Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationMon, 19 Dec 2011 07:42:11 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/19/t1324298548xgnqrbd4xb8k3ja.htm/, Retrieved Thu, 31 Oct 2024 23:22:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157332, Retrieved Thu, 31 Oct 2024 23:22:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121
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]
-   PD    [Recursive Partitioning (Regression Trees)] [Regression trees] [2011-12-19 12:42:11] [3ce5305e82abe5b27e1176cc99946857] [Current]
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Dataseries X:
1683	150596	84	535	109	0	37	18
1323	154801	50	396	73	1	42	20
192	7215	18	72	1	0	0	0
2172	122139	91	617	154	0	49	26
3335	221399	129	1118	124	0	76	30
6310	441870	237	1755	276	1	118	34
1478	134379	52	498	89	1	42	23
1324	140428	53	355	54	0	57	30
1488	103255	40	413	87	0	45	30
2756	271630	91	891	129	1	67	26
1931	121593	71	629	158	2	50	24
1966	172071	63	611	113	0	71	30
1575	83707	94	564	75	0	41	19
2855	197412	98	964	255	4	66	25
1263	134398	48	362	50	4	42	17
1479	139224	73	442	81	3	54	19
1636	134153	52	391	92	0	75	33
1076	64149	52	305	72	5	0	15
2376	122294	82	721	142	0	54	34
678	24889	22	206	47	0	13	15
902	52197	52	310	40	0	16	15
2308	188915	89	686	94	0	77	27
1590	163147	66	572	127	0	34	25
1863	98575	48	558	164	1	38	34
1799	143546	80	569	41	1	50	21
1385	139780	25	513	160	0	39	21
1870	163784	146	602	90	0	54	25
1161	152479	75	276	55	0	67	28
2417	304108	109	791	78	0	55	26
1952	184024	40	815	90	0	52	20
1514	151621	41	427	76	0	50	28
1487	164516	41	496	111	2	54	20
2051	120179	94	653	87	4	53	17
2843	214701	116	857	302	0	76	25
2216	196865	48	736	84	1	52	24
1	0	1	0	0	0	0	0
1830	181527	57	862	58	0	46	27
1563	93107	49	483	137	3	44	14
2046	129352	45	495	267	9	35	32
2005	229143	58	749	56	0	82	31
1934	177063	67	627	94	2	70	21
1572	126602	53	597	62	0	31	34
950	93742	29	348	35	2	25	23
1877	152153	72	711	59	1	48	24
1036	95704	42	322	46	2	44	22
1097	139793	84	280	40	2	40	22
730	76348	30	205	49	1	23	35
1918	188980	86	648	114	0	63	21
1826	172100	79	580	113	1	43	31
2444	146552	54	875	171	7	62	26
658	48188	28	205	37	0	12	22
1425	109185	60	363	51	0	63	21
2246	263652	68	757	89	0	60	27
1899	215609	75	647	67	0	53	26
1630	174876	54	584	49	1	53	33
1496	115124	49	457	74	6	35	11
1681	179712	60	438	58	0	49	26
816	70369	20	235	72	0	25	26
902	109215	58	312	30	0	47	21
2606	166096	85	877	59	10	30	38
1557	130414	51	454	65	6	50	29
1780	102057	71	668	81	0	36	19
1265	115310	56	346	84	11	43	19
1117	101181	32	377	46	3	44	24
1069	135228	31	365	56	0	14	26
1229	94982	37	391	36	0	38	29
2155	166919	67	476	84	8	58	34
2500	118169	64	747	152	2	68	25
1003	102361	36	246	48	0	48	24
340	31970	15	101	40	0	5	21
2586	200413	107	901	135	3	53	19
1119	103381	58	334	80	1	36	12
1251	94940	61	404	60	2	62	28
1516	101560	65	442	89	1	46	21
2473	144176	60	627	89	0	67	34
1288	71921	37	345	79	2	2	32
1911	126905	54	538	111	1	64	27
2279	131184	87	741	67	0	59	26
816	60138	23	253	76	0	16	21
1234	84971	71	395	105	0	34	31
907	80420	64	211	49	0	54	26
1827	233569	57	670	57	0	39	26
841	56252	25	244	49	0	26	23
1309	97181	32	438	132	0	37	25
764	50800	41	255	49	0	17	22
1439	125941	45	434	71	0	32	26
2500	211032	210	613	100	0	55	33
974	71960	92	233	71	0	39	22
1152	90379	53	360	49	6	39	24
1261	125650	47	486	72	0	28	21
1508	115572	36	535	59	5	45	28
2005	136266	67	585	86	1	66	22
1191	146715	55	402	65	0	39	22
1265	124626	57	466	81	0	27	15
761	49176	33	291	30	0	22	13
2156	212926	102	691	166	0	43	36
1689	173884	55	515	89	0	88	24
223	19349	12	67	15	0	13	1
2074	181141	95	712	104	3	23	24
1879	145502	70	770	61	0	40	31
566	45448	26	247	11	0	8	4
802	58280	20	240	44	0	41	20
1131	115944	44	360	84	0	51	23
981	94341	52	249	66	1	24	23
591	59090	37	138	27	0	23	12
596	27676	22	194	59	0	2	16
1261	120586	41	285	126	0	78	28
861	88011	31	227	32	0	12	10
0	0	0	0	0	0	0	0
1030	85610	31	306	58	0	46	25
991	84193	58	328	52	0	22	21
1178	117769	39	397	49	0	49	21
1200	107653	56	369	64	0	52	21
849	71894	57	287	71	0	36	21
78	3616	5	14	5	0	0	0
0	0	0	0	0	0	0	0
924	154806	38	301	70	0	35	23
1480	136061	73	535	72	0	68	29
1870	141822	89	530	118	1	26	27
861	106515	37	272	56	0	32	23
778	43410	19	292	63	0	7	1
1533	146920	64	458	88	1	67	25
889	88874	38	241	46	0	30	17
1705	111924	49	497	60	8	55	29
700	60373	39	165	29	3	3	12
285	19764	12	75	19	1	10	2
1490	121665	46	461	58	2	46	18
981	108685	26	341	66	0	23	25
1368	124493	37	446	97	0	43	29
256	11796	9	79	22	0	1	2
98	10674	9	33	7	0	0	0
1317	131263	52	449	37	0	33	18
41	6836	3	11	5	0	0	1
1768	153278	55	606	48	5	48	21
42	5118	3	6	1	0	5	0
528	40248	16	183	34	1	8	4
0	0	0	0	0	0	0	0
938	100728	42	310	49	0	25	25
1245	84267	36	245	44	0	21	26
81	7131	4	27	0	1	0	0
257	8812	13	97	18	0	0	4
891	63952	22	247	48	1	15	17
1114	120111	47	273	54	0	47	21
1079	94127	18	386	50	1	17	22




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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=157332&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=157332&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157332&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Goodness of Fit
Correlation0.8718
R-squared0.76
RMSE32404.3956

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8718 \tabularnewline
R-squared & 0.76 \tabularnewline
RMSE & 32404.3956 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157332&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8718[/C][/ROW]
[ROW][C]R-squared[/C][C]0.76[/C][/ROW]
[ROW][C]RMSE[/C][C]32404.3956[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157332&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157332&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
Correlation0.8718
R-squared0.76
RMSE32404.3956







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1150596156470.944444444-5874.94444444444
2154801119306.69565217435494.3043478261
372158818.73333333333-1603.73333333333
4122139156470.944444444-34331.9444444444
5221399226287.785714286-4888.78571428571
6441870226287.785714286215582.214285714
7134379119306.69565217415072.3043478261
8140428119306.69565217421121.3043478261
9103255119306.695652174-16051.6956521739
10271630226287.78571428645342.2142857143
11121593156470.944444444-34877.9444444444
12172071156470.94444444415600.0555555556
1383707119306.695652174-35599.6956521739
14197412226287.785714286-28875.7857142857
15134398119306.69565217415091.3043478261
16139224119306.69565217419917.3043478261
17134153156470.944444444-22317.9444444444
186414993138.1-28989.1
19122294156470.944444444-34176.9444444444
202488951379-26490
215219782634.75-30437.75
22188915156470.94444444432444.0555555556
23163147156470.9444444446676.05555555556
2498575156470.944444444-57895.9444444444
25143546156470.944444444-12924.9444444444
26139780119306.69565217420473.3043478261
27163784156470.9444444447313.05555555556
28152479119306.69565217433172.3043478261
29304108226287.78571428677820.2142857143
30184024226287.785714286-42263.7857142857
31151621119306.69565217432314.3043478261
32164516119306.69565217445209.3043478261
33120179156470.944444444-36291.9444444444
34214701226287.785714286-11586.7857142857
35196865156470.94444444440394.0555555556
3608818.73333333333-8818.73333333333
37181527226287.785714286-44760.7857142857
3893107119306.695652174-26199.6956521739
39129352156470.944444444-27118.9444444444
40229143226287.7857142862855.21428571429
41177063156470.94444444420592.0555555556
42126602119306.6956521747295.30434782608
439374293138.1603.899999999994
44152153156470.944444444-4317.94444444444
4595704119306.695652174-23602.6956521739
46139793119306.69565217420486.3043478261
47763485137924969
48188980156470.94444444432509.0555555556
49172100156470.94444444415629.0555555556
50146552226287.785714286-79735.7857142857
514818851379-3191
52109185119306.695652174-10121.6956521739
53263652226287.78571428637364.2142857143
54215609156470.94444444459138.0555555556
55174876156470.94444444418405.0555555556
56115124119306.695652174-4182.69565217392
57179712156470.94444444423241.0555555556
58703695137918990
5910921582634.7526580.25
60166096226287.785714286-60191.7857142857
61130414119306.69565217411107.3043478261
62102057156470.944444444-54413.9444444444
63115310119306.695652174-3996.69565217392
64101181119306.695652174-18125.6956521739
6513522893138.142089.9
6694982119306.695652174-24324.6956521739
67166919156470.94444444410448.0555555556
68118169156470.944444444-38301.9444444444
69102361119306.695652174-16945.6956521739
70319708818.7333333333323151.2666666667
71200413226287.785714286-25874.7857142857
72103381119306.695652174-15925.6956521739
7394940119306.695652174-24366.6956521739
74101560119306.695652174-17746.6956521739
75144176156470.944444444-12294.9444444444
767192193138.1-21217.1
77126905156470.944444444-29565.9444444444
78131184156470.944444444-25286.9444444444
7960138513798759
8084971119306.695652174-34335.6956521739
818042082634.75-2214.75
82233569156470.94444444477098.0555555556
8356252513794873
8497181119306.695652174-22125.6956521739
855080051379-579
86125941119306.6956521746634.30434782608
87211032156470.94444444454561.0555555556
8871960119306.695652174-47346.6956521739
8990379119306.695652174-28927.6956521739
90125650119306.6956521746343.30434782608
91115572119306.695652174-3734.69565217392
92136266156470.944444444-20204.9444444444
93146715119306.69565217427408.3043478261
94124626119306.6956521745319.30434782608
954917651379-2203
96212926156470.94444444456455.0555555556
97173884156470.94444444417413.0555555556
98193498818.7333333333310530.2666666667
99181141156470.94444444424670.0555555556
100145502226287.785714286-80785.7857142857
1014544851379-5931
10258280513796901
103115944119306.695652174-3362.69565217392
1049434193138.11202.89999999999
10559090513797711
1062767651379-23703
107120586119306.6956521741279.30434782608
1088801182634.755376.25
10908818.73333333333-8818.73333333333
11085610119306.695652174-33696.6956521739
1118419393138.1-8945.10000000001
112117769119306.695652174-1537.69565217392
113107653119306.695652174-11653.6956521739
1147189482634.75-10740.75
11536168818.73333333333-5202.73333333333
11608818.73333333333-8818.73333333333
117154806119306.69565217435499.3043478261
118136061119306.69565217416754.3043478261
119141822156470.944444444-14648.9444444444
12010651582634.7523880.25
1214341051379-7969
122146920119306.69565217427613.3043478261
1238887482634.756239.25
124111924156470.944444444-44546.9444444444
12560373513798994
126197648818.7333333333310945.2666666667
127121665119306.6956521742358.30434782608
12810868593138.115546.9
129124493119306.6956521745186.30434782608
130117968818.733333333332977.26666666667
131106748818.733333333331855.26666666667
132131263119306.69565217411956.3043478261
13368368818.73333333333-1982.73333333333
134153278156470.944444444-3192.94444444444
13551188818.73333333333-3700.73333333333
1364024851379-11131
13708818.73333333333-8818.73333333333
13810072893138.17589.89999999999
1398426793138.1-8871.10000000001
14071318818.73333333333-1687.73333333333
14188128818.73333333333-6.73333333333358
1426395282634.75-18682.75
143120111119306.695652174804.304347826081
1449412793138.1988.899999999994

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 150596 & 156470.944444444 & -5874.94444444444 \tabularnewline
2 & 154801 & 119306.695652174 & 35494.3043478261 \tabularnewline
3 & 7215 & 8818.73333333333 & -1603.73333333333 \tabularnewline
4 & 122139 & 156470.944444444 & -34331.9444444444 \tabularnewline
5 & 221399 & 226287.785714286 & -4888.78571428571 \tabularnewline
6 & 441870 & 226287.785714286 & 215582.214285714 \tabularnewline
7 & 134379 & 119306.695652174 & 15072.3043478261 \tabularnewline
8 & 140428 & 119306.695652174 & 21121.3043478261 \tabularnewline
9 & 103255 & 119306.695652174 & -16051.6956521739 \tabularnewline
10 & 271630 & 226287.785714286 & 45342.2142857143 \tabularnewline
11 & 121593 & 156470.944444444 & -34877.9444444444 \tabularnewline
12 & 172071 & 156470.944444444 & 15600.0555555556 \tabularnewline
13 & 83707 & 119306.695652174 & -35599.6956521739 \tabularnewline
14 & 197412 & 226287.785714286 & -28875.7857142857 \tabularnewline
15 & 134398 & 119306.695652174 & 15091.3043478261 \tabularnewline
16 & 139224 & 119306.695652174 & 19917.3043478261 \tabularnewline
17 & 134153 & 156470.944444444 & -22317.9444444444 \tabularnewline
18 & 64149 & 93138.1 & -28989.1 \tabularnewline
19 & 122294 & 156470.944444444 & -34176.9444444444 \tabularnewline
20 & 24889 & 51379 & -26490 \tabularnewline
21 & 52197 & 82634.75 & -30437.75 \tabularnewline
22 & 188915 & 156470.944444444 & 32444.0555555556 \tabularnewline
23 & 163147 & 156470.944444444 & 6676.05555555556 \tabularnewline
24 & 98575 & 156470.944444444 & -57895.9444444444 \tabularnewline
25 & 143546 & 156470.944444444 & -12924.9444444444 \tabularnewline
26 & 139780 & 119306.695652174 & 20473.3043478261 \tabularnewline
27 & 163784 & 156470.944444444 & 7313.05555555556 \tabularnewline
28 & 152479 & 119306.695652174 & 33172.3043478261 \tabularnewline
29 & 304108 & 226287.785714286 & 77820.2142857143 \tabularnewline
30 & 184024 & 226287.785714286 & -42263.7857142857 \tabularnewline
31 & 151621 & 119306.695652174 & 32314.3043478261 \tabularnewline
32 & 164516 & 119306.695652174 & 45209.3043478261 \tabularnewline
33 & 120179 & 156470.944444444 & -36291.9444444444 \tabularnewline
34 & 214701 & 226287.785714286 & -11586.7857142857 \tabularnewline
35 & 196865 & 156470.944444444 & 40394.0555555556 \tabularnewline
36 & 0 & 8818.73333333333 & -8818.73333333333 \tabularnewline
37 & 181527 & 226287.785714286 & -44760.7857142857 \tabularnewline
38 & 93107 & 119306.695652174 & -26199.6956521739 \tabularnewline
39 & 129352 & 156470.944444444 & -27118.9444444444 \tabularnewline
40 & 229143 & 226287.785714286 & 2855.21428571429 \tabularnewline
41 & 177063 & 156470.944444444 & 20592.0555555556 \tabularnewline
42 & 126602 & 119306.695652174 & 7295.30434782608 \tabularnewline
43 & 93742 & 93138.1 & 603.899999999994 \tabularnewline
44 & 152153 & 156470.944444444 & -4317.94444444444 \tabularnewline
45 & 95704 & 119306.695652174 & -23602.6956521739 \tabularnewline
46 & 139793 & 119306.695652174 & 20486.3043478261 \tabularnewline
47 & 76348 & 51379 & 24969 \tabularnewline
48 & 188980 & 156470.944444444 & 32509.0555555556 \tabularnewline
49 & 172100 & 156470.944444444 & 15629.0555555556 \tabularnewline
50 & 146552 & 226287.785714286 & -79735.7857142857 \tabularnewline
51 & 48188 & 51379 & -3191 \tabularnewline
52 & 109185 & 119306.695652174 & -10121.6956521739 \tabularnewline
53 & 263652 & 226287.785714286 & 37364.2142857143 \tabularnewline
54 & 215609 & 156470.944444444 & 59138.0555555556 \tabularnewline
55 & 174876 & 156470.944444444 & 18405.0555555556 \tabularnewline
56 & 115124 & 119306.695652174 & -4182.69565217392 \tabularnewline
57 & 179712 & 156470.944444444 & 23241.0555555556 \tabularnewline
58 & 70369 & 51379 & 18990 \tabularnewline
59 & 109215 & 82634.75 & 26580.25 \tabularnewline
60 & 166096 & 226287.785714286 & -60191.7857142857 \tabularnewline
61 & 130414 & 119306.695652174 & 11107.3043478261 \tabularnewline
62 & 102057 & 156470.944444444 & -54413.9444444444 \tabularnewline
63 & 115310 & 119306.695652174 & -3996.69565217392 \tabularnewline
64 & 101181 & 119306.695652174 & -18125.6956521739 \tabularnewline
65 & 135228 & 93138.1 & 42089.9 \tabularnewline
66 & 94982 & 119306.695652174 & -24324.6956521739 \tabularnewline
67 & 166919 & 156470.944444444 & 10448.0555555556 \tabularnewline
68 & 118169 & 156470.944444444 & -38301.9444444444 \tabularnewline
69 & 102361 & 119306.695652174 & -16945.6956521739 \tabularnewline
70 & 31970 & 8818.73333333333 & 23151.2666666667 \tabularnewline
71 & 200413 & 226287.785714286 & -25874.7857142857 \tabularnewline
72 & 103381 & 119306.695652174 & -15925.6956521739 \tabularnewline
73 & 94940 & 119306.695652174 & -24366.6956521739 \tabularnewline
74 & 101560 & 119306.695652174 & -17746.6956521739 \tabularnewline
75 & 144176 & 156470.944444444 & -12294.9444444444 \tabularnewline
76 & 71921 & 93138.1 & -21217.1 \tabularnewline
77 & 126905 & 156470.944444444 & -29565.9444444444 \tabularnewline
78 & 131184 & 156470.944444444 & -25286.9444444444 \tabularnewline
79 & 60138 & 51379 & 8759 \tabularnewline
80 & 84971 & 119306.695652174 & -34335.6956521739 \tabularnewline
81 & 80420 & 82634.75 & -2214.75 \tabularnewline
82 & 233569 & 156470.944444444 & 77098.0555555556 \tabularnewline
83 & 56252 & 51379 & 4873 \tabularnewline
84 & 97181 & 119306.695652174 & -22125.6956521739 \tabularnewline
85 & 50800 & 51379 & -579 \tabularnewline
86 & 125941 & 119306.695652174 & 6634.30434782608 \tabularnewline
87 & 211032 & 156470.944444444 & 54561.0555555556 \tabularnewline
88 & 71960 & 119306.695652174 & -47346.6956521739 \tabularnewline
89 & 90379 & 119306.695652174 & -28927.6956521739 \tabularnewline
90 & 125650 & 119306.695652174 & 6343.30434782608 \tabularnewline
91 & 115572 & 119306.695652174 & -3734.69565217392 \tabularnewline
92 & 136266 & 156470.944444444 & -20204.9444444444 \tabularnewline
93 & 146715 & 119306.695652174 & 27408.3043478261 \tabularnewline
94 & 124626 & 119306.695652174 & 5319.30434782608 \tabularnewline
95 & 49176 & 51379 & -2203 \tabularnewline
96 & 212926 & 156470.944444444 & 56455.0555555556 \tabularnewline
97 & 173884 & 156470.944444444 & 17413.0555555556 \tabularnewline
98 & 19349 & 8818.73333333333 & 10530.2666666667 \tabularnewline
99 & 181141 & 156470.944444444 & 24670.0555555556 \tabularnewline
100 & 145502 & 226287.785714286 & -80785.7857142857 \tabularnewline
101 & 45448 & 51379 & -5931 \tabularnewline
102 & 58280 & 51379 & 6901 \tabularnewline
103 & 115944 & 119306.695652174 & -3362.69565217392 \tabularnewline
104 & 94341 & 93138.1 & 1202.89999999999 \tabularnewline
105 & 59090 & 51379 & 7711 \tabularnewline
106 & 27676 & 51379 & -23703 \tabularnewline
107 & 120586 & 119306.695652174 & 1279.30434782608 \tabularnewline
108 & 88011 & 82634.75 & 5376.25 \tabularnewline
109 & 0 & 8818.73333333333 & -8818.73333333333 \tabularnewline
110 & 85610 & 119306.695652174 & -33696.6956521739 \tabularnewline
111 & 84193 & 93138.1 & -8945.10000000001 \tabularnewline
112 & 117769 & 119306.695652174 & -1537.69565217392 \tabularnewline
113 & 107653 & 119306.695652174 & -11653.6956521739 \tabularnewline
114 & 71894 & 82634.75 & -10740.75 \tabularnewline
115 & 3616 & 8818.73333333333 & -5202.73333333333 \tabularnewline
116 & 0 & 8818.73333333333 & -8818.73333333333 \tabularnewline
117 & 154806 & 119306.695652174 & 35499.3043478261 \tabularnewline
118 & 136061 & 119306.695652174 & 16754.3043478261 \tabularnewline
119 & 141822 & 156470.944444444 & -14648.9444444444 \tabularnewline
120 & 106515 & 82634.75 & 23880.25 \tabularnewline
121 & 43410 & 51379 & -7969 \tabularnewline
122 & 146920 & 119306.695652174 & 27613.3043478261 \tabularnewline
123 & 88874 & 82634.75 & 6239.25 \tabularnewline
124 & 111924 & 156470.944444444 & -44546.9444444444 \tabularnewline
125 & 60373 & 51379 & 8994 \tabularnewline
126 & 19764 & 8818.73333333333 & 10945.2666666667 \tabularnewline
127 & 121665 & 119306.695652174 & 2358.30434782608 \tabularnewline
128 & 108685 & 93138.1 & 15546.9 \tabularnewline
129 & 124493 & 119306.695652174 & 5186.30434782608 \tabularnewline
130 & 11796 & 8818.73333333333 & 2977.26666666667 \tabularnewline
131 & 10674 & 8818.73333333333 & 1855.26666666667 \tabularnewline
132 & 131263 & 119306.695652174 & 11956.3043478261 \tabularnewline
133 & 6836 & 8818.73333333333 & -1982.73333333333 \tabularnewline
134 & 153278 & 156470.944444444 & -3192.94444444444 \tabularnewline
135 & 5118 & 8818.73333333333 & -3700.73333333333 \tabularnewline
136 & 40248 & 51379 & -11131 \tabularnewline
137 & 0 & 8818.73333333333 & -8818.73333333333 \tabularnewline
138 & 100728 & 93138.1 & 7589.89999999999 \tabularnewline
139 & 84267 & 93138.1 & -8871.10000000001 \tabularnewline
140 & 7131 & 8818.73333333333 & -1687.73333333333 \tabularnewline
141 & 8812 & 8818.73333333333 & -6.73333333333358 \tabularnewline
142 & 63952 & 82634.75 & -18682.75 \tabularnewline
143 & 120111 & 119306.695652174 & 804.304347826081 \tabularnewline
144 & 94127 & 93138.1 & 988.899999999994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157332&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]150596[/C][C]156470.944444444[/C][C]-5874.94444444444[/C][/ROW]
[ROW][C]2[/C][C]154801[/C][C]119306.695652174[/C][C]35494.3043478261[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]8818.73333333333[/C][C]-1603.73333333333[/C][/ROW]
[ROW][C]4[/C][C]122139[/C][C]156470.944444444[/C][C]-34331.9444444444[/C][/ROW]
[ROW][C]5[/C][C]221399[/C][C]226287.785714286[/C][C]-4888.78571428571[/C][/ROW]
[ROW][C]6[/C][C]441870[/C][C]226287.785714286[/C][C]215582.214285714[/C][/ROW]
[ROW][C]7[/C][C]134379[/C][C]119306.695652174[/C][C]15072.3043478261[/C][/ROW]
[ROW][C]8[/C][C]140428[/C][C]119306.695652174[/C][C]21121.3043478261[/C][/ROW]
[ROW][C]9[/C][C]103255[/C][C]119306.695652174[/C][C]-16051.6956521739[/C][/ROW]
[ROW][C]10[/C][C]271630[/C][C]226287.785714286[/C][C]45342.2142857143[/C][/ROW]
[ROW][C]11[/C][C]121593[/C][C]156470.944444444[/C][C]-34877.9444444444[/C][/ROW]
[ROW][C]12[/C][C]172071[/C][C]156470.944444444[/C][C]15600.0555555556[/C][/ROW]
[ROW][C]13[/C][C]83707[/C][C]119306.695652174[/C][C]-35599.6956521739[/C][/ROW]
[ROW][C]14[/C][C]197412[/C][C]226287.785714286[/C][C]-28875.7857142857[/C][/ROW]
[ROW][C]15[/C][C]134398[/C][C]119306.695652174[/C][C]15091.3043478261[/C][/ROW]
[ROW][C]16[/C][C]139224[/C][C]119306.695652174[/C][C]19917.3043478261[/C][/ROW]
[ROW][C]17[/C][C]134153[/C][C]156470.944444444[/C][C]-22317.9444444444[/C][/ROW]
[ROW][C]18[/C][C]64149[/C][C]93138.1[/C][C]-28989.1[/C][/ROW]
[ROW][C]19[/C][C]122294[/C][C]156470.944444444[/C][C]-34176.9444444444[/C][/ROW]
[ROW][C]20[/C][C]24889[/C][C]51379[/C][C]-26490[/C][/ROW]
[ROW][C]21[/C][C]52197[/C][C]82634.75[/C][C]-30437.75[/C][/ROW]
[ROW][C]22[/C][C]188915[/C][C]156470.944444444[/C][C]32444.0555555556[/C][/ROW]
[ROW][C]23[/C][C]163147[/C][C]156470.944444444[/C][C]6676.05555555556[/C][/ROW]
[ROW][C]24[/C][C]98575[/C][C]156470.944444444[/C][C]-57895.9444444444[/C][/ROW]
[ROW][C]25[/C][C]143546[/C][C]156470.944444444[/C][C]-12924.9444444444[/C][/ROW]
[ROW][C]26[/C][C]139780[/C][C]119306.695652174[/C][C]20473.3043478261[/C][/ROW]
[ROW][C]27[/C][C]163784[/C][C]156470.944444444[/C][C]7313.05555555556[/C][/ROW]
[ROW][C]28[/C][C]152479[/C][C]119306.695652174[/C][C]33172.3043478261[/C][/ROW]
[ROW][C]29[/C][C]304108[/C][C]226287.785714286[/C][C]77820.2142857143[/C][/ROW]
[ROW][C]30[/C][C]184024[/C][C]226287.785714286[/C][C]-42263.7857142857[/C][/ROW]
[ROW][C]31[/C][C]151621[/C][C]119306.695652174[/C][C]32314.3043478261[/C][/ROW]
[ROW][C]32[/C][C]164516[/C][C]119306.695652174[/C][C]45209.3043478261[/C][/ROW]
[ROW][C]33[/C][C]120179[/C][C]156470.944444444[/C][C]-36291.9444444444[/C][/ROW]
[ROW][C]34[/C][C]214701[/C][C]226287.785714286[/C][C]-11586.7857142857[/C][/ROW]
[ROW][C]35[/C][C]196865[/C][C]156470.944444444[/C][C]40394.0555555556[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]8818.73333333333[/C][C]-8818.73333333333[/C][/ROW]
[ROW][C]37[/C][C]181527[/C][C]226287.785714286[/C][C]-44760.7857142857[/C][/ROW]
[ROW][C]38[/C][C]93107[/C][C]119306.695652174[/C][C]-26199.6956521739[/C][/ROW]
[ROW][C]39[/C][C]129352[/C][C]156470.944444444[/C][C]-27118.9444444444[/C][/ROW]
[ROW][C]40[/C][C]229143[/C][C]226287.785714286[/C][C]2855.21428571429[/C][/ROW]
[ROW][C]41[/C][C]177063[/C][C]156470.944444444[/C][C]20592.0555555556[/C][/ROW]
[ROW][C]42[/C][C]126602[/C][C]119306.695652174[/C][C]7295.30434782608[/C][/ROW]
[ROW][C]43[/C][C]93742[/C][C]93138.1[/C][C]603.899999999994[/C][/ROW]
[ROW][C]44[/C][C]152153[/C][C]156470.944444444[/C][C]-4317.94444444444[/C][/ROW]
[ROW][C]45[/C][C]95704[/C][C]119306.695652174[/C][C]-23602.6956521739[/C][/ROW]
[ROW][C]46[/C][C]139793[/C][C]119306.695652174[/C][C]20486.3043478261[/C][/ROW]
[ROW][C]47[/C][C]76348[/C][C]51379[/C][C]24969[/C][/ROW]
[ROW][C]48[/C][C]188980[/C][C]156470.944444444[/C][C]32509.0555555556[/C][/ROW]
[ROW][C]49[/C][C]172100[/C][C]156470.944444444[/C][C]15629.0555555556[/C][/ROW]
[ROW][C]50[/C][C]146552[/C][C]226287.785714286[/C][C]-79735.7857142857[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]51379[/C][C]-3191[/C][/ROW]
[ROW][C]52[/C][C]109185[/C][C]119306.695652174[/C][C]-10121.6956521739[/C][/ROW]
[ROW][C]53[/C][C]263652[/C][C]226287.785714286[/C][C]37364.2142857143[/C][/ROW]
[ROW][C]54[/C][C]215609[/C][C]156470.944444444[/C][C]59138.0555555556[/C][/ROW]
[ROW][C]55[/C][C]174876[/C][C]156470.944444444[/C][C]18405.0555555556[/C][/ROW]
[ROW][C]56[/C][C]115124[/C][C]119306.695652174[/C][C]-4182.69565217392[/C][/ROW]
[ROW][C]57[/C][C]179712[/C][C]156470.944444444[/C][C]23241.0555555556[/C][/ROW]
[ROW][C]58[/C][C]70369[/C][C]51379[/C][C]18990[/C][/ROW]
[ROW][C]59[/C][C]109215[/C][C]82634.75[/C][C]26580.25[/C][/ROW]
[ROW][C]60[/C][C]166096[/C][C]226287.785714286[/C][C]-60191.7857142857[/C][/ROW]
[ROW][C]61[/C][C]130414[/C][C]119306.695652174[/C][C]11107.3043478261[/C][/ROW]
[ROW][C]62[/C][C]102057[/C][C]156470.944444444[/C][C]-54413.9444444444[/C][/ROW]
[ROW][C]63[/C][C]115310[/C][C]119306.695652174[/C][C]-3996.69565217392[/C][/ROW]
[ROW][C]64[/C][C]101181[/C][C]119306.695652174[/C][C]-18125.6956521739[/C][/ROW]
[ROW][C]65[/C][C]135228[/C][C]93138.1[/C][C]42089.9[/C][/ROW]
[ROW][C]66[/C][C]94982[/C][C]119306.695652174[/C][C]-24324.6956521739[/C][/ROW]
[ROW][C]67[/C][C]166919[/C][C]156470.944444444[/C][C]10448.0555555556[/C][/ROW]
[ROW][C]68[/C][C]118169[/C][C]156470.944444444[/C][C]-38301.9444444444[/C][/ROW]
[ROW][C]69[/C][C]102361[/C][C]119306.695652174[/C][C]-16945.6956521739[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]8818.73333333333[/C][C]23151.2666666667[/C][/ROW]
[ROW][C]71[/C][C]200413[/C][C]226287.785714286[/C][C]-25874.7857142857[/C][/ROW]
[ROW][C]72[/C][C]103381[/C][C]119306.695652174[/C][C]-15925.6956521739[/C][/ROW]
[ROW][C]73[/C][C]94940[/C][C]119306.695652174[/C][C]-24366.6956521739[/C][/ROW]
[ROW][C]74[/C][C]101560[/C][C]119306.695652174[/C][C]-17746.6956521739[/C][/ROW]
[ROW][C]75[/C][C]144176[/C][C]156470.944444444[/C][C]-12294.9444444444[/C][/ROW]
[ROW][C]76[/C][C]71921[/C][C]93138.1[/C][C]-21217.1[/C][/ROW]
[ROW][C]77[/C][C]126905[/C][C]156470.944444444[/C][C]-29565.9444444444[/C][/ROW]
[ROW][C]78[/C][C]131184[/C][C]156470.944444444[/C][C]-25286.9444444444[/C][/ROW]
[ROW][C]79[/C][C]60138[/C][C]51379[/C][C]8759[/C][/ROW]
[ROW][C]80[/C][C]84971[/C][C]119306.695652174[/C][C]-34335.6956521739[/C][/ROW]
[ROW][C]81[/C][C]80420[/C][C]82634.75[/C][C]-2214.75[/C][/ROW]
[ROW][C]82[/C][C]233569[/C][C]156470.944444444[/C][C]77098.0555555556[/C][/ROW]
[ROW][C]83[/C][C]56252[/C][C]51379[/C][C]4873[/C][/ROW]
[ROW][C]84[/C][C]97181[/C][C]119306.695652174[/C][C]-22125.6956521739[/C][/ROW]
[ROW][C]85[/C][C]50800[/C][C]51379[/C][C]-579[/C][/ROW]
[ROW][C]86[/C][C]125941[/C][C]119306.695652174[/C][C]6634.30434782608[/C][/ROW]
[ROW][C]87[/C][C]211032[/C][C]156470.944444444[/C][C]54561.0555555556[/C][/ROW]
[ROW][C]88[/C][C]71960[/C][C]119306.695652174[/C][C]-47346.6956521739[/C][/ROW]
[ROW][C]89[/C][C]90379[/C][C]119306.695652174[/C][C]-28927.6956521739[/C][/ROW]
[ROW][C]90[/C][C]125650[/C][C]119306.695652174[/C][C]6343.30434782608[/C][/ROW]
[ROW][C]91[/C][C]115572[/C][C]119306.695652174[/C][C]-3734.69565217392[/C][/ROW]
[ROW][C]92[/C][C]136266[/C][C]156470.944444444[/C][C]-20204.9444444444[/C][/ROW]
[ROW][C]93[/C][C]146715[/C][C]119306.695652174[/C][C]27408.3043478261[/C][/ROW]
[ROW][C]94[/C][C]124626[/C][C]119306.695652174[/C][C]5319.30434782608[/C][/ROW]
[ROW][C]95[/C][C]49176[/C][C]51379[/C][C]-2203[/C][/ROW]
[ROW][C]96[/C][C]212926[/C][C]156470.944444444[/C][C]56455.0555555556[/C][/ROW]
[ROW][C]97[/C][C]173884[/C][C]156470.944444444[/C][C]17413.0555555556[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]8818.73333333333[/C][C]10530.2666666667[/C][/ROW]
[ROW][C]99[/C][C]181141[/C][C]156470.944444444[/C][C]24670.0555555556[/C][/ROW]
[ROW][C]100[/C][C]145502[/C][C]226287.785714286[/C][C]-80785.7857142857[/C][/ROW]
[ROW][C]101[/C][C]45448[/C][C]51379[/C][C]-5931[/C][/ROW]
[ROW][C]102[/C][C]58280[/C][C]51379[/C][C]6901[/C][/ROW]
[ROW][C]103[/C][C]115944[/C][C]119306.695652174[/C][C]-3362.69565217392[/C][/ROW]
[ROW][C]104[/C][C]94341[/C][C]93138.1[/C][C]1202.89999999999[/C][/ROW]
[ROW][C]105[/C][C]59090[/C][C]51379[/C][C]7711[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]51379[/C][C]-23703[/C][/ROW]
[ROW][C]107[/C][C]120586[/C][C]119306.695652174[/C][C]1279.30434782608[/C][/ROW]
[ROW][C]108[/C][C]88011[/C][C]82634.75[/C][C]5376.25[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]8818.73333333333[/C][C]-8818.73333333333[/C][/ROW]
[ROW][C]110[/C][C]85610[/C][C]119306.695652174[/C][C]-33696.6956521739[/C][/ROW]
[ROW][C]111[/C][C]84193[/C][C]93138.1[/C][C]-8945.10000000001[/C][/ROW]
[ROW][C]112[/C][C]117769[/C][C]119306.695652174[/C][C]-1537.69565217392[/C][/ROW]
[ROW][C]113[/C][C]107653[/C][C]119306.695652174[/C][C]-11653.6956521739[/C][/ROW]
[ROW][C]114[/C][C]71894[/C][C]82634.75[/C][C]-10740.75[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]8818.73333333333[/C][C]-5202.73333333333[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]8818.73333333333[/C][C]-8818.73333333333[/C][/ROW]
[ROW][C]117[/C][C]154806[/C][C]119306.695652174[/C][C]35499.3043478261[/C][/ROW]
[ROW][C]118[/C][C]136061[/C][C]119306.695652174[/C][C]16754.3043478261[/C][/ROW]
[ROW][C]119[/C][C]141822[/C][C]156470.944444444[/C][C]-14648.9444444444[/C][/ROW]
[ROW][C]120[/C][C]106515[/C][C]82634.75[/C][C]23880.25[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]51379[/C][C]-7969[/C][/ROW]
[ROW][C]122[/C][C]146920[/C][C]119306.695652174[/C][C]27613.3043478261[/C][/ROW]
[ROW][C]123[/C][C]88874[/C][C]82634.75[/C][C]6239.25[/C][/ROW]
[ROW][C]124[/C][C]111924[/C][C]156470.944444444[/C][C]-44546.9444444444[/C][/ROW]
[ROW][C]125[/C][C]60373[/C][C]51379[/C][C]8994[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]8818.73333333333[/C][C]10945.2666666667[/C][/ROW]
[ROW][C]127[/C][C]121665[/C][C]119306.695652174[/C][C]2358.30434782608[/C][/ROW]
[ROW][C]128[/C][C]108685[/C][C]93138.1[/C][C]15546.9[/C][/ROW]
[ROW][C]129[/C][C]124493[/C][C]119306.695652174[/C][C]5186.30434782608[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]8818.73333333333[/C][C]2977.26666666667[/C][/ROW]
[ROW][C]131[/C][C]10674[/C][C]8818.73333333333[/C][C]1855.26666666667[/C][/ROW]
[ROW][C]132[/C][C]131263[/C][C]119306.695652174[/C][C]11956.3043478261[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]8818.73333333333[/C][C]-1982.73333333333[/C][/ROW]
[ROW][C]134[/C][C]153278[/C][C]156470.944444444[/C][C]-3192.94444444444[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]8818.73333333333[/C][C]-3700.73333333333[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]51379[/C][C]-11131[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]8818.73333333333[/C][C]-8818.73333333333[/C][/ROW]
[ROW][C]138[/C][C]100728[/C][C]93138.1[/C][C]7589.89999999999[/C][/ROW]
[ROW][C]139[/C][C]84267[/C][C]93138.1[/C][C]-8871.10000000001[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]8818.73333333333[/C][C]-1687.73333333333[/C][/ROW]
[ROW][C]141[/C][C]8812[/C][C]8818.73333333333[/C][C]-6.73333333333358[/C][/ROW]
[ROW][C]142[/C][C]63952[/C][C]82634.75[/C][C]-18682.75[/C][/ROW]
[ROW][C]143[/C][C]120111[/C][C]119306.695652174[/C][C]804.304347826081[/C][/ROW]
[ROW][C]144[/C][C]94127[/C][C]93138.1[/C][C]988.899999999994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157332&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157332&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
#ActualsForecastsResiduals
1150596156470.944444444-5874.94444444444
2154801119306.69565217435494.3043478261
372158818.73333333333-1603.73333333333
4122139156470.944444444-34331.9444444444
5221399226287.785714286-4888.78571428571
6441870226287.785714286215582.214285714
7134379119306.69565217415072.3043478261
8140428119306.69565217421121.3043478261
9103255119306.695652174-16051.6956521739
10271630226287.78571428645342.2142857143
11121593156470.944444444-34877.9444444444
12172071156470.94444444415600.0555555556
1383707119306.695652174-35599.6956521739
14197412226287.785714286-28875.7857142857
15134398119306.69565217415091.3043478261
16139224119306.69565217419917.3043478261
17134153156470.944444444-22317.9444444444
186414993138.1-28989.1
19122294156470.944444444-34176.9444444444
202488951379-26490
215219782634.75-30437.75
22188915156470.94444444432444.0555555556
23163147156470.9444444446676.05555555556
2498575156470.944444444-57895.9444444444
25143546156470.944444444-12924.9444444444
26139780119306.69565217420473.3043478261
27163784156470.9444444447313.05555555556
28152479119306.69565217433172.3043478261
29304108226287.78571428677820.2142857143
30184024226287.785714286-42263.7857142857
31151621119306.69565217432314.3043478261
32164516119306.69565217445209.3043478261
33120179156470.944444444-36291.9444444444
34214701226287.785714286-11586.7857142857
35196865156470.94444444440394.0555555556
3608818.73333333333-8818.73333333333
37181527226287.785714286-44760.7857142857
3893107119306.695652174-26199.6956521739
39129352156470.944444444-27118.9444444444
40229143226287.7857142862855.21428571429
41177063156470.94444444420592.0555555556
42126602119306.6956521747295.30434782608
439374293138.1603.899999999994
44152153156470.944444444-4317.94444444444
4595704119306.695652174-23602.6956521739
46139793119306.69565217420486.3043478261
47763485137924969
48188980156470.94444444432509.0555555556
49172100156470.94444444415629.0555555556
50146552226287.785714286-79735.7857142857
514818851379-3191
52109185119306.695652174-10121.6956521739
53263652226287.78571428637364.2142857143
54215609156470.94444444459138.0555555556
55174876156470.94444444418405.0555555556
56115124119306.695652174-4182.69565217392
57179712156470.94444444423241.0555555556
58703695137918990
5910921582634.7526580.25
60166096226287.785714286-60191.7857142857
61130414119306.69565217411107.3043478261
62102057156470.944444444-54413.9444444444
63115310119306.695652174-3996.69565217392
64101181119306.695652174-18125.6956521739
6513522893138.142089.9
6694982119306.695652174-24324.6956521739
67166919156470.94444444410448.0555555556
68118169156470.944444444-38301.9444444444
69102361119306.695652174-16945.6956521739
70319708818.7333333333323151.2666666667
71200413226287.785714286-25874.7857142857
72103381119306.695652174-15925.6956521739
7394940119306.695652174-24366.6956521739
74101560119306.695652174-17746.6956521739
75144176156470.944444444-12294.9444444444
767192193138.1-21217.1
77126905156470.944444444-29565.9444444444
78131184156470.944444444-25286.9444444444
7960138513798759
8084971119306.695652174-34335.6956521739
818042082634.75-2214.75
82233569156470.94444444477098.0555555556
8356252513794873
8497181119306.695652174-22125.6956521739
855080051379-579
86125941119306.6956521746634.30434782608
87211032156470.94444444454561.0555555556
8871960119306.695652174-47346.6956521739
8990379119306.695652174-28927.6956521739
90125650119306.6956521746343.30434782608
91115572119306.695652174-3734.69565217392
92136266156470.944444444-20204.9444444444
93146715119306.69565217427408.3043478261
94124626119306.6956521745319.30434782608
954917651379-2203
96212926156470.94444444456455.0555555556
97173884156470.94444444417413.0555555556
98193498818.7333333333310530.2666666667
99181141156470.94444444424670.0555555556
100145502226287.785714286-80785.7857142857
1014544851379-5931
10258280513796901
103115944119306.695652174-3362.69565217392
1049434193138.11202.89999999999
10559090513797711
1062767651379-23703
107120586119306.6956521741279.30434782608
1088801182634.755376.25
10908818.73333333333-8818.73333333333
11085610119306.695652174-33696.6956521739
1118419393138.1-8945.10000000001
112117769119306.695652174-1537.69565217392
113107653119306.695652174-11653.6956521739
1147189482634.75-10740.75
11536168818.73333333333-5202.73333333333
11608818.73333333333-8818.73333333333
117154806119306.69565217435499.3043478261
118136061119306.69565217416754.3043478261
119141822156470.944444444-14648.9444444444
12010651582634.7523880.25
1214341051379-7969
122146920119306.69565217427613.3043478261
1238887482634.756239.25
124111924156470.944444444-44546.9444444444
12560373513798994
126197648818.7333333333310945.2666666667
127121665119306.6956521742358.30434782608
12810868593138.115546.9
129124493119306.6956521745186.30434782608
130117968818.733333333332977.26666666667
131106748818.733333333331855.26666666667
132131263119306.69565217411956.3043478261
13368368818.73333333333-1982.73333333333
134153278156470.944444444-3192.94444444444
13551188818.73333333333-3700.73333333333
1364024851379-11131
13708818.73333333333-8818.73333333333
13810072893138.17589.89999999999
1398426793138.1-8871.10000000001
14071318818.73333333333-1687.73333333333
14188128818.73333333333-6.73333333333358
1426395282634.75-18682.75
143120111119306.695652174804.304347826081
1449412793138.1988.899999999994



Parameters (Session):
par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 2 ; 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')
}