93083[/C][C]120212.875[/C][C]-27129.875[/C][/ROW]
[ROW][C]90[/C][C]143258[/C][C]120212.875[/C][C]23045.125[/C][/ROW]
[ROW][C]91[/C][C]117794[/C][C]120212.875[/C][C]-2418.875[/C][/ROW]
[ROW][C]92[/C][C]158586[/C][C]167104.633333333[/C][C]-8518.63333333333[/C][/ROW]
[ROW][C]93[/C][C]151465[/C][C]120212.875[/C][C]31252.125[/C][/ROW]
[ROW][C]94[/C][C]124626[/C][C]120212.875[/C][C]4413.125[/C][/ROW]
[ROW][C]95[/C][C]51801[/C][C]58700.3076923077[/C][C]-6899.3076923077[/C][/ROW]
[ROW][C]96[/C][C]223020[/C][C]206543.285714286[/C][C]16476.7142857143[/C][/ROW]
[ROW][C]97[/C][C]188957[/C][C]167104.633333333[/C][C]21852.3666666667[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]11776.7647058824[/C][C]7572.23529411765[/C][/ROW]
[ROW][C]99[/C][C]188069[/C][C]167104.633333333[/C][C]20964.3666666667[/C][/ROW]
[ROW][C]100[/C][C]150561[/C][C]167104.633333333[/C][C]-16543.6333333333[/C][/ROW]
[ROW][C]101[/C][C]53921[/C][C]58700.3076923077[/C][C]-4779.3076923077[/C][/ROW]
[ROW][C]102[/C][C]58280[/C][C]58700.3076923077[/C][C]-420.307692307695[/C][/ROW]
[ROW][C]103[/C][C]124951[/C][C]142903.5[/C][C]-17952.5[/C][/ROW]
[ROW][C]104[/C][C]112263[/C][C]120212.875[/C][C]-7949.875[/C][/ROW]
[ROW][C]105[/C][C]72904[/C][C]58700.3076923077[/C][C]14203.6923076923[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]11776.7647058824[/C][C]15899.2352941176[/C][/ROW]
[ROW][C]107[/C][C]131274[/C][C]142903.5[/C][C]-11629.5[/C][/ROW]
[ROW][C]108[/C][C]117451[/C][C]97220.5882352941[/C][C]20230.4117647059[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]11776.7647058824[/C][C]-11776.7647058824[/C][/ROW]
[ROW][C]110[/C][C]85610[/C][C]97220.5882352941[/C][C]-11610.5882352941[/C][/ROW]
[ROW][C]111[/C][C]107175[/C][C]97220.5882352941[/C][C]9954.41176470589[/C][/ROW]
[ROW][C]112[/C][C]133024[/C][C]142903.5[/C][C]-9879.5[/C][/ROW]
[ROW][C]113[/C][C]136473[/C][C]142903.5[/C][C]-6430.5[/C][/ROW]
[ROW][C]114[/C][C]71894[/C][C]58700.3076923077[/C][C]13193.6923076923[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]11776.7647058824[/C][C]-8160.76470588235[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]11776.7647058824[/C][C]-11776.7647058824[/C][/ROW]
[ROW][C]117[/C][C]154806[/C][C]97220.5882352941[/C][C]57585.4117647059[/C][/ROW]
[ROW][C]118[/C][C]137977[/C][C]142903.5[/C][C]-4926.5[/C][/ROW]
[ROW][C]119[/C][C]149846[/C][C]167104.633333333[/C][C]-17258.6333333333[/C][/ROW]
[ROW][C]120[/C][C]113245[/C][C]97220.5882352941[/C][C]16024.4117647059[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]58700.3076923077[/C][C]-15290.3076923077[/C][/ROW]
[ROW][C]122[/C][C]170330[/C][C]167104.633333333[/C][C]3225.36666666667[/C][/ROW]
[ROW][C]123[/C][C]89410[/C][C]97220.5882352941[/C][C]-7810.58823529411[/C][/ROW]
[ROW][C]124[/C][C]112749[/C][C]167104.633333333[/C][C]-54355.6333333333[/C][/ROW]
[ROW][C]125[/C][C]60373[/C][C]58700.3076923077[/C][C]1672.6923076923[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]11776.7647058824[/C][C]7987.23529411765[/C][/ROW]
[ROW][C]127[/C][C]160995[/C][C]167104.633333333[/C][C]-6109.63333333333[/C][/ROW]
[ROW][C]128[/C][C]121052[/C][C]97220.5882352941[/C][C]23831.4117647059[/C][/ROW]
[ROW][C]129[/C][C]150039[/C][C]142903.5[/C][C]7135.5[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]11776.7647058824[/C][C]19.2352941176468[/C][/ROW]
[ROW][C]131[/C][C]10674[/C][C]11776.7647058824[/C][C]-1102.76470588235[/C][/ROW]
[ROW][C]132[/C][C]134836[/C][C]120212.875[/C][C]14623.125[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]11776.7647058824[/C][C]-4940.76470588235[/C][/ROW]
[ROW][C]134[/C][C]153278[/C][C]167104.633333333[/C][C]-13826.6333333333[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]11776.7647058824[/C][C]-6658.76470588235[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]11776.7647058824[/C][C]28471.2352941176[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]11776.7647058824[/C][C]-11776.7647058824[/C][/ROW]
[ROW][C]138[/C][C]117842[/C][C]97220.5882352941[/C][C]20621.4117647059[/C][/ROW]
[ROW][C]139[/C][C]87635[/C][C]120212.875[/C][C]-32577.875[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]11776.7647058824[/C][C]-4645.76470588235[/C][/ROW]
[ROW][C]141[/C][C]8812[/C][C]11776.7647058824[/C][C]-2964.76470588235[/C][/ROW]
[ROW][C]142[/C][C]68916[/C][C]97220.5882352941[/C][C]-28304.5882352941[/C][/ROW]
[ROW][C]143[/C][C]132686[/C][C]142903.5[/C][C]-10217.5[/C][/ROW]
[ROW][C]144[/C][C]94127[/C][C]97220.5882352941[/C][C]-3093.58823529411[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158738&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158738&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	158147	167104.633333333	-8957.63333333333
2	182462	142903.5	39558.5
3	7215	11776.7647058824	-4561.76470588235
4	122259	167104.633333333	-44845.6333333333
5	222405	248925.428571429	-26520.4285714286
6	485890	248925.428571429	236964.571428571
7	150777	120212.875	30564.125
8	160529	142903.5	17625.5
9	133238	142903.5	-9665.5
10	275326	248925.428571429	26400.5714285714
11	121821	167104.633333333	-45283.6333333333
12	172489	167104.633333333	5384.36666666667
13	89942	120212.875	-30270.875
14	208851	248925.428571429	-40074.4285714286
15	151886	142903.5	8982.5
16	145427	142903.5	2523.5
17	134153	142903.5	-8750.5
18	64149	97220.5882352941	-33071.5882352941
19	122417	206543.285714286	-84126.2857142857
20	27997	58700.3076923077	-30703.3076923077
21	65004	97220.5882352941	-32216.5882352941
22	205417	206543.285714286	-1126.28571428571
23	188103	167104.633333333	20998.3666666667
24	118698	167104.633333333	-48406.6333333333
25	143682	167104.633333333	-23422.6333333333
26	140172	120212.875	19959.125
27	186377	167104.633333333	19272.3666666667
28	174870	142903.5	31966.5
29	317699	206543.285714286	111155.714285714
30	192335	167104.633333333	25230.3666666667
31	151621	142903.5	8717.5
32	167466	142903.5	24562.5
33	125909	167104.633333333	-41195.6333333333
34	221896	248925.428571429	-27029.4285714286
35	217447	206543.285714286	10903.7142857143
36	0	11776.7647058824	-11776.7647058824
37	207163	167104.633333333	40058.3666666667
38	93107	120212.875	-27105.875
39	133763	167104.633333333	-33341.6333333333
40	246427	167104.633333333	79322.3666666667
41	224097	206543.285714286	17553.7142857143
42	142057	120212.875	21844.125
43	94332	97220.5882352941	-2888.58823529411
44	171724	167104.633333333	4619.36666666667
45	101683	97220.5882352941	4462.41176470589
46	156753	120212.875	36540.125
47	81293	58700.3076923077	22592.6923076923
48	201984	167104.633333333	34879.3666666667
49	219875	206543.285714286	13331.7142857143
50	156589	206543.285714286	-49954.2857142857
51	48188	58700.3076923077	-10512.3076923077
52	138146	167104.633333333	-28958.6333333333
53	279590	206543.285714286	73046.7142857143
54	234829	167104.633333333	67724.3666666667
55	181731	142903.5	38827.5
56	141014	120212.875	20801.125
57	189220	167104.633333333	22115.3666666667
58	76419	58700.3076923077	17718.6923076923
59	151898	142903.5	8994.5
60	189402	248925.428571429	-59523.4285714286
61	140189	142903.5	-2714.5
62	123181	167104.633333333	-43923.6333333333
63	124234	120212.875	4021.125
64	107277	142903.5	-35626.5
65	153813	120212.875	33600.125
66	94982	120212.875	-25230.875
67	178613	206543.285714286	-27930.2857142857
68	138708	248925.428571429	-110217.428571429
69	102378	97220.5882352941	5157.41176470589
70	31970	11776.7647058824	20193.2352941176
71	211635	206543.285714286	5091.71428571429
72	111885	120212.875	-8327.875
73	99687	142903.5	-43216.5
74	102900	120212.875	-17312.875
75	156475	206543.285714286	-50068.2857142857
76	74513	120212.875	-45699.875
77	159186	167104.633333333	-7918.63333333333
78	155818	206543.285714286	-50725.2857142857
79	60138	58700.3076923077	1437.6923076923
80	84971	120212.875	-35241.875
81	80478	97220.5882352941	-16742.5882352941
82	244325	167104.633333333	77220.3666666667
83	56486	58700.3076923077	-2214.3076923077
84	110743	120212.875	-9469.875
85	75092	97220.5882352941	-22128.5882352941
86	148286	120212.875	28073.125
87	222914	206543.285714286	16370.7142857143
88	115019	142903.5	-27884.5
89	93083	120212.875	-27129.875
90	143258	120212.875	23045.125
91	117794	120212.875	-2418.875
92	158586	167104.633333333	-8518.63333333333
93	151465	120212.875	31252.125
94	124626	120212.875	4413.125
95	51801	58700.3076923077	-6899.3076923077
96	223020	206543.285714286	16476.7142857143
97	188957	167104.633333333	21852.3666666667
98	19349	11776.7647058824	7572.23529411765
99	188069	167104.633333333	20964.3666666667
100	150561	167104.633333333	-16543.6333333333
101	53921	58700.3076923077	-4779.3076923077
102	58280	58700.3076923077	-420.307692307695
103	124951	142903.5	-17952.5
104	112263	120212.875	-7949.875
105	72904	58700.3076923077	14203.6923076923
106	27676	11776.7647058824	15899.2352941176
107	131274	142903.5	-11629.5
108	117451	97220.5882352941	20230.4117647059
109	0	11776.7647058824	-11776.7647058824
110	85610	97220.5882352941	-11610.5882352941
111	107175	97220.5882352941	9954.41176470589
112	133024	142903.5	-9879.5
113	136473	142903.5	-6430.5
114	71894	58700.3076923077	13193.6923076923
115	3616	11776.7647058824	-8160.76470588235
116	0	11776.7647058824	-11776.7647058824
117	154806	97220.5882352941	57585.4117647059
118	137977	142903.5	-4926.5
119	149846	167104.633333333	-17258.6333333333
120	113245	97220.5882352941	16024.4117647059
121	43410	58700.3076923077	-15290.3076923077
122	170330	167104.633333333	3225.36666666667
123	89410	97220.5882352941	-7810.58823529411
124	112749	167104.633333333	-54355.6333333333
125	60373	58700.3076923077	1672.6923076923
126	19764	11776.7647058824	7987.23529411765
127	160995	167104.633333333	-6109.63333333333
128	121052	97220.5882352941	23831.4117647059
129	150039	142903.5	7135.5
130	11796	11776.7647058824	19.2352941176468
131	10674	11776.7647058824	-1102.76470588235
132	134836	120212.875	14623.125
133	6836	11776.7647058824	-4940.76470588235
134	153278	167104.633333333	-13826.6333333333
135	5118	11776.7647058824	-6658.76470588235
136	40248	11776.7647058824	28471.2352941176
137	0	11776.7647058824	-11776.7647058824
138	117842	97220.5882352941	20621.4117647059
139	87635	120212.875	-32577.875
140	7131	11776.7647058824	-4645.76470588235
141	8812	11776.7647058824	-2964.76470588235
142	68916	97220.5882352941	-28304.5882352941
143	132686	142903.5	-10217.5
144	94127	97220.5882352941	-3093.58823529411

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