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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Wed, 14 Dec 2011 10:11:12 -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/14/t1323875531i7z75nf5zy8vbzd.htm/, Retrieved Tue, 10 Feb 2026 13:13:50 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155041, Retrieved Tue, 10 Feb 2026 13:13:50 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

341

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]
- RMP   [Kendall tau Correlation Matrix] [ws10.1] [2011-12-14 12:44:38] [8ae0a4da1b3ee81f40dbba5e42914d07]
-    D    [Kendall tau Correlation Matrix] [ws10.3] [2011-12-14 14:10:41] [8ae0a4da1b3ee81f40dbba5e42914d07]
- RMP         [Recursive Partitioning (Regression Trees)] [ws10.6] [2011-12-14 15:11:12] [d6b8e0ceefc1e2de0b53f6dffb5d636c] [Current]
-   PD          [Recursive Partitioning (Regression Trees)] [ws10.9] [2011-12-21 10:12:51] [8ae0a4da1b3ee81f40dbba5e42914d07] 
-    D            [Recursive Partitioning (Regression Trees)] [ws10.11] [2011-12-21 10:30:44] [8ae0a4da1b3ee81f40dbba5e42914d07] 
-    D            [Recursive Partitioning (Regression Trees)] [ws10.12] [2011-12-21 10:34:17] [8ae0a4da1b3ee81f40dbba5e42914d07] 
-   P               [Recursive Partitioning (Regression Trees)] [ws10.16] [2011-12-21 12:49:59] [8ae0a4da1b3ee81f40dbba5e42914d07] 
- R P                 [Recursive Partitioning (Regression Trees)] [ws10.17] [2011-12-21 12:50:57] [8ae0a4da1b3ee81f40dbba5e42914d07] 
-   P                   [Recursive Partitioning (Regression Trees)] [ws10.18] [2011-12-21 12:51:53] [8ae0a4da1b3ee81f40dbba5e42914d07] 
- RMPD            [Multiple Regression] [ws10.13] [2011-12-21 11:21:32] [8ae0a4da1b3ee81f40dbba5e42914d07] 
- RMPD            [Kendall tau Correlation Matrix] [ws10.14] [2011-12-21 11:27:12] [8ae0a4da1b3ee81f40dbba5e42914d07] 
- RMPD            [Kendall tau Correlation Matrix] [ws10.15] [2011-12-21 11:30:26] [8ae0a4da1b3ee81f40dbba5e42914d07] 
- RMPD            [Skewness and Kurtosis Test] [Paper, Decomposit...] [2011-12-21 11:46:07] [75512e061a94450f738c2449abbaac12] 
- RMPD            [Mean versus Median] [Paper, DSTSM mean...] [2011-12-21 12:15:11] [75512e061a94450f738c2449abbaac12] 

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Dataseries X:

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Histogram

Boxplots

127476	20	17	59	22622
130358	38	17	50	73570
7215	0	0	0	1929
112861	49	22	51	36294
210171	74	30	112	62378
393802	104	31	118	167760
117604	37	19	59	52443
126029	53	25	90	57283
99729	42	30	50	36614
256310	62	26	79	93268
113066	50	20	49	35439
156212	65	25	74	72405
69952	28	15	32	24044
152673	48	22	82	55909
125841	42	12	43	44689
125769	47	19	65	49319
123467	71	28	111	62075
56232	0	12	36	2341
108244	50	28	89	40551
22762	12	13	28	11621
48554	16	14	35	18741
178697	76	27	78	84202
139115	29	25	67	15334
93773	38	30	61	28024
133398	50	21	58	53306
113933	33	17	49	37918
144781	45	22	77	54819
140711	59	28	71	89058
283337	49	25	82	103354
158146	40	16	53	70239
123344	40	23	71	33045
157640	51	20	58	63852
91279	41	11	25	30905
189374	73	20	59	24242
167915	43	21	77	78907
0	0	0	0	0
175403	46	27	75	36005
92342	44	14	39	31972
100023	31	29	83	35853
178277	71	31	123	115301
145062	61	19	67	47689
110980	28	30	105	34223
86039	21	23	76	43431
120821	42	20	54	52220
95535	44	22	82	33863
109894	34	19	57	46879
61554	15	32	57	23228
156520	46	18	72	42827
159121	43	26	94	65765
129362	47	25	72	38167
48188	12	22	39	14812
91198	42	19	60	32615
229864	56	24	84	82188
180317	41	26	69	51763
150640	48	27	102	59325
104416	30	10	28	48976
165098	44	26	65	43384
63205	25	23	67	26692
100056	42	21	80	53279
137214	28	34	79	20652
99630	33	29	107	38338
84557	32	18	57	36735
91199	28	16	44	42764
83419	31	23	59	44331
101723	13	22	80	41354
94982	38	29	89	47879
129700	39	31	115	103793
110708	68	21	59	52235
81518	32	21	66	49825
31970	5	21	42	4105
192268	53	15	35	58687
87611	33	9	3	40745
77890	48	21	68	33187
83261	36	18	38	14063
116290	52	31	107	37407
56544	0	25	73	7190
116173	52	24	80	49562
111488	45	22	69	76324
60138	16	21	46	21928
73422	33	26	52	27860
67751	48	22	58	28078
213351	33	26	85	49577
51185	24	20	13	28145
97181	37	25	61	36241
45100	17	19	49	10824
115801	32	22	47	46892
185664	55	25	93	61264
71960	39	22	65	22933
76441	29	21	64	20787
103613	26	20	64	43978
98707	37	23	57	51305
126527	58	22	61	55593
136781	35	21	71	51648
105863	24	12	43	30552
38775	18	9	18	23470
179984	37	32	103	77530
164808	86	24	76	57299
19349	13	1	0	9604
146824	20	24	83	34684
108660	32	22	70	41094
43803	8	4	4	3439
47062	38	15	41	25171
110845	45	21	57	23437
92517	24	23	52	34086
58660	23	12	24	24649
27676	2	16	17	2342
98550	52	24	89	45571
43284	5	9	20	3255
0	0	0	0	0
66016	43	22	45	30002
57359	18	17	63	19360
96933	41	18	48	43320
70369	45	21	70	35513
65494	29	17	32	23536
3616	0	0	0	0
0	0	0	0	0
143931	32	20	72	54438
109894	58	26	56	56812
122973	17	26	64	33838
84336	24	20	77	32366
43410	7	1	3	13
136250	62	24	73	55082
79015	30	14	37	31334
92937	49	26	54	16612
57586	3	12	32	5084
19764	10	2	4	9927
105757	42	16	55	47413
96410	18	22	81	27389
113402	40	28	90	30425
11796	1	2	1	0
7627	0	0	0	0
121085	29	17	38	33510
6836	0	1	0	0
139563	46	17	36	40389
5118	5	0	0	0
40248	8	4	7	6012
0	0	0	0	0
95079	21	25	75	22205
80750	21	26	52	17231
7131	0	0	0	0
4194	0	0	0	0
60378	15	15	45	11017
96971	40	18	60	46741
83484	17	19	48	39869

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	14 seconds
R Server	'AstonUniversity' @ aston.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 14 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155041&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155041&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155041&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	14 seconds
R Server	'AstonUniversity' @ aston.wessa.net
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.8544
R-squared	0.73
RMSE	30614.3851

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8544 \tabularnewline
R-squared & 0.73 \tabularnewline
RMSE & 30614.3851 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155041&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8544[/C][/ROW]
[ROW][C]R-squared[/C][C]0.73[/C][/ROW]
[ROW][C]RMSE[/C][C]30614.3851[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155041&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155041&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	0.8544
R-squared	0.73
RMSE	30614.3851

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	127476	90379	37097
2	130358	160563	-30205
3	7215	4401.36363636364	2813.63636363636
4	112861	116200.327868852	-3339.32786885246
5	210171	160563	49608
6	393802	223837.25	169964.75
7	117604	116200.327868852	1403.67213114754
8	126029	116200.327868852	9828.67213114754
9	99729	116200.327868852	-16471.3278688525
10	256310	223837.25	32472.75
11	113066	116200.327868852	-3134.32786885246
12	156212	160563	-4351
13	69952	90379	-20427
14	152673	116200.327868852	36472.6721311475
15	125841	116200.327868852	9640.67213114754
16	125769	116200.327868852	9568.67213114754
17	123467	160563	-37096
18	56232	49909.6363636364	6322.36363636364
19	108244	116200.327868852	-7956.32786885246
20	22762	49909.6363636364	-27147.6363636364
21	48554	49909.6363636364	-1355.63636363636
22	178697	223837.25	-45140.25
23	139115	90379	48736
24	93773	90379	3394
25	133398	116200.327868852	17197.6721311475
26	113933	116200.327868852	-2267.32786885246
27	144781	116200.327868852	28580.6721311475
28	140711	223837.25	-83126.25
29	283337	223837.25	59499.75
30	158146	160563	-2417
31	123344	116200.327868852	7143.67213114754
32	157640	160563	-2923
33	91279	90379	900
34	189374	90379	98995
35	167915	160563	7352
36	0	4401.36363636364	-4401.36363636364
37	175403	116200.327868852	59202.6721311475
38	92342	90379	1963
39	100023	116200.327868852	-16177.3278688525
40	178277	223837.25	-45560.25
41	145062	116200.327868852	28861.6721311475
42	110980	116200.327868852	-5220.32786885246
43	86039	116200.327868852	-30161.3278688525
44	120821	116200.327868852	4620.67213114754
45	95535	116200.327868852	-20665.3278688525
46	109894	116200.327868852	-6306.32786885246
47	61554	49909.6363636364	11644.3636363636
48	156520	116200.327868852	40319.6721311475
49	159121	160563	-1442
50	129362	116200.327868852	13161.6721311475
51	48188	49909.6363636364	-1721.63636363636
52	91198	90379	819
53	229864	223837.25	6026.75
54	180317	116200.327868852	64116.6721311475
55	150640	160563	-9923
56	104416	116200.327868852	-11784.3278688525
57	165098	116200.327868852	48897.6721311475
58	63205	90379	-27174
59	100056	116200.327868852	-16144.3278688525
60	137214	90379	46835
61	99630	116200.327868852	-16570.3278688525
62	84557	116200.327868852	-31643.3278688525
63	91199	116200.327868852	-25001.3278688525
64	83419	116200.327868852	-32781.3278688525
65	101723	116200.327868852	-14477.3278688525
66	94982	116200.327868852	-21218.3278688525
67	129700	223837.25	-94137.25
68	110708	116200.327868852	-5492.32786885246
69	81518	116200.327868852	-34682.3278688525
70	31970	49909.6363636364	-17939.6363636364
71	192268	160563	31705
72	87611	116200.327868852	-28589.3278688525
73	77890	116200.327868852	-38310.3278688525
74	83261	90379	-7118
75	116290	116200.327868852	89.6721311475412
76	56544	49909.6363636364	6634.36363636364
77	116173	116200.327868852	-27.3278688524588
78	111488	160563	-49075
79	60138	49909.6363636364	10228.3636363636
80	73422	90379	-16957
81	67751	90379	-22628
82	213351	116200.327868852	97150.6721311475
83	51185	33261.2	17923.8
84	97181	116200.327868852	-19019.3278688525
85	45100	49909.6363636364	-4809.63636363636
86	115801	116200.327868852	-399.327868852459
87	185664	160563	25101
88	71960	90379	-18419
89	76441	90379	-13938
90	103613	116200.327868852	-12587.3278688525
91	98707	116200.327868852	-17493.3278688525
92	126527	116200.327868852	10326.6721311475
93	136781	116200.327868852	20580.6721311475
94	105863	90379	15484
95	38775	33261.2	5513.8
96	179984	160563	19421
97	164808	160563	4245
98	19349	33261.2	-13912.2
99	146824	116200.327868852	30623.6721311475
100	108660	116200.327868852	-7540.32786885246
101	43803	33261.2	10541.8
102	47062	90379	-43317
103	110845	90379	20466
104	92517	116200.327868852	-23683.3278688525
105	58660	90379	-31719
106	27676	33261.2	-5585.2
107	98550	116200.327868852	-17650.3278688525
108	43284	33261.2	10022.8
109	0	4401.36363636364	-4401.36363636364
110	66016	90379	-24363
111	57359	90379	-33020
112	96933	116200.327868852	-19267.3278688525
113	70369	116200.327868852	-45831.3278688525
114	65494	90379	-24885
115	3616	4401.36363636364	-785.363636363636
116	0	4401.36363636364	-4401.36363636364
117	143931	116200.327868852	27730.6721311475
118	109894	116200.327868852	-6306.32786885246
119	122973	116200.327868852	6772.67213114754
120	84336	90379	-6043
121	43410	33261.2	10148.8
122	136250	116200.327868852	20049.6721311475
123	79015	90379	-11364
124	92937	90379	2558
125	57586	49909.6363636364	7676.36363636364
126	19764	33261.2	-13497.2
127	105757	116200.327868852	-10443.3278688525
128	96410	90379	6031
129	113402	90379	23023
130	11796	4401.36363636364	7394.63636363636
131	7627	4401.36363636364	3225.63636363636
132	121085	116200.327868852	4884.67213114754
133	6836	4401.36363636364	2434.63636363636
134	139563	116200.327868852	23362.6721311475
135	5118	33261.2	-28143.2
136	40248	33261.2	6986.8
137	0	4401.36363636364	-4401.36363636364
138	95079	90379	4700
139	80750	90379	-9629
140	7131	4401.36363636364	2729.63636363636
141	4194	4401.36363636364	-207.363636363636
142	60378	49909.6363636364	10468.3636363636
143	96971	116200.327868852	-19229.3278688525
144	83484	116200.327868852	-32716.3278688525

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 127476 & 90379 & 37097 \tabularnewline
2 & 130358 & 160563 & -30205 \tabularnewline
3 & 7215 & 4401.36363636364 & 2813.63636363636 \tabularnewline
4 & 112861 & 116200.327868852 & -3339.32786885246 \tabularnewline
5 & 210171 & 160563 & 49608 \tabularnewline
6 & 393802 & 223837.25 & 169964.75 \tabularnewline
7 & 117604 & 116200.327868852 & 1403.67213114754 \tabularnewline
8 & 126029 & 116200.327868852 & 9828.67213114754 \tabularnewline
9 & 99729 & 116200.327868852 & -16471.3278688525 \tabularnewline
10 & 256310 & 223837.25 & 32472.75 \tabularnewline
11 & 113066 & 116200.327868852 & -3134.32786885246 \tabularnewline
12 & 156212 & 160563 & -4351 \tabularnewline
13 & 69952 & 90379 & -20427 \tabularnewline
14 & 152673 & 116200.327868852 & 36472.6721311475 \tabularnewline
15 & 125841 & 116200.327868852 & 9640.67213114754 \tabularnewline
16 & 125769 & 116200.327868852 & 9568.67213114754 \tabularnewline
17 & 123467 & 160563 & -37096 \tabularnewline
18 & 56232 & 49909.6363636364 & 6322.36363636364 \tabularnewline
19 & 108244 & 116200.327868852 & -7956.32786885246 \tabularnewline
20 & 22762 & 49909.6363636364 & -27147.6363636364 \tabularnewline
21 & 48554 & 49909.6363636364 & -1355.63636363636 \tabularnewline
22 & 178697 & 223837.25 & -45140.25 \tabularnewline
23 & 139115 & 90379 & 48736 \tabularnewline
24 & 93773 & 90379 & 3394 \tabularnewline
25 & 133398 & 116200.327868852 & 17197.6721311475 \tabularnewline
26 & 113933 & 116200.327868852 & -2267.32786885246 \tabularnewline
27 & 144781 & 116200.327868852 & 28580.6721311475 \tabularnewline
28 & 140711 & 223837.25 & -83126.25 \tabularnewline
29 & 283337 & 223837.25 & 59499.75 \tabularnewline
30 & 158146 & 160563 & -2417 \tabularnewline
31 & 123344 & 116200.327868852 & 7143.67213114754 \tabularnewline
32 & 157640 & 160563 & -2923 \tabularnewline
33 & 91279 & 90379 & 900 \tabularnewline
34 & 189374 & 90379 & 98995 \tabularnewline
35 & 167915 & 160563 & 7352 \tabularnewline
36 & 0 & 4401.36363636364 & -4401.36363636364 \tabularnewline
37 & 175403 & 116200.327868852 & 59202.6721311475 \tabularnewline
38 & 92342 & 90379 & 1963 \tabularnewline
39 & 100023 & 116200.327868852 & -16177.3278688525 \tabularnewline
40 & 178277 & 223837.25 & -45560.25 \tabularnewline
41 & 145062 & 116200.327868852 & 28861.6721311475 \tabularnewline
42 & 110980 & 116200.327868852 & -5220.32786885246 \tabularnewline
43 & 86039 & 116200.327868852 & -30161.3278688525 \tabularnewline
44 & 120821 & 116200.327868852 & 4620.67213114754 \tabularnewline
45 & 95535 & 116200.327868852 & -20665.3278688525 \tabularnewline
46 & 109894 & 116200.327868852 & -6306.32786885246 \tabularnewline
47 & 61554 & 49909.6363636364 & 11644.3636363636 \tabularnewline
48 & 156520 & 116200.327868852 & 40319.6721311475 \tabularnewline
49 & 159121 & 160563 & -1442 \tabularnewline
50 & 129362 & 116200.327868852 & 13161.6721311475 \tabularnewline
51 & 48188 & 49909.6363636364 & -1721.63636363636 \tabularnewline
52 & 91198 & 90379 & 819 \tabularnewline
53 & 229864 & 223837.25 & 6026.75 \tabularnewline
54 & 180317 & 116200.327868852 & 64116.6721311475 \tabularnewline
55 & 150640 & 160563 & -9923 \tabularnewline
56 & 104416 & 116200.327868852 & -11784.3278688525 \tabularnewline
57 & 165098 & 116200.327868852 & 48897.6721311475 \tabularnewline
58 & 63205 & 90379 & -27174 \tabularnewline
59 & 100056 & 116200.327868852 & -16144.3278688525 \tabularnewline
60 & 137214 & 90379 & 46835 \tabularnewline
61 & 99630 & 116200.327868852 & -16570.3278688525 \tabularnewline
62 & 84557 & 116200.327868852 & -31643.3278688525 \tabularnewline
63 & 91199 & 116200.327868852 & -25001.3278688525 \tabularnewline
64 & 83419 & 116200.327868852 & -32781.3278688525 \tabularnewline
65 & 101723 & 116200.327868852 & -14477.3278688525 \tabularnewline
66 & 94982 & 116200.327868852 & -21218.3278688525 \tabularnewline
67 & 129700 & 223837.25 & -94137.25 \tabularnewline
68 & 110708 & 116200.327868852 & -5492.32786885246 \tabularnewline
69 & 81518 & 116200.327868852 & -34682.3278688525 \tabularnewline
70 & 31970 & 49909.6363636364 & -17939.6363636364 \tabularnewline
71 & 192268 & 160563 & 31705 \tabularnewline
72 & 87611 & 116200.327868852 & -28589.3278688525 \tabularnewline
73 & 77890 & 116200.327868852 & -38310.3278688525 \tabularnewline
74 & 83261 & 90379 & -7118 \tabularnewline
75 & 116290 & 116200.327868852 & 89.6721311475412 \tabularnewline
76 & 56544 & 49909.6363636364 & 6634.36363636364 \tabularnewline
77 & 116173 & 116200.327868852 & -27.3278688524588 \tabularnewline
78 & 111488 & 160563 & -49075 \tabularnewline
79 & 60138 & 49909.6363636364 & 10228.3636363636 \tabularnewline
80 & 73422 & 90379 & -16957 \tabularnewline
81 & 67751 & 90379 & -22628 \tabularnewline
82 & 213351 & 116200.327868852 & 97150.6721311475 \tabularnewline
83 & 51185 & 33261.2 & 17923.8 \tabularnewline
84 & 97181 & 116200.327868852 & -19019.3278688525 \tabularnewline
85 & 45100 & 49909.6363636364 & -4809.63636363636 \tabularnewline
86 & 115801 & 116200.327868852 & -399.327868852459 \tabularnewline
87 & 185664 & 160563 & 25101 \tabularnewline
88 & 71960 & 90379 & -18419 \tabularnewline
89 & 76441 & 90379 & -13938 \tabularnewline
90 & 103613 & 116200.327868852 & -12587.3278688525 \tabularnewline
91 & 98707 & 116200.327868852 & -17493.3278688525 \tabularnewline
92 & 126527 & 116200.327868852 & 10326.6721311475 \tabularnewline
93 & 136781 & 116200.327868852 & 20580.6721311475 \tabularnewline
94 & 105863 & 90379 & 15484 \tabularnewline
95 & 38775 & 33261.2 & 5513.8 \tabularnewline
96 & 179984 & 160563 & 19421 \tabularnewline
97 & 164808 & 160563 & 4245 \tabularnewline
98 & 19349 & 33261.2 & -13912.2 \tabularnewline
99 & 146824 & 116200.327868852 & 30623.6721311475 \tabularnewline
100 & 108660 & 116200.327868852 & -7540.32786885246 \tabularnewline
101 & 43803 & 33261.2 & 10541.8 \tabularnewline
102 & 47062 & 90379 & -43317 \tabularnewline
103 & 110845 & 90379 & 20466 \tabularnewline
104 & 92517 & 116200.327868852 & -23683.3278688525 \tabularnewline
105 & 58660 & 90379 & -31719 \tabularnewline
106 & 27676 & 33261.2 & -5585.2 \tabularnewline
107 & 98550 & 116200.327868852 & -17650.3278688525 \tabularnewline
108 & 43284 & 33261.2 & 10022.8 \tabularnewline
109 & 0 & 4401.36363636364 & -4401.36363636364 \tabularnewline
110 & 66016 & 90379 & -24363 \tabularnewline
111 & 57359 & 90379 & -33020 \tabularnewline
112 & 96933 & 116200.327868852 & -19267.3278688525 \tabularnewline
113 & 70369 & 116200.327868852 & -45831.3278688525 \tabularnewline
114 & 65494 & 90379 & -24885 \tabularnewline
115 & 3616 & 4401.36363636364 & -785.363636363636 \tabularnewline
116 & 0 & 4401.36363636364 & -4401.36363636364 \tabularnewline
117 & 143931 & 116200.327868852 & 27730.6721311475 \tabularnewline
118 & 109894 & 116200.327868852 & -6306.32786885246 \tabularnewline
119 & 122973 & 116200.327868852 & 6772.67213114754 \tabularnewline
120 & 84336 & 90379 & -6043 \tabularnewline
121 & 43410 & 33261.2 & 10148.8 \tabularnewline
122 & 136250 & 116200.327868852 & 20049.6721311475 \tabularnewline
123 & 79015 & 90379 & -11364 \tabularnewline
124 & 92937 & 90379 & 2558 \tabularnewline
125 & 57586 & 49909.6363636364 & 7676.36363636364 \tabularnewline
126 & 19764 & 33261.2 & -13497.2 \tabularnewline
127 & 105757 & 116200.327868852 & -10443.3278688525 \tabularnewline
128 & 96410 & 90379 & 6031 \tabularnewline
129 & 113402 & 90379 & 23023 \tabularnewline
130 & 11796 & 4401.36363636364 & 7394.63636363636 \tabularnewline
131 & 7627 & 4401.36363636364 & 3225.63636363636 \tabularnewline
132 & 121085 & 116200.327868852 & 4884.67213114754 \tabularnewline
133 & 6836 & 4401.36363636364 & 2434.63636363636 \tabularnewline
134 & 139563 & 116200.327868852 & 23362.6721311475 \tabularnewline
135 & 5118 & 33261.2 & -28143.2 \tabularnewline
136 & 40248 & 33261.2 & 6986.8 \tabularnewline
137 & 0 & 4401.36363636364 & -4401.36363636364 \tabularnewline
138 & 95079 & 90379 & 4700 \tabularnewline
139 & 80750 & 90379 & -9629 \tabularnewline
140 & 7131 & 4401.36363636364 & 2729.63636363636 \tabularnewline
141 & 4194 & 4401.36363636364 & -207.363636363636 \tabularnewline
142 & 60378 & 49909.6363636364 & 10468.3636363636 \tabularnewline
143 & 96971 & 116200.327868852 & -19229.3278688525 \tabularnewline
144 & 83484 & 116200.327868852 & -32716.3278688525 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155041&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]127476[/C][C]90379[/C][C]37097[/C][/ROW]
[ROW][C]2[/C][C]130358[/C][C]160563[/C][C]-30205[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]4401.36363636364[/C][C]2813.63636363636[/C][/ROW]
[ROW][C]4[/C][C]112861[/C][C]116200.327868852[/C][C]-3339.32786885246[/C][/ROW]
[ROW][C]5[/C][C]210171[/C][C]160563[/C][C]49608[/C][/ROW]
[ROW][C]6[/C][C]393802[/C][C]223837.25[/C][C]169964.75[/C][/ROW]
[ROW][C]7[/C][C]117604[/C][C]116200.327868852[/C][C]1403.67213114754[/C][/ROW]
[ROW][C]8[/C][C]126029[/C][C]116200.327868852[/C][C]9828.67213114754[/C][/ROW]
[ROW][C]9[/C][C]99729[/C][C]116200.327868852[/C][C]-16471.3278688525[/C][/ROW]
[ROW][C]10[/C][C]256310[/C][C]223837.25[/C][C]32472.75[/C][/ROW]
[ROW][C]11[/C][C]113066[/C][C]116200.327868852[/C][C]-3134.32786885246[/C][/ROW]
[ROW][C]12[/C][C]156212[/C][C]160563[/C][C]-4351[/C][/ROW]
[ROW][C]13[/C][C]69952[/C][C]90379[/C][C]-20427[/C][/ROW]
[ROW][C]14[/C][C]152673[/C][C]116200.327868852[/C][C]36472.6721311475[/C][/ROW]
[ROW][C]15[/C][C]125841[/C][C]116200.327868852[/C][C]9640.67213114754[/C][/ROW]
[ROW][C]16[/C][C]125769[/C][C]116200.327868852[/C][C]9568.67213114754[/C][/ROW]
[ROW][C]17[/C][C]123467[/C][C]160563[/C][C]-37096[/C][/ROW]
[ROW][C]18[/C][C]56232[/C][C]49909.6363636364[/C][C]6322.36363636364[/C][/ROW]
[ROW][C]19[/C][C]108244[/C][C]116200.327868852[/C][C]-7956.32786885246[/C][/ROW]
[ROW][C]20[/C][C]22762[/C][C]49909.6363636364[/C][C]-27147.6363636364[/C][/ROW]
[ROW][C]21[/C][C]48554[/C][C]49909.6363636364[/C][C]-1355.63636363636[/C][/ROW]
[ROW][C]22[/C][C]178697[/C][C]223837.25[/C][C]-45140.25[/C][/ROW]
[ROW][C]23[/C][C]139115[/C][C]90379[/C][C]48736[/C][/ROW]
[ROW][C]24[/C][C]93773[/C][C]90379[/C][C]3394[/C][/ROW]
[ROW][C]25[/C][C]133398[/C][C]116200.327868852[/C][C]17197.6721311475[/C][/ROW]
[ROW][C]26[/C][C]113933[/C][C]116200.327868852[/C][C]-2267.32786885246[/C][/ROW]
[ROW][C]27[/C][C]144781[/C][C]116200.327868852[/C][C]28580.6721311475[/C][/ROW]
[ROW][C]28[/C][C]140711[/C][C]223837.25[/C][C]-83126.25[/C][/ROW]
[ROW][C]29[/C][C]283337[/C][C]223837.25[/C][C]59499.75[/C][/ROW]
[ROW][C]30[/C][C]158146[/C][C]160563[/C][C]-2417[/C][/ROW]
[ROW][C]31[/C][C]123344[/C][C]116200.327868852[/C][C]7143.67213114754[/C][/ROW]
[ROW][C]32[/C][C]157640[/C][C]160563[/C][C]-2923[/C][/ROW]
[ROW][C]33[/C][C]91279[/C][C]90379[/C][C]900[/C][/ROW]
[ROW][C]34[/C][C]189374[/C][C]90379[/C][C]98995[/C][/ROW]
[ROW][C]35[/C][C]167915[/C][C]160563[/C][C]7352[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]4401.36363636364[/C][C]-4401.36363636364[/C][/ROW]
[ROW][C]37[/C][C]175403[/C][C]116200.327868852[/C][C]59202.6721311475[/C][/ROW]
[ROW][C]38[/C][C]92342[/C][C]90379[/C][C]1963[/C][/ROW]
[ROW][C]39[/C][C]100023[/C][C]116200.327868852[/C][C]-16177.3278688525[/C][/ROW]
[ROW][C]40[/C][C]178277[/C][C]223837.25[/C][C]-45560.25[/C][/ROW]
[ROW][C]41[/C][C]145062[/C][C]116200.327868852[/C][C]28861.6721311475[/C][/ROW]
[ROW][C]42[/C][C]110980[/C][C]116200.327868852[/C][C]-5220.32786885246[/C][/ROW]
[ROW][C]43[/C][C]86039[/C][C]116200.327868852[/C][C]-30161.3278688525[/C][/ROW]
[ROW][C]44[/C][C]120821[/C][C]116200.327868852[/C][C]4620.67213114754[/C][/ROW]
[ROW][C]45[/C][C]95535[/C][C]116200.327868852[/C][C]-20665.3278688525[/C][/ROW]
[ROW][C]46[/C][C]109894[/C][C]116200.327868852[/C][C]-6306.32786885246[/C][/ROW]
[ROW][C]47[/C][C]61554[/C][C]49909.6363636364[/C][C]11644.3636363636[/C][/ROW]
[ROW][C]48[/C][C]156520[/C][C]116200.327868852[/C][C]40319.6721311475[/C][/ROW]
[ROW][C]49[/C][C]159121[/C][C]160563[/C][C]-1442[/C][/ROW]
[ROW][C]50[/C][C]129362[/C][C]116200.327868852[/C][C]13161.6721311475[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]49909.6363636364[/C][C]-1721.63636363636[/C][/ROW]
[ROW][C]52[/C][C]91198[/C][C]90379[/C][C]819[/C][/ROW]
[ROW][C]53[/C][C]229864[/C][C]223837.25[/C][C]6026.75[/C][/ROW]
[ROW][C]54[/C][C]180317[/C][C]116200.327868852[/C][C]64116.6721311475[/C][/ROW]
[ROW][C]55[/C][C]150640[/C][C]160563[/C][C]-9923[/C][/ROW]
[ROW][C]56[/C][C]104416[/C][C]116200.327868852[/C][C]-11784.3278688525[/C][/ROW]
[ROW][C]57[/C][C]165098[/C][C]116200.327868852[/C][C]48897.6721311475[/C][/ROW]
[ROW][C]58[/C][C]63205[/C][C]90379[/C][C]-27174[/C][/ROW]
[ROW][C]59[/C][C]100056[/C][C]116200.327868852[/C][C]-16144.3278688525[/C][/ROW]
[ROW][C]60[/C][C]137214[/C][C]90379[/C][C]46835[/C][/ROW]
[ROW][C]61[/C][C]99630[/C][C]116200.327868852[/C][C]-16570.3278688525[/C][/ROW]
[ROW][C]62[/C][C]84557[/C][C]116200.327868852[/C][C]-31643.3278688525[/C][/ROW]
[ROW][C]63[/C][C]91199[/C][C]116200.327868852[/C][C]-25001.3278688525[/C][/ROW]
[ROW][C]64[/C][C]83419[/C][C]116200.327868852[/C][C]-32781.3278688525[/C][/ROW]
[ROW][C]65[/C][C]101723[/C][C]116200.327868852[/C][C]-14477.3278688525[/C][/ROW]
[ROW][C]66[/C][C]94982[/C][C]116200.327868852[/C][C]-21218.3278688525[/C][/ROW]
[ROW][C]67[/C][C]129700[/C][C]223837.25[/C][C]-94137.25[/C][/ROW]
[ROW][C]68[/C][C]110708[/C][C]116200.327868852[/C][C]-5492.32786885246[/C][/ROW]
[ROW][C]69[/C][C]81518[/C][C]116200.327868852[/C][C]-34682.3278688525[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]49909.6363636364[/C][C]-17939.6363636364[/C][/ROW]
[ROW][C]71[/C][C]192268[/C][C]160563[/C][C]31705[/C][/ROW]
[ROW][C]72[/C][C]87611[/C][C]116200.327868852[/C][C]-28589.3278688525[/C][/ROW]
[ROW][C]73[/C][C]77890[/C][C]116200.327868852[/C][C]-38310.3278688525[/C][/ROW]
[ROW][C]74[/C][C]83261[/C][C]90379[/C][C]-7118[/C][/ROW]
[ROW][C]75[/C][C]116290[/C][C]116200.327868852[/C][C]89.6721311475412[/C][/ROW]
[ROW][C]76[/C][C]56544[/C][C]49909.6363636364[/C][C]6634.36363636364[/C][/ROW]
[ROW][C]77[/C][C]116173[/C][C]116200.327868852[/C][C]-27.3278688524588[/C][/ROW]
[ROW][C]78[/C][C]111488[/C][C]160563[/C][C]-49075[/C][/ROW]
[ROW][C]79[/C][C]60138[/C][C]49909.6363636364[/C][C]10228.3636363636[/C][/ROW]
[ROW][C]80[/C][C]73422[/C][C]90379[/C][C]-16957[/C][/ROW]
[ROW][C]81[/C][C]67751[/C][C]90379[/C][C]-22628[/C][/ROW]
[ROW][C]82[/C][C]213351[/C][C]116200.327868852[/C][C]97150.6721311475[/C][/ROW]
[ROW][C]83[/C][C]51185[/C][C]33261.2[/C][C]17923.8[/C][/ROW]
[ROW][C]84[/C][C]97181[/C][C]116200.327868852[/C][C]-19019.3278688525[/C][/ROW]
[ROW][C]85[/C][C]45100[/C][C]49909.6363636364[/C][C]-4809.63636363636[/C][/ROW]
[ROW][C]86[/C][C]115801[/C][C]116200.327868852[/C][C]-399.327868852459[/C][/ROW]
[ROW][C]87[/C][C]185664[/C][C]160563[/C][C]25101[/C][/ROW]
[ROW][C]88[/C][C]71960[/C][C]90379[/C][C]-18419[/C][/ROW]
[ROW][C]89[/C][C]76441[/C][C]90379[/C][C]-13938[/C][/ROW]
[ROW][C]90[/C][C]103613[/C][C]116200.327868852[/C][C]-12587.3278688525[/C][/ROW]
[ROW][C]91[/C][C]98707[/C][C]116200.327868852[/C][C]-17493.3278688525[/C][/ROW]
[ROW][C]92[/C][C]126527[/C][C]116200.327868852[/C][C]10326.6721311475[/C][/ROW]
[ROW][C]93[/C][C]136781[/C][C]116200.327868852[/C][C]20580.6721311475[/C][/ROW]
[ROW][C]94[/C][C]105863[/C][C]90379[/C][C]15484[/C][/ROW]
[ROW][C]95[/C][C]38775[/C][C]33261.2[/C][C]5513.8[/C][/ROW]
[ROW][C]96[/C][C]179984[/C][C]160563[/C][C]19421[/C][/ROW]
[ROW][C]97[/C][C]164808[/C][C]160563[/C][C]4245[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]33261.2[/C][C]-13912.2[/C][/ROW]
[ROW][C]99[/C][C]146824[/C][C]116200.327868852[/C][C]30623.6721311475[/C][/ROW]
[ROW][C]100[/C][C]108660[/C][C]116200.327868852[/C][C]-7540.32786885246[/C][/ROW]
[ROW][C]101[/C][C]43803[/C][C]33261.2[/C][C]10541.8[/C][/ROW]
[ROW][C]102[/C][C]47062[/C][C]90379[/C][C]-43317[/C][/ROW]
[ROW][C]103[/C][C]110845[/C][C]90379[/C][C]20466[/C][/ROW]
[ROW][C]104[/C][C]92517[/C][C]116200.327868852[/C][C]-23683.3278688525[/C][/ROW]
[ROW][C]105[/C][C]58660[/C][C]90379[/C][C]-31719[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]33261.2[/C][C]-5585.2[/C][/ROW]
[ROW][C]107[/C][C]98550[/C][C]116200.327868852[/C][C]-17650.3278688525[/C][/ROW]
[ROW][C]108[/C][C]43284[/C][C]33261.2[/C][C]10022.8[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]4401.36363636364[/C][C]-4401.36363636364[/C][/ROW]
[ROW][C]110[/C][C]66016[/C][C]90379[/C][C]-24363[/C][/ROW]
[ROW][C]111[/C][C]57359[/C][C]90379[/C][C]-33020[/C][/ROW]
[ROW][C]112[/C][C]96933[/C][C]116200.327868852[/C][C]-19267.3278688525[/C][/ROW]
[ROW][C]113[/C][C]70369[/C][C]116200.327868852[/C][C]-45831.3278688525[/C][/ROW]
[ROW][C]114[/C][C]65494[/C][C]90379[/C][C]-24885[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]4401.36363636364[/C][C]-785.363636363636[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]4401.36363636364[/C][C]-4401.36363636364[/C][/ROW]
[ROW][C]117[/C][C]143931[/C][C]116200.327868852[/C][C]27730.6721311475[/C][/ROW]
[ROW][C]118[/C][C]109894[/C][C]116200.327868852[/C][C]-6306.32786885246[/C][/ROW]
[ROW][C]119[/C][C]122973[/C][C]116200.327868852[/C][C]6772.67213114754[/C][/ROW]
[ROW][C]120[/C][C]84336[/C][C]90379[/C][C]-6043[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]33261.2[/C][C]10148.8[/C][/ROW]
[ROW][C]122[/C][C]136250[/C][C]116200.327868852[/C][C]20049.6721311475[/C][/ROW]
[ROW][C]123[/C][C]79015[/C][C]90379[/C][C]-11364[/C][/ROW]
[ROW][C]124[/C][C]92937[/C][C]90379[/C][C]2558[/C][/ROW]
[ROW][C]125[/C][C]57586[/C][C]49909.6363636364[/C][C]7676.36363636364[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]33261.2[/C][C]-13497.2[/C][/ROW]
[ROW][C]127[/C][C]105757[/C][C]116200.327868852[/C][C]-10443.3278688525[/C][/ROW]
[ROW][C]128[/C][C]96410[/C][C]90379[/C][C]6031[/C][/ROW]
[ROW][C]129[/C][C]113402[/C][C]90379[/C][C]23023[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]4401.36363636364[/C][C]7394.63636363636[/C][/ROW]
[ROW][C]131[/C][C]7627[/C][C]4401.36363636364[/C][C]3225.63636363636[/C][/ROW]
[ROW][C]132[/C][C]121085[/C][C]116200.327868852[/C][C]4884.67213114754[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]4401.36363636364[/C][C]2434.63636363636[/C][/ROW]
[ROW][C]134[/C][C]139563[/C][C]116200.327868852[/C][C]23362.6721311475[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]33261.2[/C][C]-28143.2[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]33261.2[/C][C]6986.8[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]4401.36363636364[/C][C]-4401.36363636364[/C][/ROW]
[ROW][C]138[/C][C]95079[/C][C]90379[/C][C]4700[/C][/ROW]
[ROW][C]139[/C][C]80750[/C][C]90379[/C][C]-9629[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]4401.36363636364[/C][C]2729.63636363636[/C][/ROW]
[ROW][C]141[/C][C]4194[/C][C]4401.36363636364[/C][C]-207.363636363636[/C][/ROW]
[ROW][C]142[/C][C]60378[/C][C]49909.6363636364[/C][C]10468.3636363636[/C][/ROW]
[ROW][C]143[/C][C]96971[/C][C]116200.327868852[/C][C]-19229.3278688525[/C][/ROW]
[ROW][C]144[/C][C]83484[/C][C]116200.327868852[/C][C]-32716.3278688525[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155041&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155041&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	127476	90379	37097
2	130358	160563	-30205
3	7215	4401.36363636364	2813.63636363636
4	112861	116200.327868852	-3339.32786885246
5	210171	160563	49608
6	393802	223837.25	169964.75
7	117604	116200.327868852	1403.67213114754
8	126029	116200.327868852	9828.67213114754
9	99729	116200.327868852	-16471.3278688525
10	256310	223837.25	32472.75
11	113066	116200.327868852	-3134.32786885246
12	156212	160563	-4351
13	69952	90379	-20427
14	152673	116200.327868852	36472.6721311475
15	125841	116200.327868852	9640.67213114754
16	125769	116200.327868852	9568.67213114754
17	123467	160563	-37096
18	56232	49909.6363636364	6322.36363636364
19	108244	116200.327868852	-7956.32786885246
20	22762	49909.6363636364	-27147.6363636364
21	48554	49909.6363636364	-1355.63636363636
22	178697	223837.25	-45140.25
23	139115	90379	48736
24	93773	90379	3394
25	133398	116200.327868852	17197.6721311475
26	113933	116200.327868852	-2267.32786885246
27	144781	116200.327868852	28580.6721311475
28	140711	223837.25	-83126.25
29	283337	223837.25	59499.75
30	158146	160563	-2417
31	123344	116200.327868852	7143.67213114754
32	157640	160563	-2923
33	91279	90379	900
34	189374	90379	98995
35	167915	160563	7352
36	0	4401.36363636364	-4401.36363636364
37	175403	116200.327868852	59202.6721311475
38	92342	90379	1963
39	100023	116200.327868852	-16177.3278688525
40	178277	223837.25	-45560.25
41	145062	116200.327868852	28861.6721311475
42	110980	116200.327868852	-5220.32786885246
43	86039	116200.327868852	-30161.3278688525
44	120821	116200.327868852	4620.67213114754
45	95535	116200.327868852	-20665.3278688525
46	109894	116200.327868852	-6306.32786885246
47	61554	49909.6363636364	11644.3636363636
48	156520	116200.327868852	40319.6721311475
49	159121	160563	-1442
50	129362	116200.327868852	13161.6721311475
51	48188	49909.6363636364	-1721.63636363636
52	91198	90379	819
53	229864	223837.25	6026.75
54	180317	116200.327868852	64116.6721311475
55	150640	160563	-9923
56	104416	116200.327868852	-11784.3278688525
57	165098	116200.327868852	48897.6721311475
58	63205	90379	-27174
59	100056	116200.327868852	-16144.3278688525
60	137214	90379	46835
61	99630	116200.327868852	-16570.3278688525
62	84557	116200.327868852	-31643.3278688525
63	91199	116200.327868852	-25001.3278688525
64	83419	116200.327868852	-32781.3278688525
65	101723	116200.327868852	-14477.3278688525
66	94982	116200.327868852	-21218.3278688525
67	129700	223837.25	-94137.25
68	110708	116200.327868852	-5492.32786885246
69	81518	116200.327868852	-34682.3278688525
70	31970	49909.6363636364	-17939.6363636364
71	192268	160563	31705
72	87611	116200.327868852	-28589.3278688525
73	77890	116200.327868852	-38310.3278688525
74	83261	90379	-7118
75	116290	116200.327868852	89.6721311475412
76	56544	49909.6363636364	6634.36363636364
77	116173	116200.327868852	-27.3278688524588
78	111488	160563	-49075
79	60138	49909.6363636364	10228.3636363636
80	73422	90379	-16957
81	67751	90379	-22628
82	213351	116200.327868852	97150.6721311475
83	51185	33261.2	17923.8
84	97181	116200.327868852	-19019.3278688525
85	45100	49909.6363636364	-4809.63636363636
86	115801	116200.327868852	-399.327868852459
87	185664	160563	25101
88	71960	90379	-18419
89	76441	90379	-13938
90	103613	116200.327868852	-12587.3278688525
91	98707	116200.327868852	-17493.3278688525
92	126527	116200.327868852	10326.6721311475
93	136781	116200.327868852	20580.6721311475
94	105863	90379	15484
95	38775	33261.2	5513.8
96	179984	160563	19421
97	164808	160563	4245
98	19349	33261.2	-13912.2
99	146824	116200.327868852	30623.6721311475
100	108660	116200.327868852	-7540.32786885246
101	43803	33261.2	10541.8
102	47062	90379	-43317
103	110845	90379	20466
104	92517	116200.327868852	-23683.3278688525
105	58660	90379	-31719
106	27676	33261.2	-5585.2
107	98550	116200.327868852	-17650.3278688525
108	43284	33261.2	10022.8
109	0	4401.36363636364	-4401.36363636364
110	66016	90379	-24363
111	57359	90379	-33020
112	96933	116200.327868852	-19267.3278688525
113	70369	116200.327868852	-45831.3278688525
114	65494	90379	-24885
115	3616	4401.36363636364	-785.363636363636
116	0	4401.36363636364	-4401.36363636364
117	143931	116200.327868852	27730.6721311475
118	109894	116200.327868852	-6306.32786885246
119	122973	116200.327868852	6772.67213114754
120	84336	90379	-6043
121	43410	33261.2	10148.8
122	136250	116200.327868852	20049.6721311475
123	79015	90379	-11364
124	92937	90379	2558
125	57586	49909.6363636364	7676.36363636364
126	19764	33261.2	-13497.2
127	105757	116200.327868852	-10443.3278688525
128	96410	90379	6031
129	113402	90379	23023
130	11796	4401.36363636364	7394.63636363636
131	7627	4401.36363636364	3225.63636363636
132	121085	116200.327868852	4884.67213114754
133	6836	4401.36363636364	2434.63636363636
134	139563	116200.327868852	23362.6721311475
135	5118	33261.2	-28143.2
136	40248	33261.2	6986.8
137	0	4401.36363636364	-4401.36363636364
138	95079	90379	4700
139	80750	90379	-9629
140	7131	4401.36363636364	2729.63636363636
141	4194	4401.36363636364	-207.363636363636
142	60378	49909.6363636364	10468.3636363636
143	96971	116200.327868852	-19229.3278688525
144	83484	116200.327868852	-32716.3278688525

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 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code