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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

Thu, 15 Dec 2011 14:20:37 -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/15/t1323976882tbee2hl8ylxlfuq.htm/, Retrieved Wed, 08 May 2024 19:18:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155663, Retrieved Wed, 08 May 2024 19:18:40 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

109

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)] [] [2011-12-15 19:20:37] [fdc44bfd4083541fef2da21135af4be8] [Current]
-    D      [Recursive Partitioning (Regression Trees)] [] [2011-12-21 14:48:59] [74be16979710d4c4e7c6647856088456] 
-   P         [Recursive Partitioning (Regression Trees)] [] [2011-12-21 19:18:25] [74be16979710d4c4e7c6647856088456] 
-   P         [Recursive Partitioning (Regression Trees)] [] [2011-12-21 19:48:14] [74be16979710d4c4e7c6647856088456] 
-             [Recursive Partitioning (Regression Trees)] [] [2011-12-23 19:24:25] [74be16979710d4c4e7c6647856088456] 

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Dataseries X:

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Histogram

Boxplots

0	129988	81	20	18
0	130358	46	38	17
0	7215	18	0	0
1	112914	86	49	22
1	219904	126	76	30
1	402036	218	104	31
1	117604	50	37	19
0	131822	50	57	25
1	99729	38	42	30
1	256310	86	62	26
1	113066	69	50	20
1	165392	62	66	30
0	78240	90	38	15
0	152673	84	48	22
0	134368	47	42	17
0	125769	67	47	19
0	123467	50	71	28
1	56232	47	0	12
1	108458	79	50	28
0	22762	21	12	13
0	48633	50	16	14
0	182081	83	77	27
1	140857	59	29	25
0	93773	46	38	30
0	133398	78	50	21
0	113933	23	33	17
0	153851	139	49	22
1	140711	75	59	28
0	303844	105	55	26
1	163810	38	42	17
1	123344	40	40	23
0	157640	39	51	20
1	103274	90	45	16
0	193500	105	73	20
0	178768	43	51	21
0	0	1	0	0
1	181412	55	46	27
1	92342	47	44	14
1	100023	41	31	29
1	178277	50	71	31
1	145067	58	61	19
1	114146	50	28	30
0	86039	25	21	23
1	125481	66	42	21
1	95535	42	44	22
1	129221	78	40	21
0	61554	26	15	32
0	168048	82	46	20
1	159121	75	43	26
0	129362	51	47	25
1	48188	28	12	22
0	95461	56	46	19
0	229864	64	56	24
0	191094	68	47	26
1	161082	51	50	27
0	111388	47	35	10
1	172614	58	45	26
1	63205	18	25	23
1	109102	56	47	21
1	137303	74	28	34
1	125304	50	48	29
1	88620	65	32	19
0	95808	48	28	19
1	83419	29	31	23
0	101723	25	13	22
0	94982	37	38	29
0	143566	61	48	31
1	113325	63	68	21
0	81518	32	32	21
1	31970	15	5	21
1	192268	102	53	15
1	91261	55	33	9
0	80820	56	54	23
1	85829	59	37	18
1	116322	53	52	31
1	56544	32	0	25
0	118838	52	52	25
1	118781	80	51	22
1	60138	23	16	21
0	73422	66	33	26
0	67751	58	48	22
1	225857	54	35	26
1	51185	24	24	20
0	97181	32	37	25
0	45100	39	17	19
1	115801	43	32	22
1	186310	190	55	25
0	71960	86	39	22
0	80105	48	31	21
0	107728	42	26	21
1	98707	33	37	23
1	136234	67	66	22
0	136781	52	35	21
1	105863	52	24	12
1	49164	33	22	13
0	189493	93	42	32
0	169406	50	86	24
0	19349	12	13	1
1	160819	87	21	24
0	109510	53	32	25
0	43803	25	8	4
1	47062	19	38	15
1	110845	44	45	21
0	92517	52	24	23
1	58660	36	23	12
1	27676	22	2	16
1	98550	32	52	24
0	43646	24	5	9
0	0	0	0	0
0	75566	28	43	25
0	57359	48	18	17
1	104330	36	44	18
1	70369	47	45	21
0	65494	56	29	17
0	3616	5	0	0
1	0	0	0	0
1	143931	37	32	20
1	117946	66	65	26
0	137332	85	26	27
0	84336	33	24	20
1	43410	19	7	1
0	137585	60	62	25
1	79015	34	30	14
1	101354	46	49	27
1	57586	38	3	12
1	19764	12	10	2
1	105757	42	42	16
0	103651	25	23	23
0	113402	35	40	28
0	11796	9	1	2
0	7627	9	0	0
1	121085	49	29	17
1	6836	3	0	1
0	139563	46	46	17
0	5118	3	5	0
1	40248	16	8	4
0	0	0	0	0
1	95079	42	21	25
0	80763	32	21	26
1	7131	4	0	0
1	4194	11	0	0
0	60378	20	15	15
1	109173	44	47	20
1	83484	16	17	19

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	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155663&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155663&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155663&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	'Gwilym Jenkins' @ jenkins.wessa.net

Goodness of Fit
Correlation	0.8355
R-squared	0.698
RMSE	33474.553

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8355 \tabularnewline
R-squared & 0.698 \tabularnewline
RMSE & 33474.553 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155663&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8355[/C][/ROW]
[ROW][C]R-squared[/C][C]0.698[/C][/ROW]
[ROW][C]RMSE[/C][C]33474.553[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155663&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155663&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	0.8355
R-squared	0.698
RMSE	33474.553

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	129988	114206.088888889	15781.9111111111
2	130358	114206.088888889	16151.9111111111
3	7215	9070.4	-1855.4
4	112914	139143.9	-26229.9
5	219904	230150.75	-10246.75
6	402036	230150.75	171885.25
7	117604	114206.088888889	3397.91111111111
8	131822	139143.9	-7321.89999999999
9	99729	114206.088888889	-14477.0888888889
10	256310	139143.9	117166.1
11	113066	139143.9	-26077.9
12	165392	139143.9	26248.1
13	78240	114206.088888889	-35966.0888888889
14	152673	139143.9	13529.1
15	134368	114206.088888889	20161.9111111111
16	125769	139143.9	-13374.9
17	123467	139143.9	-15676.9
18	56232	52612.4444444444	3619.55555555555
19	108458	139143.9	-30685.9
20	22762	52612.4444444444	-29850.4444444444
21	48633	52612.4444444444	-3979.44444444445
22	182081	139143.9	42937.1
23	140857	114206.088888889	26650.9111111111
24	93773	114206.088888889	-20433.0888888889
25	133398	139143.9	-5745.89999999999
26	113933	81729.7777777778	32203.2222222222
27	153851	230150.75	-76299.75
28	140711	139143.9	1567.10000000001
29	303844	230150.75	73693.25
30	163810	114206.088888889	49603.9111111111
31	123344	114206.088888889	9137.91111111111
32	157640	139143.9	18496.1
33	103274	114206.088888889	-10932.0888888889
34	193500	230150.75	-36650.75
35	178768	139143.9	39624.1
36	0	9070.4	-9070.4
37	181412	139143.9	42268.1
38	92342	114206.088888889	-21864.0888888889
39	100023	114206.088888889	-14183.0888888889
40	178277	139143.9	39133.1
41	145067	139143.9	5923.10000000001
42	114146	114206.088888889	-60.0888888888876
43	86039	81729.7777777778	4309.22222222222
44	125481	114206.088888889	11274.9111111111
45	95535	114206.088888889	-18671.0888888889
46	129221	114206.088888889	15014.9111111111
47	61554	52612.4444444444	8941.55555555555
48	168048	139143.9	28904.1
49	159121	114206.088888889	44914.9111111111
50	129362	139143.9	-9781.89999999999
51	48188	52612.4444444444	-4424.44444444445
52	95461	139143.9	-43682.9
53	229864	139143.9	90720.1
54	191094	139143.9	51950.1
55	161082	139143.9	21938.1
56	111388	114206.088888889	-2818.08888888889
57	172614	114206.088888889	58407.9111111111
58	63205	81729.7777777778	-18524.7777777778
59	109102	139143.9	-30041.9
60	137303	114206.088888889	23096.9111111111
61	125304	139143.9	-13839.9
62	88620	114206.088888889	-25586.0888888889
63	95808	114206.088888889	-18398.0888888889
64	83419	81729.7777777778	1689.22222222222
65	101723	52612.4444444444	49110.5555555556
66	94982	114206.088888889	-19224.0888888889
67	143566	139143.9	4422.10000000001
68	113325	139143.9	-25818.9
69	81518	81729.7777777778	-211.777777777781
70	31970	52612.4444444444	-20642.4444444444
71	192268	230150.75	-37882.75
72	91261	114206.088888889	-22945.0888888889
73	80820	139143.9	-58323.9
74	85829	114206.088888889	-28377.0888888889
75	116322	139143.9	-22821.9
76	56544	52612.4444444444	3931.55555555555
77	118838	139143.9	-20305.9
78	118781	139143.9	-20362.9
79	60138	52612.4444444444	7525.55555555555
80	73422	114206.088888889	-40784.0888888889
81	67751	139143.9	-71392.9
82	225857	114206.088888889	111650.911111111
83	51185	81729.7777777778	-30544.7777777778
84	97181	81729.7777777778	15451.2222222222
85	45100	52612.4444444444	-7512.44444444445
86	115801	114206.088888889	1594.91111111111
87	186310	230150.75	-43840.75
88	71960	114206.088888889	-42246.0888888889
89	80105	114206.088888889	-34101.0888888889
90	107728	114206.088888889	-6478.08888888889
91	98707	81729.7777777778	16977.2222222222
92	136234	139143.9	-2909.89999999999
93	136781	114206.088888889	22574.9111111111
94	105863	114206.088888889	-8343.08888888889
95	49164	81729.7777777778	-32565.7777777778
96	189493	230150.75	-40657.75
97	169406	139143.9	30262.1
98	19349	9070.4	10278.6
99	160819	114206.088888889	46612.9111111111
100	109510	114206.088888889	-4696.08888888889
101	43803	52612.4444444444	-8809.44444444445
102	47062	81729.7777777778	-34667.7777777778
103	110845	114206.088888889	-3361.08888888889
104	92517	114206.088888889	-21689.0888888889
105	58660	81729.7777777778	-23069.7777777778
106	27676	52612.4444444444	-24936.4444444444
107	98550	139143.9	-40593.9
108	43646	52612.4444444444	-8966.44444444445
109	0	9070.4	-9070.4
110	75566	81729.7777777778	-6163.77777777778
111	57359	52612.4444444444	4746.55555555555
112	104330	81729.7777777778	22600.2222222222
113	70369	114206.088888889	-43837.0888888889
114	65494	114206.088888889	-48712.0888888889
115	3616	9070.4	-5454.4
116	0	9070.4	-9070.4
117	143931	114206.088888889	29724.9111111111
118	117946	139143.9	-21197.9
119	137332	114206.088888889	23125.9111111111
120	84336	81729.7777777778	2606.22222222222
121	43410	9070.4	34339.6
122	137585	139143.9	-1558.89999999999
123	79015	81729.7777777778	-2714.77777777778
124	101354	139143.9	-37789.9
125	57586	52612.4444444444	4973.55555555555
126	19764	9070.4	10693.6
127	105757	114206.088888889	-8449.08888888889
128	103651	81729.7777777778	21921.2222222222
129	113402	81729.7777777778	31672.2222222222
130	11796	9070.4	2725.6
131	7627	9070.4	-1443.4
132	121085	114206.088888889	6878.91111111111
133	6836	9070.4	-2234.4
134	139563	139143.9	419.100000000006
135	5118	9070.4	-3952.4
136	40248	52612.4444444444	-12364.4444444444
137	0	9070.4	-9070.4
138	95079	114206.088888889	-19127.0888888889
139	80763	81729.7777777778	-966.777777777781
140	7131	9070.4	-1939.4
141	4194	9070.4	-4876.4
142	60378	52612.4444444444	7765.55555555555
143	109173	139143.9	-29970.9
144	83484	52612.4444444444	30871.5555555556

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 129988 & 114206.088888889 & 15781.9111111111 \tabularnewline
2 & 130358 & 114206.088888889 & 16151.9111111111 \tabularnewline
3 & 7215 & 9070.4 & -1855.4 \tabularnewline
4 & 112914 & 139143.9 & -26229.9 \tabularnewline
5 & 219904 & 230150.75 & -10246.75 \tabularnewline
6 & 402036 & 230150.75 & 171885.25 \tabularnewline
7 & 117604 & 114206.088888889 & 3397.91111111111 \tabularnewline
8 & 131822 & 139143.9 & -7321.89999999999 \tabularnewline
9 & 99729 & 114206.088888889 & -14477.0888888889 \tabularnewline
10 & 256310 & 139143.9 & 117166.1 \tabularnewline
11 & 113066 & 139143.9 & -26077.9 \tabularnewline
12 & 165392 & 139143.9 & 26248.1 \tabularnewline
13 & 78240 & 114206.088888889 & -35966.0888888889 \tabularnewline
14 & 152673 & 139143.9 & 13529.1 \tabularnewline
15 & 134368 & 114206.088888889 & 20161.9111111111 \tabularnewline
16 & 125769 & 139143.9 & -13374.9 \tabularnewline
17 & 123467 & 139143.9 & -15676.9 \tabularnewline
18 & 56232 & 52612.4444444444 & 3619.55555555555 \tabularnewline
19 & 108458 & 139143.9 & -30685.9 \tabularnewline
20 & 22762 & 52612.4444444444 & -29850.4444444444 \tabularnewline
21 & 48633 & 52612.4444444444 & -3979.44444444445 \tabularnewline
22 & 182081 & 139143.9 & 42937.1 \tabularnewline
23 & 140857 & 114206.088888889 & 26650.9111111111 \tabularnewline
24 & 93773 & 114206.088888889 & -20433.0888888889 \tabularnewline
25 & 133398 & 139143.9 & -5745.89999999999 \tabularnewline
26 & 113933 & 81729.7777777778 & 32203.2222222222 \tabularnewline
27 & 153851 & 230150.75 & -76299.75 \tabularnewline
28 & 140711 & 139143.9 & 1567.10000000001 \tabularnewline
29 & 303844 & 230150.75 & 73693.25 \tabularnewline
30 & 163810 & 114206.088888889 & 49603.9111111111 \tabularnewline
31 & 123344 & 114206.088888889 & 9137.91111111111 \tabularnewline
32 & 157640 & 139143.9 & 18496.1 \tabularnewline
33 & 103274 & 114206.088888889 & -10932.0888888889 \tabularnewline
34 & 193500 & 230150.75 & -36650.75 \tabularnewline
35 & 178768 & 139143.9 & 39624.1 \tabularnewline
36 & 0 & 9070.4 & -9070.4 \tabularnewline
37 & 181412 & 139143.9 & 42268.1 \tabularnewline
38 & 92342 & 114206.088888889 & -21864.0888888889 \tabularnewline
39 & 100023 & 114206.088888889 & -14183.0888888889 \tabularnewline
40 & 178277 & 139143.9 & 39133.1 \tabularnewline
41 & 145067 & 139143.9 & 5923.10000000001 \tabularnewline
42 & 114146 & 114206.088888889 & -60.0888888888876 \tabularnewline
43 & 86039 & 81729.7777777778 & 4309.22222222222 \tabularnewline
44 & 125481 & 114206.088888889 & 11274.9111111111 \tabularnewline
45 & 95535 & 114206.088888889 & -18671.0888888889 \tabularnewline
46 & 129221 & 114206.088888889 & 15014.9111111111 \tabularnewline
47 & 61554 & 52612.4444444444 & 8941.55555555555 \tabularnewline
48 & 168048 & 139143.9 & 28904.1 \tabularnewline
49 & 159121 & 114206.088888889 & 44914.9111111111 \tabularnewline
50 & 129362 & 139143.9 & -9781.89999999999 \tabularnewline
51 & 48188 & 52612.4444444444 & -4424.44444444445 \tabularnewline
52 & 95461 & 139143.9 & -43682.9 \tabularnewline
53 & 229864 & 139143.9 & 90720.1 \tabularnewline
54 & 191094 & 139143.9 & 51950.1 \tabularnewline
55 & 161082 & 139143.9 & 21938.1 \tabularnewline
56 & 111388 & 114206.088888889 & -2818.08888888889 \tabularnewline
57 & 172614 & 114206.088888889 & 58407.9111111111 \tabularnewline
58 & 63205 & 81729.7777777778 & -18524.7777777778 \tabularnewline
59 & 109102 & 139143.9 & -30041.9 \tabularnewline
60 & 137303 & 114206.088888889 & 23096.9111111111 \tabularnewline
61 & 125304 & 139143.9 & -13839.9 \tabularnewline
62 & 88620 & 114206.088888889 & -25586.0888888889 \tabularnewline
63 & 95808 & 114206.088888889 & -18398.0888888889 \tabularnewline
64 & 83419 & 81729.7777777778 & 1689.22222222222 \tabularnewline
65 & 101723 & 52612.4444444444 & 49110.5555555556 \tabularnewline
66 & 94982 & 114206.088888889 & -19224.0888888889 \tabularnewline
67 & 143566 & 139143.9 & 4422.10000000001 \tabularnewline
68 & 113325 & 139143.9 & -25818.9 \tabularnewline
69 & 81518 & 81729.7777777778 & -211.777777777781 \tabularnewline
70 & 31970 & 52612.4444444444 & -20642.4444444444 \tabularnewline
71 & 192268 & 230150.75 & -37882.75 \tabularnewline
72 & 91261 & 114206.088888889 & -22945.0888888889 \tabularnewline
73 & 80820 & 139143.9 & -58323.9 \tabularnewline
74 & 85829 & 114206.088888889 & -28377.0888888889 \tabularnewline
75 & 116322 & 139143.9 & -22821.9 \tabularnewline
76 & 56544 & 52612.4444444444 & 3931.55555555555 \tabularnewline
77 & 118838 & 139143.9 & -20305.9 \tabularnewline
78 & 118781 & 139143.9 & -20362.9 \tabularnewline
79 & 60138 & 52612.4444444444 & 7525.55555555555 \tabularnewline
80 & 73422 & 114206.088888889 & -40784.0888888889 \tabularnewline
81 & 67751 & 139143.9 & -71392.9 \tabularnewline
82 & 225857 & 114206.088888889 & 111650.911111111 \tabularnewline
83 & 51185 & 81729.7777777778 & -30544.7777777778 \tabularnewline
84 & 97181 & 81729.7777777778 & 15451.2222222222 \tabularnewline
85 & 45100 & 52612.4444444444 & -7512.44444444445 \tabularnewline
86 & 115801 & 114206.088888889 & 1594.91111111111 \tabularnewline
87 & 186310 & 230150.75 & -43840.75 \tabularnewline
88 & 71960 & 114206.088888889 & -42246.0888888889 \tabularnewline
89 & 80105 & 114206.088888889 & -34101.0888888889 \tabularnewline
90 & 107728 & 114206.088888889 & -6478.08888888889 \tabularnewline
91 & 98707 & 81729.7777777778 & 16977.2222222222 \tabularnewline
92 & 136234 & 139143.9 & -2909.89999999999 \tabularnewline
93 & 136781 & 114206.088888889 & 22574.9111111111 \tabularnewline
94 & 105863 & 114206.088888889 & -8343.08888888889 \tabularnewline
95 & 49164 & 81729.7777777778 & -32565.7777777778 \tabularnewline
96 & 189493 & 230150.75 & -40657.75 \tabularnewline
97 & 169406 & 139143.9 & 30262.1 \tabularnewline
98 & 19349 & 9070.4 & 10278.6 \tabularnewline
99 & 160819 & 114206.088888889 & 46612.9111111111 \tabularnewline
100 & 109510 & 114206.088888889 & -4696.08888888889 \tabularnewline
101 & 43803 & 52612.4444444444 & -8809.44444444445 \tabularnewline
102 & 47062 & 81729.7777777778 & -34667.7777777778 \tabularnewline
103 & 110845 & 114206.088888889 & -3361.08888888889 \tabularnewline
104 & 92517 & 114206.088888889 & -21689.0888888889 \tabularnewline
105 & 58660 & 81729.7777777778 & -23069.7777777778 \tabularnewline
106 & 27676 & 52612.4444444444 & -24936.4444444444 \tabularnewline
107 & 98550 & 139143.9 & -40593.9 \tabularnewline
108 & 43646 & 52612.4444444444 & -8966.44444444445 \tabularnewline
109 & 0 & 9070.4 & -9070.4 \tabularnewline
110 & 75566 & 81729.7777777778 & -6163.77777777778 \tabularnewline
111 & 57359 & 52612.4444444444 & 4746.55555555555 \tabularnewline
112 & 104330 & 81729.7777777778 & 22600.2222222222 \tabularnewline
113 & 70369 & 114206.088888889 & -43837.0888888889 \tabularnewline
114 & 65494 & 114206.088888889 & -48712.0888888889 \tabularnewline
115 & 3616 & 9070.4 & -5454.4 \tabularnewline
116 & 0 & 9070.4 & -9070.4 \tabularnewline
117 & 143931 & 114206.088888889 & 29724.9111111111 \tabularnewline
118 & 117946 & 139143.9 & -21197.9 \tabularnewline
119 & 137332 & 114206.088888889 & 23125.9111111111 \tabularnewline
120 & 84336 & 81729.7777777778 & 2606.22222222222 \tabularnewline
121 & 43410 & 9070.4 & 34339.6 \tabularnewline
122 & 137585 & 139143.9 & -1558.89999999999 \tabularnewline
123 & 79015 & 81729.7777777778 & -2714.77777777778 \tabularnewline
124 & 101354 & 139143.9 & -37789.9 \tabularnewline
125 & 57586 & 52612.4444444444 & 4973.55555555555 \tabularnewline
126 & 19764 & 9070.4 & 10693.6 \tabularnewline
127 & 105757 & 114206.088888889 & -8449.08888888889 \tabularnewline
128 & 103651 & 81729.7777777778 & 21921.2222222222 \tabularnewline
129 & 113402 & 81729.7777777778 & 31672.2222222222 \tabularnewline
130 & 11796 & 9070.4 & 2725.6 \tabularnewline
131 & 7627 & 9070.4 & -1443.4 \tabularnewline
132 & 121085 & 114206.088888889 & 6878.91111111111 \tabularnewline
133 & 6836 & 9070.4 & -2234.4 \tabularnewline
134 & 139563 & 139143.9 & 419.100000000006 \tabularnewline
135 & 5118 & 9070.4 & -3952.4 \tabularnewline
136 & 40248 & 52612.4444444444 & -12364.4444444444 \tabularnewline
137 & 0 & 9070.4 & -9070.4 \tabularnewline
138 & 95079 & 114206.088888889 & -19127.0888888889 \tabularnewline
139 & 80763 & 81729.7777777778 & -966.777777777781 \tabularnewline
140 & 7131 & 9070.4 & -1939.4 \tabularnewline
141 & 4194 & 9070.4 & -4876.4 \tabularnewline
142 & 60378 & 52612.4444444444 & 7765.55555555555 \tabularnewline
143 & 109173 & 139143.9 & -29970.9 \tabularnewline
144 & 83484 & 52612.4444444444 & 30871.5555555556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155663&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]129988[/C][C]114206.088888889[/C][C]15781.9111111111[/C][/ROW]
[ROW][C]2[/C][C]130358[/C][C]114206.088888889[/C][C]16151.9111111111[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]9070.4[/C][C]-1855.4[/C][/ROW]
[ROW][C]4[/C][C]112914[/C][C]139143.9[/C][C]-26229.9[/C][/ROW]
[ROW][C]5[/C][C]219904[/C][C]230150.75[/C][C]-10246.75[/C][/ROW]
[ROW][C]6[/C][C]402036[/C][C]230150.75[/C][C]171885.25[/C][/ROW]
[ROW][C]7[/C][C]117604[/C][C]114206.088888889[/C][C]3397.91111111111[/C][/ROW]
[ROW][C]8[/C][C]131822[/C][C]139143.9[/C][C]-7321.89999999999[/C][/ROW]
[ROW][C]9[/C][C]99729[/C][C]114206.088888889[/C][C]-14477.0888888889[/C][/ROW]
[ROW][C]10[/C][C]256310[/C][C]139143.9[/C][C]117166.1[/C][/ROW]
[ROW][C]11[/C][C]113066[/C][C]139143.9[/C][C]-26077.9[/C][/ROW]
[ROW][C]12[/C][C]165392[/C][C]139143.9[/C][C]26248.1[/C][/ROW]
[ROW][C]13[/C][C]78240[/C][C]114206.088888889[/C][C]-35966.0888888889[/C][/ROW]
[ROW][C]14[/C][C]152673[/C][C]139143.9[/C][C]13529.1[/C][/ROW]
[ROW][C]15[/C][C]134368[/C][C]114206.088888889[/C][C]20161.9111111111[/C][/ROW]
[ROW][C]16[/C][C]125769[/C][C]139143.9[/C][C]-13374.9[/C][/ROW]
[ROW][C]17[/C][C]123467[/C][C]139143.9[/C][C]-15676.9[/C][/ROW]
[ROW][C]18[/C][C]56232[/C][C]52612.4444444444[/C][C]3619.55555555555[/C][/ROW]
[ROW][C]19[/C][C]108458[/C][C]139143.9[/C][C]-30685.9[/C][/ROW]
[ROW][C]20[/C][C]22762[/C][C]52612.4444444444[/C][C]-29850.4444444444[/C][/ROW]
[ROW][C]21[/C][C]48633[/C][C]52612.4444444444[/C][C]-3979.44444444445[/C][/ROW]
[ROW][C]22[/C][C]182081[/C][C]139143.9[/C][C]42937.1[/C][/ROW]
[ROW][C]23[/C][C]140857[/C][C]114206.088888889[/C][C]26650.9111111111[/C][/ROW]
[ROW][C]24[/C][C]93773[/C][C]114206.088888889[/C][C]-20433.0888888889[/C][/ROW]
[ROW][C]25[/C][C]133398[/C][C]139143.9[/C][C]-5745.89999999999[/C][/ROW]
[ROW][C]26[/C][C]113933[/C][C]81729.7777777778[/C][C]32203.2222222222[/C][/ROW]
[ROW][C]27[/C][C]153851[/C][C]230150.75[/C][C]-76299.75[/C][/ROW]
[ROW][C]28[/C][C]140711[/C][C]139143.9[/C][C]1567.10000000001[/C][/ROW]
[ROW][C]29[/C][C]303844[/C][C]230150.75[/C][C]73693.25[/C][/ROW]
[ROW][C]30[/C][C]163810[/C][C]114206.088888889[/C][C]49603.9111111111[/C][/ROW]
[ROW][C]31[/C][C]123344[/C][C]114206.088888889[/C][C]9137.91111111111[/C][/ROW]
[ROW][C]32[/C][C]157640[/C][C]139143.9[/C][C]18496.1[/C][/ROW]
[ROW][C]33[/C][C]103274[/C][C]114206.088888889[/C][C]-10932.0888888889[/C][/ROW]
[ROW][C]34[/C][C]193500[/C][C]230150.75[/C][C]-36650.75[/C][/ROW]
[ROW][C]35[/C][C]178768[/C][C]139143.9[/C][C]39624.1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]9070.4[/C][C]-9070.4[/C][/ROW]
[ROW][C]37[/C][C]181412[/C][C]139143.9[/C][C]42268.1[/C][/ROW]
[ROW][C]38[/C][C]92342[/C][C]114206.088888889[/C][C]-21864.0888888889[/C][/ROW]
[ROW][C]39[/C][C]100023[/C][C]114206.088888889[/C][C]-14183.0888888889[/C][/ROW]
[ROW][C]40[/C][C]178277[/C][C]139143.9[/C][C]39133.1[/C][/ROW]
[ROW][C]41[/C][C]145067[/C][C]139143.9[/C][C]5923.10000000001[/C][/ROW]
[ROW][C]42[/C][C]114146[/C][C]114206.088888889[/C][C]-60.0888888888876[/C][/ROW]
[ROW][C]43[/C][C]86039[/C][C]81729.7777777778[/C][C]4309.22222222222[/C][/ROW]
[ROW][C]44[/C][C]125481[/C][C]114206.088888889[/C][C]11274.9111111111[/C][/ROW]
[ROW][C]45[/C][C]95535[/C][C]114206.088888889[/C][C]-18671.0888888889[/C][/ROW]
[ROW][C]46[/C][C]129221[/C][C]114206.088888889[/C][C]15014.9111111111[/C][/ROW]
[ROW][C]47[/C][C]61554[/C][C]52612.4444444444[/C][C]8941.55555555555[/C][/ROW]
[ROW][C]48[/C][C]168048[/C][C]139143.9[/C][C]28904.1[/C][/ROW]
[ROW][C]49[/C][C]159121[/C][C]114206.088888889[/C][C]44914.9111111111[/C][/ROW]
[ROW][C]50[/C][C]129362[/C][C]139143.9[/C][C]-9781.89999999999[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]52612.4444444444[/C][C]-4424.44444444445[/C][/ROW]
[ROW][C]52[/C][C]95461[/C][C]139143.9[/C][C]-43682.9[/C][/ROW]
[ROW][C]53[/C][C]229864[/C][C]139143.9[/C][C]90720.1[/C][/ROW]
[ROW][C]54[/C][C]191094[/C][C]139143.9[/C][C]51950.1[/C][/ROW]
[ROW][C]55[/C][C]161082[/C][C]139143.9[/C][C]21938.1[/C][/ROW]
[ROW][C]56[/C][C]111388[/C][C]114206.088888889[/C][C]-2818.08888888889[/C][/ROW]
[ROW][C]57[/C][C]172614[/C][C]114206.088888889[/C][C]58407.9111111111[/C][/ROW]
[ROW][C]58[/C][C]63205[/C][C]81729.7777777778[/C][C]-18524.7777777778[/C][/ROW]
[ROW][C]59[/C][C]109102[/C][C]139143.9[/C][C]-30041.9[/C][/ROW]
[ROW][C]60[/C][C]137303[/C][C]114206.088888889[/C][C]23096.9111111111[/C][/ROW]
[ROW][C]61[/C][C]125304[/C][C]139143.9[/C][C]-13839.9[/C][/ROW]
[ROW][C]62[/C][C]88620[/C][C]114206.088888889[/C][C]-25586.0888888889[/C][/ROW]
[ROW][C]63[/C][C]95808[/C][C]114206.088888889[/C][C]-18398.0888888889[/C][/ROW]
[ROW][C]64[/C][C]83419[/C][C]81729.7777777778[/C][C]1689.22222222222[/C][/ROW]
[ROW][C]65[/C][C]101723[/C][C]52612.4444444444[/C][C]49110.5555555556[/C][/ROW]
[ROW][C]66[/C][C]94982[/C][C]114206.088888889[/C][C]-19224.0888888889[/C][/ROW]
[ROW][C]67[/C][C]143566[/C][C]139143.9[/C][C]4422.10000000001[/C][/ROW]
[ROW][C]68[/C][C]113325[/C][C]139143.9[/C][C]-25818.9[/C][/ROW]
[ROW][C]69[/C][C]81518[/C][C]81729.7777777778[/C][C]-211.777777777781[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]52612.4444444444[/C][C]-20642.4444444444[/C][/ROW]
[ROW][C]71[/C][C]192268[/C][C]230150.75[/C][C]-37882.75[/C][/ROW]
[ROW][C]72[/C][C]91261[/C][C]114206.088888889[/C][C]-22945.0888888889[/C][/ROW]
[ROW][C]73[/C][C]80820[/C][C]139143.9[/C][C]-58323.9[/C][/ROW]
[ROW][C]74[/C][C]85829[/C][C]114206.088888889[/C][C]-28377.0888888889[/C][/ROW]
[ROW][C]75[/C][C]116322[/C][C]139143.9[/C][C]-22821.9[/C][/ROW]
[ROW][C]76[/C][C]56544[/C][C]52612.4444444444[/C][C]3931.55555555555[/C][/ROW]
[ROW][C]77[/C][C]118838[/C][C]139143.9[/C][C]-20305.9[/C][/ROW]
[ROW][C]78[/C][C]118781[/C][C]139143.9[/C][C]-20362.9[/C][/ROW]
[ROW][C]79[/C][C]60138[/C][C]52612.4444444444[/C][C]7525.55555555555[/C][/ROW]
[ROW][C]80[/C][C]73422[/C][C]114206.088888889[/C][C]-40784.0888888889[/C][/ROW]
[ROW][C]81[/C][C]67751[/C][C]139143.9[/C][C]-71392.9[/C][/ROW]
[ROW][C]82[/C][C]225857[/C][C]114206.088888889[/C][C]111650.911111111[/C][/ROW]
[ROW][C]83[/C][C]51185[/C][C]81729.7777777778[/C][C]-30544.7777777778[/C][/ROW]
[ROW][C]84[/C][C]97181[/C][C]81729.7777777778[/C][C]15451.2222222222[/C][/ROW]
[ROW][C]85[/C][C]45100[/C][C]52612.4444444444[/C][C]-7512.44444444445[/C][/ROW]
[ROW][C]86[/C][C]115801[/C][C]114206.088888889[/C][C]1594.91111111111[/C][/ROW]
[ROW][C]87[/C][C]186310[/C][C]230150.75[/C][C]-43840.75[/C][/ROW]
[ROW][C]88[/C][C]71960[/C][C]114206.088888889[/C][C]-42246.0888888889[/C][/ROW]
[ROW][C]89[/C][C]80105[/C][C]114206.088888889[/C][C]-34101.0888888889[/C][/ROW]
[ROW][C]90[/C][C]107728[/C][C]114206.088888889[/C][C]-6478.08888888889[/C][/ROW]
[ROW][C]91[/C][C]98707[/C][C]81729.7777777778[/C][C]16977.2222222222[/C][/ROW]
[ROW][C]92[/C][C]136234[/C][C]139143.9[/C][C]-2909.89999999999[/C][/ROW]
[ROW][C]93[/C][C]136781[/C][C]114206.088888889[/C][C]22574.9111111111[/C][/ROW]
[ROW][C]94[/C][C]105863[/C][C]114206.088888889[/C][C]-8343.08888888889[/C][/ROW]
[ROW][C]95[/C][C]49164[/C][C]81729.7777777778[/C][C]-32565.7777777778[/C][/ROW]
[ROW][C]96[/C][C]189493[/C][C]230150.75[/C][C]-40657.75[/C][/ROW]
[ROW][C]97[/C][C]169406[/C][C]139143.9[/C][C]30262.1[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]9070.4[/C][C]10278.6[/C][/ROW]
[ROW][C]99[/C][C]160819[/C][C]114206.088888889[/C][C]46612.9111111111[/C][/ROW]
[ROW][C]100[/C][C]109510[/C][C]114206.088888889[/C][C]-4696.08888888889[/C][/ROW]
[ROW][C]101[/C][C]43803[/C][C]52612.4444444444[/C][C]-8809.44444444445[/C][/ROW]
[ROW][C]102[/C][C]47062[/C][C]81729.7777777778[/C][C]-34667.7777777778[/C][/ROW]
[ROW][C]103[/C][C]110845[/C][C]114206.088888889[/C][C]-3361.08888888889[/C][/ROW]
[ROW][C]104[/C][C]92517[/C][C]114206.088888889[/C][C]-21689.0888888889[/C][/ROW]
[ROW][C]105[/C][C]58660[/C][C]81729.7777777778[/C][C]-23069.7777777778[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]52612.4444444444[/C][C]-24936.4444444444[/C][/ROW]
[ROW][C]107[/C][C]98550[/C][C]139143.9[/C][C]-40593.9[/C][/ROW]
[ROW][C]108[/C][C]43646[/C][C]52612.4444444444[/C][C]-8966.44444444445[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]9070.4[/C][C]-9070.4[/C][/ROW]
[ROW][C]110[/C][C]75566[/C][C]81729.7777777778[/C][C]-6163.77777777778[/C][/ROW]
[ROW][C]111[/C][C]57359[/C][C]52612.4444444444[/C][C]4746.55555555555[/C][/ROW]
[ROW][C]112[/C][C]104330[/C][C]81729.7777777778[/C][C]22600.2222222222[/C][/ROW]
[ROW][C]113[/C][C]70369[/C][C]114206.088888889[/C][C]-43837.0888888889[/C][/ROW]
[ROW][C]114[/C][C]65494[/C][C]114206.088888889[/C][C]-48712.0888888889[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]9070.4[/C][C]-5454.4[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]9070.4[/C][C]-9070.4[/C][/ROW]
[ROW][C]117[/C][C]143931[/C][C]114206.088888889[/C][C]29724.9111111111[/C][/ROW]
[ROW][C]118[/C][C]117946[/C][C]139143.9[/C][C]-21197.9[/C][/ROW]
[ROW][C]119[/C][C]137332[/C][C]114206.088888889[/C][C]23125.9111111111[/C][/ROW]
[ROW][C]120[/C][C]84336[/C][C]81729.7777777778[/C][C]2606.22222222222[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]9070.4[/C][C]34339.6[/C][/ROW]
[ROW][C]122[/C][C]137585[/C][C]139143.9[/C][C]-1558.89999999999[/C][/ROW]
[ROW][C]123[/C][C]79015[/C][C]81729.7777777778[/C][C]-2714.77777777778[/C][/ROW]
[ROW][C]124[/C][C]101354[/C][C]139143.9[/C][C]-37789.9[/C][/ROW]
[ROW][C]125[/C][C]57586[/C][C]52612.4444444444[/C][C]4973.55555555555[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]9070.4[/C][C]10693.6[/C][/ROW]
[ROW][C]127[/C][C]105757[/C][C]114206.088888889[/C][C]-8449.08888888889[/C][/ROW]
[ROW][C]128[/C][C]103651[/C][C]81729.7777777778[/C][C]21921.2222222222[/C][/ROW]
[ROW][C]129[/C][C]113402[/C][C]81729.7777777778[/C][C]31672.2222222222[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]9070.4[/C][C]2725.6[/C][/ROW]
[ROW][C]131[/C][C]7627[/C][C]9070.4[/C][C]-1443.4[/C][/ROW]
[ROW][C]132[/C][C]121085[/C][C]114206.088888889[/C][C]6878.91111111111[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]9070.4[/C][C]-2234.4[/C][/ROW]
[ROW][C]134[/C][C]139563[/C][C]139143.9[/C][C]419.100000000006[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]9070.4[/C][C]-3952.4[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]52612.4444444444[/C][C]-12364.4444444444[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]9070.4[/C][C]-9070.4[/C][/ROW]
[ROW][C]138[/C][C]95079[/C][C]114206.088888889[/C][C]-19127.0888888889[/C][/ROW]
[ROW][C]139[/C][C]80763[/C][C]81729.7777777778[/C][C]-966.777777777781[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]9070.4[/C][C]-1939.4[/C][/ROW]
[ROW][C]141[/C][C]4194[/C][C]9070.4[/C][C]-4876.4[/C][/ROW]
[ROW][C]142[/C][C]60378[/C][C]52612.4444444444[/C][C]7765.55555555555[/C][/ROW]
[ROW][C]143[/C][C]109173[/C][C]139143.9[/C][C]-29970.9[/C][/ROW]
[ROW][C]144[/C][C]83484[/C][C]52612.4444444444[/C][C]30871.5555555556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155663&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155663&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	129988	114206.088888889	15781.9111111111
2	130358	114206.088888889	16151.9111111111
3	7215	9070.4	-1855.4
4	112914	139143.9	-26229.9
5	219904	230150.75	-10246.75
6	402036	230150.75	171885.25
7	117604	114206.088888889	3397.91111111111
8	131822	139143.9	-7321.89999999999
9	99729	114206.088888889	-14477.0888888889
10	256310	139143.9	117166.1
11	113066	139143.9	-26077.9
12	165392	139143.9	26248.1
13	78240	114206.088888889	-35966.0888888889
14	152673	139143.9	13529.1
15	134368	114206.088888889	20161.9111111111
16	125769	139143.9	-13374.9
17	123467	139143.9	-15676.9
18	56232	52612.4444444444	3619.55555555555
19	108458	139143.9	-30685.9
20	22762	52612.4444444444	-29850.4444444444
21	48633	52612.4444444444	-3979.44444444445
22	182081	139143.9	42937.1
23	140857	114206.088888889	26650.9111111111
24	93773	114206.088888889	-20433.0888888889
25	133398	139143.9	-5745.89999999999
26	113933	81729.7777777778	32203.2222222222
27	153851	230150.75	-76299.75
28	140711	139143.9	1567.10000000001
29	303844	230150.75	73693.25
30	163810	114206.088888889	49603.9111111111
31	123344	114206.088888889	9137.91111111111
32	157640	139143.9	18496.1
33	103274	114206.088888889	-10932.0888888889
34	193500	230150.75	-36650.75
35	178768	139143.9	39624.1
36	0	9070.4	-9070.4
37	181412	139143.9	42268.1
38	92342	114206.088888889	-21864.0888888889
39	100023	114206.088888889	-14183.0888888889
40	178277	139143.9	39133.1
41	145067	139143.9	5923.10000000001
42	114146	114206.088888889	-60.0888888888876
43	86039	81729.7777777778	4309.22222222222
44	125481	114206.088888889	11274.9111111111
45	95535	114206.088888889	-18671.0888888889
46	129221	114206.088888889	15014.9111111111
47	61554	52612.4444444444	8941.55555555555
48	168048	139143.9	28904.1
49	159121	114206.088888889	44914.9111111111
50	129362	139143.9	-9781.89999999999
51	48188	52612.4444444444	-4424.44444444445
52	95461	139143.9	-43682.9
53	229864	139143.9	90720.1
54	191094	139143.9	51950.1
55	161082	139143.9	21938.1
56	111388	114206.088888889	-2818.08888888889
57	172614	114206.088888889	58407.9111111111
58	63205	81729.7777777778	-18524.7777777778
59	109102	139143.9	-30041.9
60	137303	114206.088888889	23096.9111111111
61	125304	139143.9	-13839.9
62	88620	114206.088888889	-25586.0888888889
63	95808	114206.088888889	-18398.0888888889
64	83419	81729.7777777778	1689.22222222222
65	101723	52612.4444444444	49110.5555555556
66	94982	114206.088888889	-19224.0888888889
67	143566	139143.9	4422.10000000001
68	113325	139143.9	-25818.9
69	81518	81729.7777777778	-211.777777777781
70	31970	52612.4444444444	-20642.4444444444
71	192268	230150.75	-37882.75
72	91261	114206.088888889	-22945.0888888889
73	80820	139143.9	-58323.9
74	85829	114206.088888889	-28377.0888888889
75	116322	139143.9	-22821.9
76	56544	52612.4444444444	3931.55555555555
77	118838	139143.9	-20305.9
78	118781	139143.9	-20362.9
79	60138	52612.4444444444	7525.55555555555
80	73422	114206.088888889	-40784.0888888889
81	67751	139143.9	-71392.9
82	225857	114206.088888889	111650.911111111
83	51185	81729.7777777778	-30544.7777777778
84	97181	81729.7777777778	15451.2222222222
85	45100	52612.4444444444	-7512.44444444445
86	115801	114206.088888889	1594.91111111111
87	186310	230150.75	-43840.75
88	71960	114206.088888889	-42246.0888888889
89	80105	114206.088888889	-34101.0888888889
90	107728	114206.088888889	-6478.08888888889
91	98707	81729.7777777778	16977.2222222222
92	136234	139143.9	-2909.89999999999
93	136781	114206.088888889	22574.9111111111
94	105863	114206.088888889	-8343.08888888889
95	49164	81729.7777777778	-32565.7777777778
96	189493	230150.75	-40657.75
97	169406	139143.9	30262.1
98	19349	9070.4	10278.6
99	160819	114206.088888889	46612.9111111111
100	109510	114206.088888889	-4696.08888888889
101	43803	52612.4444444444	-8809.44444444445
102	47062	81729.7777777778	-34667.7777777778
103	110845	114206.088888889	-3361.08888888889
104	92517	114206.088888889	-21689.0888888889
105	58660	81729.7777777778	-23069.7777777778
106	27676	52612.4444444444	-24936.4444444444
107	98550	139143.9	-40593.9
108	43646	52612.4444444444	-8966.44444444445
109	0	9070.4	-9070.4
110	75566	81729.7777777778	-6163.77777777778
111	57359	52612.4444444444	4746.55555555555
112	104330	81729.7777777778	22600.2222222222
113	70369	114206.088888889	-43837.0888888889
114	65494	114206.088888889	-48712.0888888889
115	3616	9070.4	-5454.4
116	0	9070.4	-9070.4
117	143931	114206.088888889	29724.9111111111
118	117946	139143.9	-21197.9
119	137332	114206.088888889	23125.9111111111
120	84336	81729.7777777778	2606.22222222222
121	43410	9070.4	34339.6
122	137585	139143.9	-1558.89999999999
123	79015	81729.7777777778	-2714.77777777778
124	101354	139143.9	-37789.9
125	57586	52612.4444444444	4973.55555555555
126	19764	9070.4	10693.6
127	105757	114206.088888889	-8449.08888888889
128	103651	81729.7777777778	21921.2222222222
129	113402	81729.7777777778	31672.2222222222
130	11796	9070.4	2725.6
131	7627	9070.4	-1443.4
132	121085	114206.088888889	6878.91111111111
133	6836	9070.4	-2234.4
134	139563	139143.9	419.100000000006
135	5118	9070.4	-3952.4
136	40248	52612.4444444444	-12364.4444444444
137	0	9070.4	-9070.4
138	95079	114206.088888889	-19127.0888888889
139	80763	81729.7777777778	-966.777777777781
140	7131	9070.4	-1939.4
141	4194	9070.4	-4876.4
142	60378	52612.4444444444	7765.55555555555
143	109173	139143.9	-29970.9
144	83484	52612.4444444444	30871.5555555556

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 = 2 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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