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 computationFri, 23 Dec 2011 08:21:36 -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/23/t1324646551hp59blltkoa6adm.htm/, Retrieved Thu, 31 Oct 2024 23:39:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160383, Retrieved Thu, 31 Oct 2024 23:39:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165
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)] [Paper: Recursive ...] [2010-12-10 09:41:02] [1fd136673b2a4fecb5c545b9b4a05d64]
- R PD      [Recursive Partitioning (Regression Trees)] [] [2011-12-23 13:21:36] [daf26cf00f2f7a7ee0a1368c8ac8117e] [Current]
Feedback Forum

Post a new message
Dataseries X:
112285	3	24188	146283
84786	4	18273	98364
83123	12	14130	86146
101193	2	32287	96933
38361	1	8654	79234
68504	3	9245	42551
119182	0	33251	195663
22807	0	1271	6853
17140	0	5279	21529
116174	5	27101	95757
57635	0	16373	85584
66198	0	19716	143983
71701	7	17753	75851
57793	7	9028	59238
80444	3	18653	93163
53855	9	8828	96037
97668	0	29498	151511
133824	4	27563	136368
101481	3	18293	112642
99645	0	22530	94728
114789	7	15977	105499
99052	0	35082	121527
67654	1	16116	127766
65553	5	15849	98958
97500	7	16026	77900
69112	0	26569	85646
82753	0	24785	98579
85323	5	17569	130767
72654	0	23825	131741
30727	0	7869	53907
77873	0	14975	178812
117478	3	37791	146761
74007	4	9605	82036
90183	1	27295	163253
61542	4	2746	27032
101494	2	34461	171975
27570	0	8098	65990
55813	0	4787	86572
79215	0	24919	159676
1423	0	603	1929
55461	2	16329	85371
31081	1	12558	58391
22996	0	7784	31580
83122	2	28522	136815
70106	10	22265	120642
60578	6	14459	69107
39992	0	14526	50495
79892	5	22240	108016
49810	4	11802	46341
71570	1	7623	78348
100708	2	11912	79336
33032	2	7935	56968
82875	0	18220	93176
139077	8	19199	161632
71595	3	19918	87850
72260	0	21884	127969
5950	0	2694	15049
115762	8	15808	155135
32551	5	3597	25109
31701	3	5296	45824
80670	1	25239	102996
143558	5	29801	160604
117105	1	18450	158051
23789	1	7132	44547
120733	5	34861	162647
105195	0	35940	174141
73107	12	16688	60622
132068	8	24683	179566
149193	8	46230	184301
46821	8	10387	75661
87011	8	21436	96144
95260	2	30546	129847
55183	0	19746	117286
106671	5	15977	71180
73511	8	22583	109377
92945	2	17274	85298
78664	5	16469	73631
70054	12	14251	86767
22618	6	3007	23824
74011	7	16851	93487
83737	2	21113	82981
69094	0	17401	73815
93133	4	23958	94552
95536	3	23567	132190
225920	6	13065	128754
62133	2	15358	66363
61370	0	14587	67808
43836	1	12770	61724
106117	0	24021	131722
38692	5	9648	68580
84651	2	20537	106175
56622	0	7905	55792
15986	0	4527	25157
95364	5	30495	76669
26706	0	7117	57283
89691	1	17719	105805
67267	0	27056	129484
126846	1	33473	72413
41140	1	9758	87831
102860	2	21115	96971
51715	6	7236	71299
55801	1	13790	77494
111813	4	32902	120336
120293	2	25131	93913
138599	3	30910	136048
161647	0	35947	181248
115929	10	29848	146123
24266	0	6943	32036
162901	9	42705	186646
109825	7	31808	102255
129838	0	26675	168237
37510	0	8435	64219
43750	4	7409	19630
40652	4	14993	76825
87771	0	36867	115338
85872	0	33835	109427
89275	0	24164	118168
44418	1	12607	84845
192565	0	22609	153197
35232	1	5892	29877
40909	0	17014	63506
13294	0	5394	22445
32387	4	9178	47695
140867	0	6440	68370
120662	4	21916	146304
21233	4	4011	38233
44332	3	5818	42071
61056	0	18647	50517
101338	0	20556	103950
1168	0	238	5841
13497	5	70	2341
65567	0	22392	84396
25162	4	3913	24610
32334	0	12237	35753
40735	0	8388	55515
91413	1	22120	209056
855	0	338	6622
97068	5	11727	115814
44339	0	3704	11609
14116	0	3988	13155
10288	0	3030	18274
65622	0	13520	72875
16563	0	1421	10112
76643	2	20923	142775
110681	7	20237	68847
29011	1	3219	17659
92696	8	3769	20112
94785	2	12252	61023
8773	0	1888	13983
83209	2	14497	65176
93815	0	28864	132432
86687	0	21721	112494
34553	1	4821	45109
105547	3	33644	170875
103487	0	15923	180759
213688	3	42935	214921
71220	0	18864	100226
23517	0	4977	32043
56926	0	7785	54454
91721	4	17939	78876
115168	4	23436	170745
111194	11	325	6940
51009	0	13539	49025
135777	0	34538	122037
51513	4	12198	53782
74163	0	26924	127748
51633	1	12716	86839
75345	0	8172	44830
33416	0	10855	77395
83305	0	11932	89324
98952	9	14300	103300
102372	1	25515	112283
37238	3	2805	10901
103772	10	29402	120691
123969	5	16440	58106
27142	0	11221	57140
135400	2	28732	122422
21399	0	5250	25899
130115	1	28608	139296
24874	2	8092	52678
34988	4	4473	23853
45549	0	1572	17306
6023	0	2065	7953
64466	2	14817	89455
54990	1	16714	147866
1644	0	556	4245
6179	0	2089	21509
3926	0	2658	7670
32755	1	10695	66675
34777	0	1669	14336
73224	2	16267	53608
27114	0	7768	30059
20760	3	7252	29668
37636	6	6387	22097
65461	0	18715	96841
30080	2	7936	41907
24094	0	8643	27080




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.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=160383&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.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=160383&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160383&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.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
Correlation0.8296
R-squared0.6882
RMSE22844.4358

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8296 \tabularnewline
R-squared & 0.6882 \tabularnewline
RMSE & 22844.4358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160383&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8296[/C][/ROW]
[ROW][C]R-squared[/C][C]0.6882[/C][/ROW]
[ROW][C]RMSE[/C][C]22844.4358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160383&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160383&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
Correlation0.8296
R-squared0.6882
RMSE22844.4358







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1112285132351.15-20066.15
28478684518.5128205128267.487179487172
38312365127.617995.4
4101193104357.25-3164.25
53836165127.6-26766.6
66850440224.615384615428279.3846153846
711918299908.689655172419273.3103448276
82280715695.63636363647111.36363636364
91714015695.63636363641444.36363636364
10116174104357.2511816.75
115763584518.5128205128-26883.5128205128
126619899908.6896551724-33710.6896551724
137170184518.5128205128-12817.5128205128
145779352569.28571428575223.71428571428
158044484518.5128205128-4074.51282051283
165385565127.6-11272.6
179766899908.6896551724-2240.68965517242
18133824132351.151472.85000000001
1910148184518.512820512816962.4871794872
209964584518.512820512815126.4871794872
2111478984518.512820512830270.4871794872
229905299908.6896551724-856.68965517242
236765499908.6896551724-32254.6896551724
246555365127.6425.400000000001
259750084518.512820512812981.4871794872
266911284518.5128205128-15406.5128205128
278275384518.5128205128-1765.51282051283
2885323132351.15-47028.15
297265499908.6896551724-27254.6896551724
303072734467.8571428571-3740.85714285714
317787399908.6896551724-22035.6896551724
32117478132351.15-14873.15
337400765127.68879.4
349018399908.6896551724-9725.68965517242
356154240224.615384615421317.3846153846
3610149499908.68965517241585.31034482758
372757034467.8571428571-6897.85714285714
385581365127.6-9314.6
397921599908.6896551724-20693.6896551724
40142315695.6363636364-14272.6363636364
415546184518.5128205128-29057.5128205128
423108164590.7692307692-33509.7692307692
432299634467.8571428571-11471.8571428571
448312299908.6896551724-16786.6896551724
457010684518.5128205128-14412.5128205128
466057865127.6-4549.6
473999264590.7692307692-24598.7692307692
487989284518.5128205128-4626.51282051283
494981040224.61538461549585.38461538462
507157065127.66442.4
5110070865127.635580.4
523303234467.8571428571-1435.85714285714
538287584518.5128205128-1643.51282051283
54139077132351.156725.85000000001
557159584518.5128205128-12923.5128205128
567226099908.6896551724-27648.6896551724
57595015695.6363636364-9745.63636363636
58115762132351.15-16589.15
593255152569.2857142857-20018.2857142857
603170140224.6153846154-8523.61538461538
618067084518.5128205128-3848.51282051283
62143558132351.1511206.85
6311710599908.689655172417196.3103448276
642378934467.8571428571-10678.8571428571
65120733132351.15-11618.15
6610519599908.68965517245286.31034482758
677310764590.76923076928516.23076923077
68132068132351.15-283.149999999994
69149193132351.1516841.85
704682165127.6-18306.6
718701184518.51282051282492.48717948717
729526099908.6896551724-4648.68965517242
735518384518.5128205128-29335.5128205128
7410667184518.512820512822152.4871794872
757351184518.5128205128-11007.5128205128
769294584518.51282051288426.48717948717
777866484518.5128205128-5854.51282051283
787005465127.64926.4
792261852569.2857142857-29951.2857142857
807401184518.5128205128-10507.5128205128
818373784518.5128205128-781.512820512828
826909484518.5128205128-15424.5128205128
839313384518.51282051288614.48717948717
8495536132351.15-36815.15
85225920132351.1593568.85
866213364590.7692307692-2457.76923076923
876137064590.7692307692-3220.76923076923
884383664590.7692307692-20754.7692307692
8910611799908.68965517246208.31034482758
903869265127.6-26435.6
918465184518.5128205128132.487179487172
925662234467.857142857122154.1428571429
931598615695.6363636364290.363636363636
9495364104357.25-8993.25
952670634467.8571428571-7761.85714285714
968969184518.51282051285172.48717948717
976726799908.6896551724-32641.6896551724
98126846104357.2522488.75
994114065127.6-23987.6
10010286084518.512820512818341.4871794872
1015171565127.6-13412.6
1025580165127.6-9326.6
103111813104357.257455.75
10412029384518.512820512835774.4871794872
105138599132351.156247.85000000001
10616164799908.689655172461738.3103448276
107115929132351.15-16422.15
1082426634467.8571428571-10201.8571428571
109162901132351.1530549.85
110109825104357.255467.75
11112983899908.689655172429929.3103448276
1123751034467.85714285713042.14285714286
1134375040224.61538461543525.38461538462
1144065265127.6-24475.6
11587771104357.25-16586.25
11685872104357.25-18485.25
1178927584518.51282051284756.48717948717
1184441865127.6-20709.6
11919256599908.689655172492656.3103448276
1203523234467.8571428571764.142857142855
1214090964590.7692307692-23681.7692307692
1221329415695.6363636364-2401.63636363636
1233238740224.6153846154-7837.61538461538
12414086765127.675739.4
125120662132351.15-11689.15
1262123340224.6153846154-18991.6153846154
1274433240224.61538461544107.38461538462
1286105664590.7692307692-3534.76923076923
12910133884518.512820512816819.4871794872
130116815695.6363636364-14527.6363636364
1311349752569.2857142857-39072.2857142857
1326556784518.5128205128-18951.5128205128
1332516240224.6153846154-15062.6153846154
1343233434467.8571428571-2133.85714285714
1354073534467.85714285716267.14285714286
1369141399908.6896551724-8495.68965517242
13785515695.6363636364-14840.6363636364
1389706865127.631940.4
1394433915695.636363636428643.3636363636
1401411615695.6363636364-1579.63636363636
1411028815695.6363636364-5407.63636363636
1426562265127.6494.400000000001
1431656315695.6363636364867.363636363636
1447664399908.6896551724-23265.6896551724
14511068184518.512820512826162.4871794872
1462901115695.636363636413315.3636363636
1479269652569.285714285740126.7142857143
1489478564590.769230769230194.2307692308
149877315695.6363636364-6922.63636363636
1508320964590.769230769218618.2307692308
1519381599908.6896551724-6093.68965517242
1528668784518.51282051282168.48717948717
1533455334467.857142857185.1428571428551
154105547132351.15-26804.15
15510348799908.68965517243578.31034482758
156213688132351.1581336.85
1577122084518.5128205128-13298.5128205128
1582351734467.8571428571-10950.8571428571
1595692634467.857142857122458.1428571429
1609172184518.51282051287202.48717948717
161115168132351.15-17183.15
16211119452569.285714285758624.7142857143
1635100964590.7692307692-13581.7692307692
16413577799908.689655172435868.3103448276
1655151340224.615384615411288.3846153846
1667416399908.6896551724-25745.6896551724
1675163365127.6-13494.6
1687534534467.857142857140877.1428571429
1693341665127.6-31711.6
1708330565127.618177.4
1719895265127.633824.4
17210237284518.512820512817853.4871794872
1733723840224.6153846154-2986.61538461538
174103772132351.15-28579.15
17512396964590.769230769259378.2307692308
1762714234467.8571428571-7325.85714285714
17713540099908.689655172435491.3103448276
1782139915695.63636363645703.36363636364
17913011599908.689655172430206.3103448276
1802487434467.8571428571-9593.85714285714
1813498840224.6153846154-5236.61538461538
1824554915695.636363636429853.3636363636
183602315695.6363636364-9672.63636363636
1846446665127.6-661.599999999999
1855499099908.6896551724-44918.6896551724
186164415695.6363636364-14051.6363636364
187617915695.6363636364-9516.63636363636
188392615695.6363636364-11769.6363636364
1893275534467.8571428571-1712.85714285714
1903477715695.636363636419081.3636363636
1917322464590.76923076928633.23076923077
1922711434467.8571428571-7353.85714285714
1932076040224.6153846154-19464.6153846154
1943763652569.2857142857-14933.2857142857
1956546184518.5128205128-19057.5128205128
1963008034467.8571428571-4387.85714285714
1972409415695.63636363648398.36363636364

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 112285 & 132351.15 & -20066.15 \tabularnewline
2 & 84786 & 84518.5128205128 & 267.487179487172 \tabularnewline
3 & 83123 & 65127.6 & 17995.4 \tabularnewline
4 & 101193 & 104357.25 & -3164.25 \tabularnewline
5 & 38361 & 65127.6 & -26766.6 \tabularnewline
6 & 68504 & 40224.6153846154 & 28279.3846153846 \tabularnewline
7 & 119182 & 99908.6896551724 & 19273.3103448276 \tabularnewline
8 & 22807 & 15695.6363636364 & 7111.36363636364 \tabularnewline
9 & 17140 & 15695.6363636364 & 1444.36363636364 \tabularnewline
10 & 116174 & 104357.25 & 11816.75 \tabularnewline
11 & 57635 & 84518.5128205128 & -26883.5128205128 \tabularnewline
12 & 66198 & 99908.6896551724 & -33710.6896551724 \tabularnewline
13 & 71701 & 84518.5128205128 & -12817.5128205128 \tabularnewline
14 & 57793 & 52569.2857142857 & 5223.71428571428 \tabularnewline
15 & 80444 & 84518.5128205128 & -4074.51282051283 \tabularnewline
16 & 53855 & 65127.6 & -11272.6 \tabularnewline
17 & 97668 & 99908.6896551724 & -2240.68965517242 \tabularnewline
18 & 133824 & 132351.15 & 1472.85000000001 \tabularnewline
19 & 101481 & 84518.5128205128 & 16962.4871794872 \tabularnewline
20 & 99645 & 84518.5128205128 & 15126.4871794872 \tabularnewline
21 & 114789 & 84518.5128205128 & 30270.4871794872 \tabularnewline
22 & 99052 & 99908.6896551724 & -856.68965517242 \tabularnewline
23 & 67654 & 99908.6896551724 & -32254.6896551724 \tabularnewline
24 & 65553 & 65127.6 & 425.400000000001 \tabularnewline
25 & 97500 & 84518.5128205128 & 12981.4871794872 \tabularnewline
26 & 69112 & 84518.5128205128 & -15406.5128205128 \tabularnewline
27 & 82753 & 84518.5128205128 & -1765.51282051283 \tabularnewline
28 & 85323 & 132351.15 & -47028.15 \tabularnewline
29 & 72654 & 99908.6896551724 & -27254.6896551724 \tabularnewline
30 & 30727 & 34467.8571428571 & -3740.85714285714 \tabularnewline
31 & 77873 & 99908.6896551724 & -22035.6896551724 \tabularnewline
32 & 117478 & 132351.15 & -14873.15 \tabularnewline
33 & 74007 & 65127.6 & 8879.4 \tabularnewline
34 & 90183 & 99908.6896551724 & -9725.68965517242 \tabularnewline
35 & 61542 & 40224.6153846154 & 21317.3846153846 \tabularnewline
36 & 101494 & 99908.6896551724 & 1585.31034482758 \tabularnewline
37 & 27570 & 34467.8571428571 & -6897.85714285714 \tabularnewline
38 & 55813 & 65127.6 & -9314.6 \tabularnewline
39 & 79215 & 99908.6896551724 & -20693.6896551724 \tabularnewline
40 & 1423 & 15695.6363636364 & -14272.6363636364 \tabularnewline
41 & 55461 & 84518.5128205128 & -29057.5128205128 \tabularnewline
42 & 31081 & 64590.7692307692 & -33509.7692307692 \tabularnewline
43 & 22996 & 34467.8571428571 & -11471.8571428571 \tabularnewline
44 & 83122 & 99908.6896551724 & -16786.6896551724 \tabularnewline
45 & 70106 & 84518.5128205128 & -14412.5128205128 \tabularnewline
46 & 60578 & 65127.6 & -4549.6 \tabularnewline
47 & 39992 & 64590.7692307692 & -24598.7692307692 \tabularnewline
48 & 79892 & 84518.5128205128 & -4626.51282051283 \tabularnewline
49 & 49810 & 40224.6153846154 & 9585.38461538462 \tabularnewline
50 & 71570 & 65127.6 & 6442.4 \tabularnewline
51 & 100708 & 65127.6 & 35580.4 \tabularnewline
52 & 33032 & 34467.8571428571 & -1435.85714285714 \tabularnewline
53 & 82875 & 84518.5128205128 & -1643.51282051283 \tabularnewline
54 & 139077 & 132351.15 & 6725.85000000001 \tabularnewline
55 & 71595 & 84518.5128205128 & -12923.5128205128 \tabularnewline
56 & 72260 & 99908.6896551724 & -27648.6896551724 \tabularnewline
57 & 5950 & 15695.6363636364 & -9745.63636363636 \tabularnewline
58 & 115762 & 132351.15 & -16589.15 \tabularnewline
59 & 32551 & 52569.2857142857 & -20018.2857142857 \tabularnewline
60 & 31701 & 40224.6153846154 & -8523.61538461538 \tabularnewline
61 & 80670 & 84518.5128205128 & -3848.51282051283 \tabularnewline
62 & 143558 & 132351.15 & 11206.85 \tabularnewline
63 & 117105 & 99908.6896551724 & 17196.3103448276 \tabularnewline
64 & 23789 & 34467.8571428571 & -10678.8571428571 \tabularnewline
65 & 120733 & 132351.15 & -11618.15 \tabularnewline
66 & 105195 & 99908.6896551724 & 5286.31034482758 \tabularnewline
67 & 73107 & 64590.7692307692 & 8516.23076923077 \tabularnewline
68 & 132068 & 132351.15 & -283.149999999994 \tabularnewline
69 & 149193 & 132351.15 & 16841.85 \tabularnewline
70 & 46821 & 65127.6 & -18306.6 \tabularnewline
71 & 87011 & 84518.5128205128 & 2492.48717948717 \tabularnewline
72 & 95260 & 99908.6896551724 & -4648.68965517242 \tabularnewline
73 & 55183 & 84518.5128205128 & -29335.5128205128 \tabularnewline
74 & 106671 & 84518.5128205128 & 22152.4871794872 \tabularnewline
75 & 73511 & 84518.5128205128 & -11007.5128205128 \tabularnewline
76 & 92945 & 84518.5128205128 & 8426.48717948717 \tabularnewline
77 & 78664 & 84518.5128205128 & -5854.51282051283 \tabularnewline
78 & 70054 & 65127.6 & 4926.4 \tabularnewline
79 & 22618 & 52569.2857142857 & -29951.2857142857 \tabularnewline
80 & 74011 & 84518.5128205128 & -10507.5128205128 \tabularnewline
81 & 83737 & 84518.5128205128 & -781.512820512828 \tabularnewline
82 & 69094 & 84518.5128205128 & -15424.5128205128 \tabularnewline
83 & 93133 & 84518.5128205128 & 8614.48717948717 \tabularnewline
84 & 95536 & 132351.15 & -36815.15 \tabularnewline
85 & 225920 & 132351.15 & 93568.85 \tabularnewline
86 & 62133 & 64590.7692307692 & -2457.76923076923 \tabularnewline
87 & 61370 & 64590.7692307692 & -3220.76923076923 \tabularnewline
88 & 43836 & 64590.7692307692 & -20754.7692307692 \tabularnewline
89 & 106117 & 99908.6896551724 & 6208.31034482758 \tabularnewline
90 & 38692 & 65127.6 & -26435.6 \tabularnewline
91 & 84651 & 84518.5128205128 & 132.487179487172 \tabularnewline
92 & 56622 & 34467.8571428571 & 22154.1428571429 \tabularnewline
93 & 15986 & 15695.6363636364 & 290.363636363636 \tabularnewline
94 & 95364 & 104357.25 & -8993.25 \tabularnewline
95 & 26706 & 34467.8571428571 & -7761.85714285714 \tabularnewline
96 & 89691 & 84518.5128205128 & 5172.48717948717 \tabularnewline
97 & 67267 & 99908.6896551724 & -32641.6896551724 \tabularnewline
98 & 126846 & 104357.25 & 22488.75 \tabularnewline
99 & 41140 & 65127.6 & -23987.6 \tabularnewline
100 & 102860 & 84518.5128205128 & 18341.4871794872 \tabularnewline
101 & 51715 & 65127.6 & -13412.6 \tabularnewline
102 & 55801 & 65127.6 & -9326.6 \tabularnewline
103 & 111813 & 104357.25 & 7455.75 \tabularnewline
104 & 120293 & 84518.5128205128 & 35774.4871794872 \tabularnewline
105 & 138599 & 132351.15 & 6247.85000000001 \tabularnewline
106 & 161647 & 99908.6896551724 & 61738.3103448276 \tabularnewline
107 & 115929 & 132351.15 & -16422.15 \tabularnewline
108 & 24266 & 34467.8571428571 & -10201.8571428571 \tabularnewline
109 & 162901 & 132351.15 & 30549.85 \tabularnewline
110 & 109825 & 104357.25 & 5467.75 \tabularnewline
111 & 129838 & 99908.6896551724 & 29929.3103448276 \tabularnewline
112 & 37510 & 34467.8571428571 & 3042.14285714286 \tabularnewline
113 & 43750 & 40224.6153846154 & 3525.38461538462 \tabularnewline
114 & 40652 & 65127.6 & -24475.6 \tabularnewline
115 & 87771 & 104357.25 & -16586.25 \tabularnewline
116 & 85872 & 104357.25 & -18485.25 \tabularnewline
117 & 89275 & 84518.5128205128 & 4756.48717948717 \tabularnewline
118 & 44418 & 65127.6 & -20709.6 \tabularnewline
119 & 192565 & 99908.6896551724 & 92656.3103448276 \tabularnewline
120 & 35232 & 34467.8571428571 & 764.142857142855 \tabularnewline
121 & 40909 & 64590.7692307692 & -23681.7692307692 \tabularnewline
122 & 13294 & 15695.6363636364 & -2401.63636363636 \tabularnewline
123 & 32387 & 40224.6153846154 & -7837.61538461538 \tabularnewline
124 & 140867 & 65127.6 & 75739.4 \tabularnewline
125 & 120662 & 132351.15 & -11689.15 \tabularnewline
126 & 21233 & 40224.6153846154 & -18991.6153846154 \tabularnewline
127 & 44332 & 40224.6153846154 & 4107.38461538462 \tabularnewline
128 & 61056 & 64590.7692307692 & -3534.76923076923 \tabularnewline
129 & 101338 & 84518.5128205128 & 16819.4871794872 \tabularnewline
130 & 1168 & 15695.6363636364 & -14527.6363636364 \tabularnewline
131 & 13497 & 52569.2857142857 & -39072.2857142857 \tabularnewline
132 & 65567 & 84518.5128205128 & -18951.5128205128 \tabularnewline
133 & 25162 & 40224.6153846154 & -15062.6153846154 \tabularnewline
134 & 32334 & 34467.8571428571 & -2133.85714285714 \tabularnewline
135 & 40735 & 34467.8571428571 & 6267.14285714286 \tabularnewline
136 & 91413 & 99908.6896551724 & -8495.68965517242 \tabularnewline
137 & 855 & 15695.6363636364 & -14840.6363636364 \tabularnewline
138 & 97068 & 65127.6 & 31940.4 \tabularnewline
139 & 44339 & 15695.6363636364 & 28643.3636363636 \tabularnewline
140 & 14116 & 15695.6363636364 & -1579.63636363636 \tabularnewline
141 & 10288 & 15695.6363636364 & -5407.63636363636 \tabularnewline
142 & 65622 & 65127.6 & 494.400000000001 \tabularnewline
143 & 16563 & 15695.6363636364 & 867.363636363636 \tabularnewline
144 & 76643 & 99908.6896551724 & -23265.6896551724 \tabularnewline
145 & 110681 & 84518.5128205128 & 26162.4871794872 \tabularnewline
146 & 29011 & 15695.6363636364 & 13315.3636363636 \tabularnewline
147 & 92696 & 52569.2857142857 & 40126.7142857143 \tabularnewline
148 & 94785 & 64590.7692307692 & 30194.2307692308 \tabularnewline
149 & 8773 & 15695.6363636364 & -6922.63636363636 \tabularnewline
150 & 83209 & 64590.7692307692 & 18618.2307692308 \tabularnewline
151 & 93815 & 99908.6896551724 & -6093.68965517242 \tabularnewline
152 & 86687 & 84518.5128205128 & 2168.48717948717 \tabularnewline
153 & 34553 & 34467.8571428571 & 85.1428571428551 \tabularnewline
154 & 105547 & 132351.15 & -26804.15 \tabularnewline
155 & 103487 & 99908.6896551724 & 3578.31034482758 \tabularnewline
156 & 213688 & 132351.15 & 81336.85 \tabularnewline
157 & 71220 & 84518.5128205128 & -13298.5128205128 \tabularnewline
158 & 23517 & 34467.8571428571 & -10950.8571428571 \tabularnewline
159 & 56926 & 34467.8571428571 & 22458.1428571429 \tabularnewline
160 & 91721 & 84518.5128205128 & 7202.48717948717 \tabularnewline
161 & 115168 & 132351.15 & -17183.15 \tabularnewline
162 & 111194 & 52569.2857142857 & 58624.7142857143 \tabularnewline
163 & 51009 & 64590.7692307692 & -13581.7692307692 \tabularnewline
164 & 135777 & 99908.6896551724 & 35868.3103448276 \tabularnewline
165 & 51513 & 40224.6153846154 & 11288.3846153846 \tabularnewline
166 & 74163 & 99908.6896551724 & -25745.6896551724 \tabularnewline
167 & 51633 & 65127.6 & -13494.6 \tabularnewline
168 & 75345 & 34467.8571428571 & 40877.1428571429 \tabularnewline
169 & 33416 & 65127.6 & -31711.6 \tabularnewline
170 & 83305 & 65127.6 & 18177.4 \tabularnewline
171 & 98952 & 65127.6 & 33824.4 \tabularnewline
172 & 102372 & 84518.5128205128 & 17853.4871794872 \tabularnewline
173 & 37238 & 40224.6153846154 & -2986.61538461538 \tabularnewline
174 & 103772 & 132351.15 & -28579.15 \tabularnewline
175 & 123969 & 64590.7692307692 & 59378.2307692308 \tabularnewline
176 & 27142 & 34467.8571428571 & -7325.85714285714 \tabularnewline
177 & 135400 & 99908.6896551724 & 35491.3103448276 \tabularnewline
178 & 21399 & 15695.6363636364 & 5703.36363636364 \tabularnewline
179 & 130115 & 99908.6896551724 & 30206.3103448276 \tabularnewline
180 & 24874 & 34467.8571428571 & -9593.85714285714 \tabularnewline
181 & 34988 & 40224.6153846154 & -5236.61538461538 \tabularnewline
182 & 45549 & 15695.6363636364 & 29853.3636363636 \tabularnewline
183 & 6023 & 15695.6363636364 & -9672.63636363636 \tabularnewline
184 & 64466 & 65127.6 & -661.599999999999 \tabularnewline
185 & 54990 & 99908.6896551724 & -44918.6896551724 \tabularnewline
186 & 1644 & 15695.6363636364 & -14051.6363636364 \tabularnewline
187 & 6179 & 15695.6363636364 & -9516.63636363636 \tabularnewline
188 & 3926 & 15695.6363636364 & -11769.6363636364 \tabularnewline
189 & 32755 & 34467.8571428571 & -1712.85714285714 \tabularnewline
190 & 34777 & 15695.6363636364 & 19081.3636363636 \tabularnewline
191 & 73224 & 64590.7692307692 & 8633.23076923077 \tabularnewline
192 & 27114 & 34467.8571428571 & -7353.85714285714 \tabularnewline
193 & 20760 & 40224.6153846154 & -19464.6153846154 \tabularnewline
194 & 37636 & 52569.2857142857 & -14933.2857142857 \tabularnewline
195 & 65461 & 84518.5128205128 & -19057.5128205128 \tabularnewline
196 & 30080 & 34467.8571428571 & -4387.85714285714 \tabularnewline
197 & 24094 & 15695.6363636364 & 8398.36363636364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160383&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]112285[/C][C]132351.15[/C][C]-20066.15[/C][/ROW]
[ROW][C]2[/C][C]84786[/C][C]84518.5128205128[/C][C]267.487179487172[/C][/ROW]
[ROW][C]3[/C][C]83123[/C][C]65127.6[/C][C]17995.4[/C][/ROW]
[ROW][C]4[/C][C]101193[/C][C]104357.25[/C][C]-3164.25[/C][/ROW]
[ROW][C]5[/C][C]38361[/C][C]65127.6[/C][C]-26766.6[/C][/ROW]
[ROW][C]6[/C][C]68504[/C][C]40224.6153846154[/C][C]28279.3846153846[/C][/ROW]
[ROW][C]7[/C][C]119182[/C][C]99908.6896551724[/C][C]19273.3103448276[/C][/ROW]
[ROW][C]8[/C][C]22807[/C][C]15695.6363636364[/C][C]7111.36363636364[/C][/ROW]
[ROW][C]9[/C][C]17140[/C][C]15695.6363636364[/C][C]1444.36363636364[/C][/ROW]
[ROW][C]10[/C][C]116174[/C][C]104357.25[/C][C]11816.75[/C][/ROW]
[ROW][C]11[/C][C]57635[/C][C]84518.5128205128[/C][C]-26883.5128205128[/C][/ROW]
[ROW][C]12[/C][C]66198[/C][C]99908.6896551724[/C][C]-33710.6896551724[/C][/ROW]
[ROW][C]13[/C][C]71701[/C][C]84518.5128205128[/C][C]-12817.5128205128[/C][/ROW]
[ROW][C]14[/C][C]57793[/C][C]52569.2857142857[/C][C]5223.71428571428[/C][/ROW]
[ROW][C]15[/C][C]80444[/C][C]84518.5128205128[/C][C]-4074.51282051283[/C][/ROW]
[ROW][C]16[/C][C]53855[/C][C]65127.6[/C][C]-11272.6[/C][/ROW]
[ROW][C]17[/C][C]97668[/C][C]99908.6896551724[/C][C]-2240.68965517242[/C][/ROW]
[ROW][C]18[/C][C]133824[/C][C]132351.15[/C][C]1472.85000000001[/C][/ROW]
[ROW][C]19[/C][C]101481[/C][C]84518.5128205128[/C][C]16962.4871794872[/C][/ROW]
[ROW][C]20[/C][C]99645[/C][C]84518.5128205128[/C][C]15126.4871794872[/C][/ROW]
[ROW][C]21[/C][C]114789[/C][C]84518.5128205128[/C][C]30270.4871794872[/C][/ROW]
[ROW][C]22[/C][C]99052[/C][C]99908.6896551724[/C][C]-856.68965517242[/C][/ROW]
[ROW][C]23[/C][C]67654[/C][C]99908.6896551724[/C][C]-32254.6896551724[/C][/ROW]
[ROW][C]24[/C][C]65553[/C][C]65127.6[/C][C]425.400000000001[/C][/ROW]
[ROW][C]25[/C][C]97500[/C][C]84518.5128205128[/C][C]12981.4871794872[/C][/ROW]
[ROW][C]26[/C][C]69112[/C][C]84518.5128205128[/C][C]-15406.5128205128[/C][/ROW]
[ROW][C]27[/C][C]82753[/C][C]84518.5128205128[/C][C]-1765.51282051283[/C][/ROW]
[ROW][C]28[/C][C]85323[/C][C]132351.15[/C][C]-47028.15[/C][/ROW]
[ROW][C]29[/C][C]72654[/C][C]99908.6896551724[/C][C]-27254.6896551724[/C][/ROW]
[ROW][C]30[/C][C]30727[/C][C]34467.8571428571[/C][C]-3740.85714285714[/C][/ROW]
[ROW][C]31[/C][C]77873[/C][C]99908.6896551724[/C][C]-22035.6896551724[/C][/ROW]
[ROW][C]32[/C][C]117478[/C][C]132351.15[/C][C]-14873.15[/C][/ROW]
[ROW][C]33[/C][C]74007[/C][C]65127.6[/C][C]8879.4[/C][/ROW]
[ROW][C]34[/C][C]90183[/C][C]99908.6896551724[/C][C]-9725.68965517242[/C][/ROW]
[ROW][C]35[/C][C]61542[/C][C]40224.6153846154[/C][C]21317.3846153846[/C][/ROW]
[ROW][C]36[/C][C]101494[/C][C]99908.6896551724[/C][C]1585.31034482758[/C][/ROW]
[ROW][C]37[/C][C]27570[/C][C]34467.8571428571[/C][C]-6897.85714285714[/C][/ROW]
[ROW][C]38[/C][C]55813[/C][C]65127.6[/C][C]-9314.6[/C][/ROW]
[ROW][C]39[/C][C]79215[/C][C]99908.6896551724[/C][C]-20693.6896551724[/C][/ROW]
[ROW][C]40[/C][C]1423[/C][C]15695.6363636364[/C][C]-14272.6363636364[/C][/ROW]
[ROW][C]41[/C][C]55461[/C][C]84518.5128205128[/C][C]-29057.5128205128[/C][/ROW]
[ROW][C]42[/C][C]31081[/C][C]64590.7692307692[/C][C]-33509.7692307692[/C][/ROW]
[ROW][C]43[/C][C]22996[/C][C]34467.8571428571[/C][C]-11471.8571428571[/C][/ROW]
[ROW][C]44[/C][C]83122[/C][C]99908.6896551724[/C][C]-16786.6896551724[/C][/ROW]
[ROW][C]45[/C][C]70106[/C][C]84518.5128205128[/C][C]-14412.5128205128[/C][/ROW]
[ROW][C]46[/C][C]60578[/C][C]65127.6[/C][C]-4549.6[/C][/ROW]
[ROW][C]47[/C][C]39992[/C][C]64590.7692307692[/C][C]-24598.7692307692[/C][/ROW]
[ROW][C]48[/C][C]79892[/C][C]84518.5128205128[/C][C]-4626.51282051283[/C][/ROW]
[ROW][C]49[/C][C]49810[/C][C]40224.6153846154[/C][C]9585.38461538462[/C][/ROW]
[ROW][C]50[/C][C]71570[/C][C]65127.6[/C][C]6442.4[/C][/ROW]
[ROW][C]51[/C][C]100708[/C][C]65127.6[/C][C]35580.4[/C][/ROW]
[ROW][C]52[/C][C]33032[/C][C]34467.8571428571[/C][C]-1435.85714285714[/C][/ROW]
[ROW][C]53[/C][C]82875[/C][C]84518.5128205128[/C][C]-1643.51282051283[/C][/ROW]
[ROW][C]54[/C][C]139077[/C][C]132351.15[/C][C]6725.85000000001[/C][/ROW]
[ROW][C]55[/C][C]71595[/C][C]84518.5128205128[/C][C]-12923.5128205128[/C][/ROW]
[ROW][C]56[/C][C]72260[/C][C]99908.6896551724[/C][C]-27648.6896551724[/C][/ROW]
[ROW][C]57[/C][C]5950[/C][C]15695.6363636364[/C][C]-9745.63636363636[/C][/ROW]
[ROW][C]58[/C][C]115762[/C][C]132351.15[/C][C]-16589.15[/C][/ROW]
[ROW][C]59[/C][C]32551[/C][C]52569.2857142857[/C][C]-20018.2857142857[/C][/ROW]
[ROW][C]60[/C][C]31701[/C][C]40224.6153846154[/C][C]-8523.61538461538[/C][/ROW]
[ROW][C]61[/C][C]80670[/C][C]84518.5128205128[/C][C]-3848.51282051283[/C][/ROW]
[ROW][C]62[/C][C]143558[/C][C]132351.15[/C][C]11206.85[/C][/ROW]
[ROW][C]63[/C][C]117105[/C][C]99908.6896551724[/C][C]17196.3103448276[/C][/ROW]
[ROW][C]64[/C][C]23789[/C][C]34467.8571428571[/C][C]-10678.8571428571[/C][/ROW]
[ROW][C]65[/C][C]120733[/C][C]132351.15[/C][C]-11618.15[/C][/ROW]
[ROW][C]66[/C][C]105195[/C][C]99908.6896551724[/C][C]5286.31034482758[/C][/ROW]
[ROW][C]67[/C][C]73107[/C][C]64590.7692307692[/C][C]8516.23076923077[/C][/ROW]
[ROW][C]68[/C][C]132068[/C][C]132351.15[/C][C]-283.149999999994[/C][/ROW]
[ROW][C]69[/C][C]149193[/C][C]132351.15[/C][C]16841.85[/C][/ROW]
[ROW][C]70[/C][C]46821[/C][C]65127.6[/C][C]-18306.6[/C][/ROW]
[ROW][C]71[/C][C]87011[/C][C]84518.5128205128[/C][C]2492.48717948717[/C][/ROW]
[ROW][C]72[/C][C]95260[/C][C]99908.6896551724[/C][C]-4648.68965517242[/C][/ROW]
[ROW][C]73[/C][C]55183[/C][C]84518.5128205128[/C][C]-29335.5128205128[/C][/ROW]
[ROW][C]74[/C][C]106671[/C][C]84518.5128205128[/C][C]22152.4871794872[/C][/ROW]
[ROW][C]75[/C][C]73511[/C][C]84518.5128205128[/C][C]-11007.5128205128[/C][/ROW]
[ROW][C]76[/C][C]92945[/C][C]84518.5128205128[/C][C]8426.48717948717[/C][/ROW]
[ROW][C]77[/C][C]78664[/C][C]84518.5128205128[/C][C]-5854.51282051283[/C][/ROW]
[ROW][C]78[/C][C]70054[/C][C]65127.6[/C][C]4926.4[/C][/ROW]
[ROW][C]79[/C][C]22618[/C][C]52569.2857142857[/C][C]-29951.2857142857[/C][/ROW]
[ROW][C]80[/C][C]74011[/C][C]84518.5128205128[/C][C]-10507.5128205128[/C][/ROW]
[ROW][C]81[/C][C]83737[/C][C]84518.5128205128[/C][C]-781.512820512828[/C][/ROW]
[ROW][C]82[/C][C]69094[/C][C]84518.5128205128[/C][C]-15424.5128205128[/C][/ROW]
[ROW][C]83[/C][C]93133[/C][C]84518.5128205128[/C][C]8614.48717948717[/C][/ROW]
[ROW][C]84[/C][C]95536[/C][C]132351.15[/C][C]-36815.15[/C][/ROW]
[ROW][C]85[/C][C]225920[/C][C]132351.15[/C][C]93568.85[/C][/ROW]
[ROW][C]86[/C][C]62133[/C][C]64590.7692307692[/C][C]-2457.76923076923[/C][/ROW]
[ROW][C]87[/C][C]61370[/C][C]64590.7692307692[/C][C]-3220.76923076923[/C][/ROW]
[ROW][C]88[/C][C]43836[/C][C]64590.7692307692[/C][C]-20754.7692307692[/C][/ROW]
[ROW][C]89[/C][C]106117[/C][C]99908.6896551724[/C][C]6208.31034482758[/C][/ROW]
[ROW][C]90[/C][C]38692[/C][C]65127.6[/C][C]-26435.6[/C][/ROW]
[ROW][C]91[/C][C]84651[/C][C]84518.5128205128[/C][C]132.487179487172[/C][/ROW]
[ROW][C]92[/C][C]56622[/C][C]34467.8571428571[/C][C]22154.1428571429[/C][/ROW]
[ROW][C]93[/C][C]15986[/C][C]15695.6363636364[/C][C]290.363636363636[/C][/ROW]
[ROW][C]94[/C][C]95364[/C][C]104357.25[/C][C]-8993.25[/C][/ROW]
[ROW][C]95[/C][C]26706[/C][C]34467.8571428571[/C][C]-7761.85714285714[/C][/ROW]
[ROW][C]96[/C][C]89691[/C][C]84518.5128205128[/C][C]5172.48717948717[/C][/ROW]
[ROW][C]97[/C][C]67267[/C][C]99908.6896551724[/C][C]-32641.6896551724[/C][/ROW]
[ROW][C]98[/C][C]126846[/C][C]104357.25[/C][C]22488.75[/C][/ROW]
[ROW][C]99[/C][C]41140[/C][C]65127.6[/C][C]-23987.6[/C][/ROW]
[ROW][C]100[/C][C]102860[/C][C]84518.5128205128[/C][C]18341.4871794872[/C][/ROW]
[ROW][C]101[/C][C]51715[/C][C]65127.6[/C][C]-13412.6[/C][/ROW]
[ROW][C]102[/C][C]55801[/C][C]65127.6[/C][C]-9326.6[/C][/ROW]
[ROW][C]103[/C][C]111813[/C][C]104357.25[/C][C]7455.75[/C][/ROW]
[ROW][C]104[/C][C]120293[/C][C]84518.5128205128[/C][C]35774.4871794872[/C][/ROW]
[ROW][C]105[/C][C]138599[/C][C]132351.15[/C][C]6247.85000000001[/C][/ROW]
[ROW][C]106[/C][C]161647[/C][C]99908.6896551724[/C][C]61738.3103448276[/C][/ROW]
[ROW][C]107[/C][C]115929[/C][C]132351.15[/C][C]-16422.15[/C][/ROW]
[ROW][C]108[/C][C]24266[/C][C]34467.8571428571[/C][C]-10201.8571428571[/C][/ROW]
[ROW][C]109[/C][C]162901[/C][C]132351.15[/C][C]30549.85[/C][/ROW]
[ROW][C]110[/C][C]109825[/C][C]104357.25[/C][C]5467.75[/C][/ROW]
[ROW][C]111[/C][C]129838[/C][C]99908.6896551724[/C][C]29929.3103448276[/C][/ROW]
[ROW][C]112[/C][C]37510[/C][C]34467.8571428571[/C][C]3042.14285714286[/C][/ROW]
[ROW][C]113[/C][C]43750[/C][C]40224.6153846154[/C][C]3525.38461538462[/C][/ROW]
[ROW][C]114[/C][C]40652[/C][C]65127.6[/C][C]-24475.6[/C][/ROW]
[ROW][C]115[/C][C]87771[/C][C]104357.25[/C][C]-16586.25[/C][/ROW]
[ROW][C]116[/C][C]85872[/C][C]104357.25[/C][C]-18485.25[/C][/ROW]
[ROW][C]117[/C][C]89275[/C][C]84518.5128205128[/C][C]4756.48717948717[/C][/ROW]
[ROW][C]118[/C][C]44418[/C][C]65127.6[/C][C]-20709.6[/C][/ROW]
[ROW][C]119[/C][C]192565[/C][C]99908.6896551724[/C][C]92656.3103448276[/C][/ROW]
[ROW][C]120[/C][C]35232[/C][C]34467.8571428571[/C][C]764.142857142855[/C][/ROW]
[ROW][C]121[/C][C]40909[/C][C]64590.7692307692[/C][C]-23681.7692307692[/C][/ROW]
[ROW][C]122[/C][C]13294[/C][C]15695.6363636364[/C][C]-2401.63636363636[/C][/ROW]
[ROW][C]123[/C][C]32387[/C][C]40224.6153846154[/C][C]-7837.61538461538[/C][/ROW]
[ROW][C]124[/C][C]140867[/C][C]65127.6[/C][C]75739.4[/C][/ROW]
[ROW][C]125[/C][C]120662[/C][C]132351.15[/C][C]-11689.15[/C][/ROW]
[ROW][C]126[/C][C]21233[/C][C]40224.6153846154[/C][C]-18991.6153846154[/C][/ROW]
[ROW][C]127[/C][C]44332[/C][C]40224.6153846154[/C][C]4107.38461538462[/C][/ROW]
[ROW][C]128[/C][C]61056[/C][C]64590.7692307692[/C][C]-3534.76923076923[/C][/ROW]
[ROW][C]129[/C][C]101338[/C][C]84518.5128205128[/C][C]16819.4871794872[/C][/ROW]
[ROW][C]130[/C][C]1168[/C][C]15695.6363636364[/C][C]-14527.6363636364[/C][/ROW]
[ROW][C]131[/C][C]13497[/C][C]52569.2857142857[/C][C]-39072.2857142857[/C][/ROW]
[ROW][C]132[/C][C]65567[/C][C]84518.5128205128[/C][C]-18951.5128205128[/C][/ROW]
[ROW][C]133[/C][C]25162[/C][C]40224.6153846154[/C][C]-15062.6153846154[/C][/ROW]
[ROW][C]134[/C][C]32334[/C][C]34467.8571428571[/C][C]-2133.85714285714[/C][/ROW]
[ROW][C]135[/C][C]40735[/C][C]34467.8571428571[/C][C]6267.14285714286[/C][/ROW]
[ROW][C]136[/C][C]91413[/C][C]99908.6896551724[/C][C]-8495.68965517242[/C][/ROW]
[ROW][C]137[/C][C]855[/C][C]15695.6363636364[/C][C]-14840.6363636364[/C][/ROW]
[ROW][C]138[/C][C]97068[/C][C]65127.6[/C][C]31940.4[/C][/ROW]
[ROW][C]139[/C][C]44339[/C][C]15695.6363636364[/C][C]28643.3636363636[/C][/ROW]
[ROW][C]140[/C][C]14116[/C][C]15695.6363636364[/C][C]-1579.63636363636[/C][/ROW]
[ROW][C]141[/C][C]10288[/C][C]15695.6363636364[/C][C]-5407.63636363636[/C][/ROW]
[ROW][C]142[/C][C]65622[/C][C]65127.6[/C][C]494.400000000001[/C][/ROW]
[ROW][C]143[/C][C]16563[/C][C]15695.6363636364[/C][C]867.363636363636[/C][/ROW]
[ROW][C]144[/C][C]76643[/C][C]99908.6896551724[/C][C]-23265.6896551724[/C][/ROW]
[ROW][C]145[/C][C]110681[/C][C]84518.5128205128[/C][C]26162.4871794872[/C][/ROW]
[ROW][C]146[/C][C]29011[/C][C]15695.6363636364[/C][C]13315.3636363636[/C][/ROW]
[ROW][C]147[/C][C]92696[/C][C]52569.2857142857[/C][C]40126.7142857143[/C][/ROW]
[ROW][C]148[/C][C]94785[/C][C]64590.7692307692[/C][C]30194.2307692308[/C][/ROW]
[ROW][C]149[/C][C]8773[/C][C]15695.6363636364[/C][C]-6922.63636363636[/C][/ROW]
[ROW][C]150[/C][C]83209[/C][C]64590.7692307692[/C][C]18618.2307692308[/C][/ROW]
[ROW][C]151[/C][C]93815[/C][C]99908.6896551724[/C][C]-6093.68965517242[/C][/ROW]
[ROW][C]152[/C][C]86687[/C][C]84518.5128205128[/C][C]2168.48717948717[/C][/ROW]
[ROW][C]153[/C][C]34553[/C][C]34467.8571428571[/C][C]85.1428571428551[/C][/ROW]
[ROW][C]154[/C][C]105547[/C][C]132351.15[/C][C]-26804.15[/C][/ROW]
[ROW][C]155[/C][C]103487[/C][C]99908.6896551724[/C][C]3578.31034482758[/C][/ROW]
[ROW][C]156[/C][C]213688[/C][C]132351.15[/C][C]81336.85[/C][/ROW]
[ROW][C]157[/C][C]71220[/C][C]84518.5128205128[/C][C]-13298.5128205128[/C][/ROW]
[ROW][C]158[/C][C]23517[/C][C]34467.8571428571[/C][C]-10950.8571428571[/C][/ROW]
[ROW][C]159[/C][C]56926[/C][C]34467.8571428571[/C][C]22458.1428571429[/C][/ROW]
[ROW][C]160[/C][C]91721[/C][C]84518.5128205128[/C][C]7202.48717948717[/C][/ROW]
[ROW][C]161[/C][C]115168[/C][C]132351.15[/C][C]-17183.15[/C][/ROW]
[ROW][C]162[/C][C]111194[/C][C]52569.2857142857[/C][C]58624.7142857143[/C][/ROW]
[ROW][C]163[/C][C]51009[/C][C]64590.7692307692[/C][C]-13581.7692307692[/C][/ROW]
[ROW][C]164[/C][C]135777[/C][C]99908.6896551724[/C][C]35868.3103448276[/C][/ROW]
[ROW][C]165[/C][C]51513[/C][C]40224.6153846154[/C][C]11288.3846153846[/C][/ROW]
[ROW][C]166[/C][C]74163[/C][C]99908.6896551724[/C][C]-25745.6896551724[/C][/ROW]
[ROW][C]167[/C][C]51633[/C][C]65127.6[/C][C]-13494.6[/C][/ROW]
[ROW][C]168[/C][C]75345[/C][C]34467.8571428571[/C][C]40877.1428571429[/C][/ROW]
[ROW][C]169[/C][C]33416[/C][C]65127.6[/C][C]-31711.6[/C][/ROW]
[ROW][C]170[/C][C]83305[/C][C]65127.6[/C][C]18177.4[/C][/ROW]
[ROW][C]171[/C][C]98952[/C][C]65127.6[/C][C]33824.4[/C][/ROW]
[ROW][C]172[/C][C]102372[/C][C]84518.5128205128[/C][C]17853.4871794872[/C][/ROW]
[ROW][C]173[/C][C]37238[/C][C]40224.6153846154[/C][C]-2986.61538461538[/C][/ROW]
[ROW][C]174[/C][C]103772[/C][C]132351.15[/C][C]-28579.15[/C][/ROW]
[ROW][C]175[/C][C]123969[/C][C]64590.7692307692[/C][C]59378.2307692308[/C][/ROW]
[ROW][C]176[/C][C]27142[/C][C]34467.8571428571[/C][C]-7325.85714285714[/C][/ROW]
[ROW][C]177[/C][C]135400[/C][C]99908.6896551724[/C][C]35491.3103448276[/C][/ROW]
[ROW][C]178[/C][C]21399[/C][C]15695.6363636364[/C][C]5703.36363636364[/C][/ROW]
[ROW][C]179[/C][C]130115[/C][C]99908.6896551724[/C][C]30206.3103448276[/C][/ROW]
[ROW][C]180[/C][C]24874[/C][C]34467.8571428571[/C][C]-9593.85714285714[/C][/ROW]
[ROW][C]181[/C][C]34988[/C][C]40224.6153846154[/C][C]-5236.61538461538[/C][/ROW]
[ROW][C]182[/C][C]45549[/C][C]15695.6363636364[/C][C]29853.3636363636[/C][/ROW]
[ROW][C]183[/C][C]6023[/C][C]15695.6363636364[/C][C]-9672.63636363636[/C][/ROW]
[ROW][C]184[/C][C]64466[/C][C]65127.6[/C][C]-661.599999999999[/C][/ROW]
[ROW][C]185[/C][C]54990[/C][C]99908.6896551724[/C][C]-44918.6896551724[/C][/ROW]
[ROW][C]186[/C][C]1644[/C][C]15695.6363636364[/C][C]-14051.6363636364[/C][/ROW]
[ROW][C]187[/C][C]6179[/C][C]15695.6363636364[/C][C]-9516.63636363636[/C][/ROW]
[ROW][C]188[/C][C]3926[/C][C]15695.6363636364[/C][C]-11769.6363636364[/C][/ROW]
[ROW][C]189[/C][C]32755[/C][C]34467.8571428571[/C][C]-1712.85714285714[/C][/ROW]
[ROW][C]190[/C][C]34777[/C][C]15695.6363636364[/C][C]19081.3636363636[/C][/ROW]
[ROW][C]191[/C][C]73224[/C][C]64590.7692307692[/C][C]8633.23076923077[/C][/ROW]
[ROW][C]192[/C][C]27114[/C][C]34467.8571428571[/C][C]-7353.85714285714[/C][/ROW]
[ROW][C]193[/C][C]20760[/C][C]40224.6153846154[/C][C]-19464.6153846154[/C][/ROW]
[ROW][C]194[/C][C]37636[/C][C]52569.2857142857[/C][C]-14933.2857142857[/C][/ROW]
[ROW][C]195[/C][C]65461[/C][C]84518.5128205128[/C][C]-19057.5128205128[/C][/ROW]
[ROW][C]196[/C][C]30080[/C][C]34467.8571428571[/C][C]-4387.85714285714[/C][/ROW]
[ROW][C]197[/C][C]24094[/C][C]15695.6363636364[/C][C]8398.36363636364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160383&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160383&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
1112285132351.15-20066.15
28478684518.5128205128267.487179487172
38312365127.617995.4
4101193104357.25-3164.25
53836165127.6-26766.6
66850440224.615384615428279.3846153846
711918299908.689655172419273.3103448276
82280715695.63636363647111.36363636364
91714015695.63636363641444.36363636364
10116174104357.2511816.75
115763584518.5128205128-26883.5128205128
126619899908.6896551724-33710.6896551724
137170184518.5128205128-12817.5128205128
145779352569.28571428575223.71428571428
158044484518.5128205128-4074.51282051283
165385565127.6-11272.6
179766899908.6896551724-2240.68965517242
18133824132351.151472.85000000001
1910148184518.512820512816962.4871794872
209964584518.512820512815126.4871794872
2111478984518.512820512830270.4871794872
229905299908.6896551724-856.68965517242
236765499908.6896551724-32254.6896551724
246555365127.6425.400000000001
259750084518.512820512812981.4871794872
266911284518.5128205128-15406.5128205128
278275384518.5128205128-1765.51282051283
2885323132351.15-47028.15
297265499908.6896551724-27254.6896551724
303072734467.8571428571-3740.85714285714
317787399908.6896551724-22035.6896551724
32117478132351.15-14873.15
337400765127.68879.4
349018399908.6896551724-9725.68965517242
356154240224.615384615421317.3846153846
3610149499908.68965517241585.31034482758
372757034467.8571428571-6897.85714285714
385581365127.6-9314.6
397921599908.6896551724-20693.6896551724
40142315695.6363636364-14272.6363636364
415546184518.5128205128-29057.5128205128
423108164590.7692307692-33509.7692307692
432299634467.8571428571-11471.8571428571
448312299908.6896551724-16786.6896551724
457010684518.5128205128-14412.5128205128
466057865127.6-4549.6
473999264590.7692307692-24598.7692307692
487989284518.5128205128-4626.51282051283
494981040224.61538461549585.38461538462
507157065127.66442.4
5110070865127.635580.4
523303234467.8571428571-1435.85714285714
538287584518.5128205128-1643.51282051283
54139077132351.156725.85000000001
557159584518.5128205128-12923.5128205128
567226099908.6896551724-27648.6896551724
57595015695.6363636364-9745.63636363636
58115762132351.15-16589.15
593255152569.2857142857-20018.2857142857
603170140224.6153846154-8523.61538461538
618067084518.5128205128-3848.51282051283
62143558132351.1511206.85
6311710599908.689655172417196.3103448276
642378934467.8571428571-10678.8571428571
65120733132351.15-11618.15
6610519599908.68965517245286.31034482758
677310764590.76923076928516.23076923077
68132068132351.15-283.149999999994
69149193132351.1516841.85
704682165127.6-18306.6
718701184518.51282051282492.48717948717
729526099908.6896551724-4648.68965517242
735518384518.5128205128-29335.5128205128
7410667184518.512820512822152.4871794872
757351184518.5128205128-11007.5128205128
769294584518.51282051288426.48717948717
777866484518.5128205128-5854.51282051283
787005465127.64926.4
792261852569.2857142857-29951.2857142857
807401184518.5128205128-10507.5128205128
818373784518.5128205128-781.512820512828
826909484518.5128205128-15424.5128205128
839313384518.51282051288614.48717948717
8495536132351.15-36815.15
85225920132351.1593568.85
866213364590.7692307692-2457.76923076923
876137064590.7692307692-3220.76923076923
884383664590.7692307692-20754.7692307692
8910611799908.68965517246208.31034482758
903869265127.6-26435.6
918465184518.5128205128132.487179487172
925662234467.857142857122154.1428571429
931598615695.6363636364290.363636363636
9495364104357.25-8993.25
952670634467.8571428571-7761.85714285714
968969184518.51282051285172.48717948717
976726799908.6896551724-32641.6896551724
98126846104357.2522488.75
994114065127.6-23987.6
10010286084518.512820512818341.4871794872
1015171565127.6-13412.6
1025580165127.6-9326.6
103111813104357.257455.75
10412029384518.512820512835774.4871794872
105138599132351.156247.85000000001
10616164799908.689655172461738.3103448276
107115929132351.15-16422.15
1082426634467.8571428571-10201.8571428571
109162901132351.1530549.85
110109825104357.255467.75
11112983899908.689655172429929.3103448276
1123751034467.85714285713042.14285714286
1134375040224.61538461543525.38461538462
1144065265127.6-24475.6
11587771104357.25-16586.25
11685872104357.25-18485.25
1178927584518.51282051284756.48717948717
1184441865127.6-20709.6
11919256599908.689655172492656.3103448276
1203523234467.8571428571764.142857142855
1214090964590.7692307692-23681.7692307692
1221329415695.6363636364-2401.63636363636
1233238740224.6153846154-7837.61538461538
12414086765127.675739.4
125120662132351.15-11689.15
1262123340224.6153846154-18991.6153846154
1274433240224.61538461544107.38461538462
1286105664590.7692307692-3534.76923076923
12910133884518.512820512816819.4871794872
130116815695.6363636364-14527.6363636364
1311349752569.2857142857-39072.2857142857
1326556784518.5128205128-18951.5128205128
1332516240224.6153846154-15062.6153846154
1343233434467.8571428571-2133.85714285714
1354073534467.85714285716267.14285714286
1369141399908.6896551724-8495.68965517242
13785515695.6363636364-14840.6363636364
1389706865127.631940.4
1394433915695.636363636428643.3636363636
1401411615695.6363636364-1579.63636363636
1411028815695.6363636364-5407.63636363636
1426562265127.6494.400000000001
1431656315695.6363636364867.363636363636
1447664399908.6896551724-23265.6896551724
14511068184518.512820512826162.4871794872
1462901115695.636363636413315.3636363636
1479269652569.285714285740126.7142857143
1489478564590.769230769230194.2307692308
149877315695.6363636364-6922.63636363636
1508320964590.769230769218618.2307692308
1519381599908.6896551724-6093.68965517242
1528668784518.51282051282168.48717948717
1533455334467.857142857185.1428571428551
154105547132351.15-26804.15
15510348799908.68965517243578.31034482758
156213688132351.1581336.85
1577122084518.5128205128-13298.5128205128
1582351734467.8571428571-10950.8571428571
1595692634467.857142857122458.1428571429
1609172184518.51282051287202.48717948717
161115168132351.15-17183.15
16211119452569.285714285758624.7142857143
1635100964590.7692307692-13581.7692307692
16413577799908.689655172435868.3103448276
1655151340224.615384615411288.3846153846
1667416399908.6896551724-25745.6896551724
1675163365127.6-13494.6
1687534534467.857142857140877.1428571429
1693341665127.6-31711.6
1708330565127.618177.4
1719895265127.633824.4
17210237284518.512820512817853.4871794872
1733723840224.6153846154-2986.61538461538
174103772132351.15-28579.15
17512396964590.769230769259378.2307692308
1762714234467.8571428571-7325.85714285714
17713540099908.689655172435491.3103448276
1782139915695.63636363645703.36363636364
17913011599908.689655172430206.3103448276
1802487434467.8571428571-9593.85714285714
1813498840224.6153846154-5236.61538461538
1824554915695.636363636429853.3636363636
183602315695.6363636364-9672.63636363636
1846446665127.6-661.599999999999
1855499099908.6896551724-44918.6896551724
186164415695.6363636364-14051.6363636364
187617915695.6363636364-9516.63636363636
188392615695.6363636364-11769.6363636364
1893275534467.8571428571-1712.85714285714
1903477715695.636363636419081.3636363636
1917322464590.76923076928633.23076923077
1922711434467.8571428571-7353.85714285714
1932076040224.6153846154-19464.6153846154
1943763652569.2857142857-14933.2857142857
1956546184518.5128205128-19057.5128205128
1963008034467.8571428571-4387.85714285714
1972409415695.63636363648398.36363636364



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = none ; par3 = ; par4 = no ;
R code (references can be found in the software module):
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}