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Description of Statistical Computation

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 computationWed, 14 Dec 2011 15:18:32 -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/14/t1323893942t96gszt2fue3hgp.htm/, Retrieved Wed, 01 May 2024 16:14:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155231, Retrieved Wed, 01 May 2024 16:14:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD    [Recursive Partitioning (Regression Trees)] [regression tree] [2011-12-14 20:18:32] [8432dc408001a08517818ba7ac24bdb0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1418	210907	56	396	81	3	79	30	115	94	112285	24188	146283	144	145	11
869	120982	56	297	55	4	58	28	109	103	84786	18273	98364	103	101	15
1530	176508	54	559	50	12	60	38	146	93	83123	14130	86146	98	98	19
3201	385534	92	1562	63	0	121	25	96	91	119182	33251	195663	150	144	23
1583	149061	44	656	66	5	43	26	100	93	116174	27101	95757	84	84	16
1439	165446	33	511	57	0	69	25	93	60	57635	16373	85584	80	79	21
1764	237213	84	655	74	0	78	38	140	123	66198	19716	143983	130	127	24
1373	133131	55	525	52	7	44	30	99	90	57793	9028	59238	60	60	15
4041	324799	154	1436	108	0	158	47	181	168	97668	29498	151511	140	133	17
1706	230964	53	612	43	4	102	30	116	115	133824	27563	136368	151	150	19
2152	236785	119	865	75	3	77	31	116	71	101481	18293	112642	91	91	19
1036	135473	41	385	32	0	82	23	88	66	99645	22530	94728	138	132	25
1929	215147	58	639	85	0	101	36	135	117	99052	35082	121527	124	124	19
2242	344297	75	963	86	1	80	30	108	108	67654	16116	127766	119	118	28
1220	153935	33	398	56	5	50	25	89	84	65553	15849	98958	73	70	24
2515	174724	92	966	135	0	123	34	129	120	69112	26569	85646	123	119	26
2147	174415	100	801	63	0	73	31	118	114	82753	24785	98579	90	89	15
2352	225548	112	892	81	5	81	31	118	94	85323	17569	130767	116	112	21
1638	223632	73	513	52	0	105	33	125	120	72654	23825	131741	113	108	26
1222	124817	40	469	44	0	47	25	95	81	30727	7869	53907	56	52	16
1677	210767	60	643	39	3	94	35	135	133	117478	37791	146761	119	116	16
1579	170266	62	535	73	4	44	42	154	122	74007	9605	82036	129	123	20
2452	294424	77	992	59	2	107	33	127	124	101494	34461	171975	175	162	24
2662	325107	99	937	64	0	84	36	136	126	79215	24919	159676	96	92	10
186	7176	17	70	1	0	0	0	0	0	1423	603	1929	0	0	19
865	106408	30	260	32	1	33	14	46	37	31081	12558	58391	41	41	25
1793	96560	76	503	129	0	42	17	54	38	22996	7784	31580	47	47	22
2527	265769	146	927	37	2	96	32	124	120	83122	28522	136815	126	120	15
1324	149112	56	537	65	6	56	35	128	95	60578	14459	69107	80	79	21
2702	175824	107	910	107	0	57	20	80	77	39992	14526	50495	70	65	22
1383	152871	58	532	74	5	59	28	97	90	79892	22240	108016	73	70	27
1179	111665	34	345	54	4	39	28	104	80	49810	11802	46341	57	55	26
4308	362301	119	1635	715	2	76	34	125	110	100708	11912	79336	68	67	26
1831	183167	66	557	66	0	91	39	149	138	82875	18220	93176	127	127	22
1438	168809	66	452	32	0	76	28	118	100	72260	21884	127969	102	99	20
496	24188	24	218	20	0	8	4	12	7	5950	2694	15049	7	7	22
2253	329267	259	764	71	8	79	39	144	140	115762	15808	155135	148	141	21
2352	218946	41	866	112	1	76	29	108	96	80670	25239	102996	112	109	22
2144	244052	68	574	66	5	101	44	166	164	143558	29801	160604	137	133	20
4691	341570	168	1276	190	1	94	21	80	78	117105	18450	158051	135	123	21
1112	103597	43	379	66	1	27	16	60	49	23789	7132	44547	26	26	20
1973	256462	105	798	56	0	123	35	127	124	105195	35940	174141	181	166	25
2474	235800	94	921	127	8	105	23	84	62	149193	46230	184301	190	179	18
1226	196553	57	503	50	2	41	29	111	99	95260	30546	129847	107	108	22
1389	174184	53	382	52	0	72	25	98	70	55183	19746	117286	94	90	25
1496	143246	103	464	42	5	67	27	105	104	106671	15977	71180	116	114	21
2269	187559	121	717	76	8	75	36	135	116	73511	22583	109377	106	103	20
1833	187681	62	690	67	2	114	28	107	91	92945	17274	85298	143	142	20
893	73566	32	385	39	6	22	23	88	67	22618	3007	23824	26	25	18
1403	167488	45	619	77	2	69	28	104	72	83737	21113	82981	113	113	8
1425	143756	46	479	57	0	105	34	132	120	69094	17401	73815	120	118	22
1840	243199	75	752	34	3	88	28	108	105	95536	23567	132190	134	129	26
1502	182999	88	430	39	6	73	34	129	104	225920	13065	128754	54	51	18
1420	152299	53	537	63	0	62	33	122	98	61370	14587	67808	78	76	20
2970	346485	90	1000	106	0	118	38	147	111	106117	24021	131722	121	118	24
1644	193339	78	465	47	2	100	35	87	71	84651	20537	106175	145	141	17
1654	122774	45	711	162	0	24	24	90	69	15986	4527	25157	27	27	20
1054	130585	46	299	57	5	67	29	109	107	95364	30495	76669	91	91	23
937	112611	41	248	36	0	46	20	78	73	26706	7117	57283	48	48	20
3004	286468	144	1162	263	1	57	29	111	107	89691	17719	105805	68	63	22
2547	148446	91	905	63	1	135	37	141	129	126846	33473	72413	150	144	20
1626	182079	63	512	63	2	124	33	124	118	102860	21115	96971	181	168	19
1468	140344	53	472	77	6	33	25	93	73	51715	7236	71299	65	64	15
2445	220516	62	905	79	1	98	32	124	119	55801	13790	77494	97	97	20
1964	243060	63	786	110	4	58	29	112	104	111813	32902	120336	121	117	22
1381	162765	32	489	56	2	68	28	108	107	120293	25131	93913	99	100	13
1659	232138	62	617	43	0	131	31	117	90	161647	35947	181248	188	187	20
2888	265318	117	925	111	10	110	52	199	197	115929	29848	146123	138	127	17
1290	85574	34	351	71	0	37	21	78	36	24266	6943	32036	40	37	14
2845	310839	92	1144	62	9	130	24	91	85	162901	42705	186646	254	245	22
1982	225060	93	669	56	7	93	41	158	139	109825	31808	102255	87	87	24
1904	232317	54	707	74	0	118	33	126	106	129838	26675	168237	178	177	22
1391	144966	144	458	60	0	39	32	122	50	37510	8435	64219	51	49	23
1559	164709	109	572	53	0	81	31	115	63	87771	36867	115338	176	177	17
2146	220801	75	720	105	1	51	18	72	63	44418	12607	84845	66	60	23
874	99466	50	273	32	0	28	23	91	69	192565	22609	153197	56	55	25
1590	92661	61	508	133	1	40	17	45	41	35232	5892	29877	39	39	16
1590	133328	55	506	79	0	56	20	78	56	40909	17014	63506	66	64	18
1210	61361	77	451	51	0	27	12	39	25	13294	5394	22445	27	26	20
1281	100750	72	407	67	0	83	30	119	93	140867	6440	68370	58	58	18
1105	102010	53	370	66	3	28	13	50	44	44332	5818	42071	26	26	24
1272	101523	42	316	76	0	59	22	88	87	61056	18647	50517	77	76	23
1944	243511	71	603	65	0	133	42	155	110	101338	20556	103950	130	129	24
391	22938	10	154	9	0	12	1	0	0	1168	238	5841	11	11	23
1605	152474	65	577	45	0	106	32	123	83	65567	22392	84396	101	101	23
1988	99923	66	617	115	0	44	25	99	80	32334	12237	35753	36	36	13
1386	132487	41	411	97	0	71	36	136	98	40735	8388	55515	120	89	20
2395	317394	86	975	53	1	116	31	117	82	91413	22120	209056	195	193	18
387	21054	16	146	2	0	4	0	0	0	855	338	6622	4	4	21
1742	209641	42	705	52	5	62	24	88	60	97068	11727	115814	89	84	17
620	22648	19	184	44	0	12	13	39	28	44339	3704	11609	24	23	20
449	31414	19	200	22	0	18	8	25	9	14116	3988	13155	39	39	19
800	46698	45	274	35	0	14	13	52	33	10288	3030	18274	14	14	18
1684	131698	65	502	74	0	60	19	75	59	65622	13520	72875	78	78	19
2699	244749	95	964	144	2	98	33	124	115	76643	20923	142775	106	101	22
1204	128423	64	369	89	8	32	38	145	120	92696	3769	20112	37	36	22
1138	97839	38	417	42	2	25	24	87	66	94785	12252	61023	77	75	15
2158	272458	65	822	99	0	100	43	162	152	93815	28864	132432	132	131	17
1111	172494	52	389	52	0	46	43	165	139	86687	21721	112494	144	131	19
1421	108043	62	466	29	1	45	14	54	38	34553	4821	45109	40	39	20
2833	328107	65	1255	125	3	129	41	159	144	105547	33644	170875	153	144	22
2922	351067	95	1024	95	3	136	45	170	160	213688	42935	214921	220	211	21
1002	158015	29	400	40	0	59	31	119	114	71220	18864	100226	79	78	19
2186	229242	247	719	128	4	63	31	120	119	91721	17939	78876	95	90	21
1035	84207	29	356	73	11	14	30	112	101	111194	325	6940	12	12	18
1417	120445	118	457	72	0	36	16	59	56	51009	13539	49025	63	57	16
3261	324598	110	1402	128	0	113	37	136	133	135777	34538	122037	134	133	20
1587	131069	67	600	61	4	47	30	107	83	51513	12198	53782	69	69	21
1424	204271	42	480	73	0	92	35	130	116	74163	26924	127748	119	119	15
946	116048	64	230	45	0	50	20	75	50	33416	10855	77395	63	61	20
1926	250047	81	651	58	0	41	18	71	61	83305	11932	89324	55	49	23
3352	299775	95	1367	97	9	91	31	120	97	98952	14300	103300	103	101	15
1641	195838	67	564	50	1	111	31	116	98	102372	25515	112283	197	196	18
2035	173260	63	716	37	3	41	21	79	78	37238	2805	10901	16	15	22
2312	254488	83	747	50	10	120	39	150	117	103772	29402	120691	140	136	16
961	92499	32	319	57	0	25	18	71	55	21399	5250	25899	21	21	17
1900	224330	83	612	52	1	131	39	144	132	130115	28608	139296	167	163	24
1254	135781	31	433	98	2	45	14	47	44	24874	8092	52678	32	29	13
1335	74408	67	434	61	4	29	7	28	21	34988	4473	23853	36	35	23
1597	81240	66	503	89	0	58	17	68	50	45549	1572	17306	13	13	5
1645	181633	70	564	48	2	47	30	110	73	64466	14817	89455	96	96	19
2429	271856	103	824	91	1	109	37	147	86	54990	16714	147866	151	151	24
872	95227	34	239	70	0	37	32	111	48	34777	1669	14336	23	23	19
1018	98146	40	459	37	0	15	17	68	48	27114	7768	30059	21	14	20
1314	59194	31	288	247	6	7	24	80	68	37636	6387	22097	20	20	22
1335	139942	42	498	46	0	54	22	88	87	65461	18715	96841	82	72	15
1403	118612	46	454	72	2	54	12	48	43	30080	7936	41907	90	87	19
910	72880	33	376	41	0	14	19	76	67	24094	8643	27080	25	21	25
616	65475	18	225	24	2	16	13	51	46	69008	7294	35885	60	56	21
771	71965	35	252	33	1	32	15	59	56	46090	7185	28313	85	82	19
1376	135131	66	481	87	0	38	15	60	60	34029	8509	36134	41	38	17
1232	108446	60	389	90	1	22	17	68	65	46300	13275	55764	26	25	15
1544	181528	54	609	69	0	32	16	61	60	40662	10737	66956	49	47	21
1230	134019	53	422	51	0	32	18	67	54	28987	8033	47487	46	45	24
1255	121848	39	339	45	0	37	17	64	52	30594	5401	35619	41	41	22
721	81872	45	245	25	0	32	16	64	61	27913	10856	45608	23	23	19
1109	58981	36	384	38	7	0	23	91	61	42744	2154	7721	14	14	20
740	53515	28	212	52	2	5	22	88	81	12934	6117	20634	16	16	21
728	56375	30	229	74	7	10	13	49	40	41385	4820	31931	21	21	19
689	65490	22	224	38	3	27	16	62	40	18653	5615	37754	32	27	22
995	76302	31	333	26	0	29	20	76	68	30976	8702	40557	35	33	14
1613	104011	55	384	67	6	25	22	88	79	63339	15340	94238	42	42	25
2048	98104	54	636	132	2	55	17	66	47	25568	8030	44197	68	68	11
301	30989	14	93	35	0	5	17	68	41	4154	1278	4103	6	6	16
1803	135458	81	581	118	3	43	12	48	29	19474	4236	44144	68	67	19
861	63123	43	304	43	1	34	17	68	60	39067	7196	27640	84	77	17
1451	74914	30	407	64	0	35	23	90	79	65892	6371	28990	30	30	20
628	31774	23	170	48	1	0	17	66	47	4143	1574	4694	0	0	22
1161	81437	38	312	64	0	37	14	54	40	28579	9620	42648	36	36	20
979	65745	53	340	75	0	26	21	77	42	38084	8645	25836	50	48	22
675	56653	45	168	39	0	38	18	68	49	27717	8987	22779	30	29	15
1241	158399	39	443	42	0	23	18	72	57	32928	5544	40820	30	28	23
1049	73624	24	367	93	0	30	17	64	40	19499	6909	32378	33	33	20
1081	91899	35	335	60	0	18	15	59	33	36874	6745	39613	37	33	17
1688	139526	151	364	71	0	28	21	84	77	48259	16724	60865	83	80	20
617	51567	30	206	27	2	21	14	56	45	28207	7025	20107	30	30	25
1656	102538	57	490	79	1	50	15	58	45	45833	9078	48231	51	51	22
705	86678	40	238	44	0	12	15	59	50	29156	4605	39725	19	18	16
1597	150580	77	530	124	0	27	22	83	71	45588	9653	62991	41	39	25
982	99611	35	291	81	0	41	21	81	67	45097	8914	49363	54	54	18
1212	99373	63	397	92	1	12	18	72	62	28394	6700	24552	25	24	19
1143	86230	44	467	42	0	21	17	61	54	18632	5788	31493	25	24	25
435	30837	19	178	10	0	8	4	15	4	2325	593	3439	8	8	23
532	31706	13	175	24	0	26	10	32	25	25139	4506	19555	26	26	24
882	89806	42	299	64	0	27	16	62	40	27975	6382	21228	20	19	21
830	64175	42	260	48	0	37	18	72	59	21792	6928	28893	46	47	21
652	59382	49	227	49	0	29	12	41	24	26263	1514	21425	47	47	22
707	119308	30	239	48	0	32	16	61	58	23686	9238	50276	37	37	21
954	76702	49	333	62	0	35	21	67	42	49303	8204	37643	51	51	18
285	19764	12	75	19	1	10	2	8	4	5752	2416	9927	10	10	13
733	84105	20	261	45	0	17	17	66	63	20055	5432	27184	34	34	22
642	64187	27	238	36	0	10	16	61	54	20154	5576	18475	12	11	23
894	72535	14	329	44	0	17	16	64	39	19540	6095	35873	27	21	15

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155231&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155231&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155231&T=0

As an alternative you can also use a QR Code:  
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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Goodness of Fit
Correlation0.9496
R-squared0.9018
RMSE11.473

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.9496 \tabularnewline
R-squared & 0.9018 \tabularnewline
RMSE & 11.473 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155231&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.9496[/C][/ROW]
[ROW][C]R-squared[/C][C]0.9018[/C][/ROW]
[ROW][C]RMSE[/C][C]11.473[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155231&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155231&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Goodness of Fit
Correlation0.9496
R-squared0.9018
RMSE11.473






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
19496.0588235294118-2.05882352941177
210396.05882352941186.94117647058823
393103.125-10.125
49196.0588235294118-5.05882352941177
59396.0588235294118-3.05882352941177
66072.88-12.88
7123120.6111111111112.38888888888889
89096.0588235294118-6.05882352941177
9168144.42857142857123.5714285714286
1011596.058823529411818.9411764705882
117196.0588235294118-25.0588235294118
126672.88-6.88
13117120.611111111111-3.61111111111111
1410896.058823529411811.9411764705882
158472.8811.12
16120120.611111111111-0.611111111111114
1711496.058823529411817.9411764705882
189496.0588235294118-2.05882352941177
19120120.611111111111-0.611111111111114
208170.285714285714310.7142857142857
21133120.61111111111112.3888888888889
22122144.428571428571-22.4285714285714
23124120.6111111111113.38888888888889
24126120.6111111111115.38888888888889
25012.25-12.25
263740.2142857142857-3.21428571428572
273840.2142857142857-2.21428571428572
28120120.611111111111-0.611111111111114
2995103.125-8.125
307772.884.12
319096.0588235294118-6.05882352941177
328070.28571428571439.71428571428571
33110103.1256.875
34138144.428571428571-6.42857142857142
3510096.05882352941183.94117647058823
36712.25-5.25
37140144.428571428571-4.42857142857142
389696.0588235294118-0.058823529411768
39164144.42857142857119.5714285714286
407872.885.12
414953.0731707317073-4.07317073170731
42124120.6111111111113.38888888888889
436272.88-10.88
449996.05882352941182.94117647058823
457070.2857142857143-0.285714285714292
4610496.05882352941187.94117647058823
47116120.611111111111-4.61111111111111
489196.0588235294118-5.05882352941177
496772.88-5.88
507296.0588235294118-24.0588235294118
51120120.611111111111-0.611111111111114
5210596.05882352941188.94117647058823
53104103.1250.875
549896.05882352941181.94117647058823
55111120.611111111111-9.61111111111111
567172.88-1.88
576972.88-3.88
5810796.058823529411810.9411764705882
597353.073170731707319.9268292682927
6010796.058823529411810.9411764705882
61129120.6111111111118.38888888888889
62118120.611111111111-2.61111111111111
637372.880.120000000000005
64119103.12515.875
6510496.05882352941187.94117647058823
6610796.058823529411810.9411764705882
679096.0588235294118-6.05882352941177
68197144.42857142857152.5714285714286
693653.0731707317073-17.0731707317073
708572.8812.12
71139144.428571428571-5.42857142857142
72106120.611111111111-14.6111111111111
735070.2857142857143-20.2857142857143
746396.0588235294118-33.0588235294118
756353.07317073170739.92682926829269
766972.88-3.88
774140.21428571428570.785714285714285
785653.07317073170732.92682926829269
792512.2512.75
809396.0588235294118-3.05882352941177
814440.21428571428573.78571428571428
828772.8814.12
83110144.428571428571-34.4285714285714
84012.25-12.25
858396.0588235294118-13.0588235294118
868070.28571428571439.71428571428571
8798103.125-5.125
888296.0588235294118-14.0588235294118
89012.25-12.25
906072.88-12.88
912812.2515.75
92912.25-3.25
933340.2142857142857-7.21428571428572
945953.07317073170735.92682926829269
95115120.611111111111-5.61111111111111
96120103.12516.875
976672.88-6.88
98152144.4285714285717.57142857142858
99139144.428571428571-5.42857142857142
1003840.2142857142857-2.21428571428572
101144144.428571428571-0.428571428571416
102160144.42857142857115.5714285714286
10311496.058823529411817.9411764705882
10411996.058823529411822.9411764705882
10510196.05882352941184.94117647058823
1065653.07317073170732.92682926829269
107133120.61111111111112.3888888888889
1088370.285714285714312.7142857142857
109116120.611111111111-4.61111111111111
1105053.0731707317073-3.07317073170731
1116153.07317073170737.92682926829269
1129796.05882352941180.941176470588232
1139896.05882352941181.94117647058823
1147872.885.12
115117144.428571428571-27.4285714285714
1165553.07317073170731.92682926829269
117132144.428571428571-12.4285714285714
1184440.21428571428573.78571428571428
1192112.258.75
1205053.0731707317073-3.07317073170731
1217396.0588235294118-23.0588235294118
12286103.125-17.125
1234870.2857142857143-22.2857142857143
1244853.0731707317073-5.07317073170731
1256872.88-4.88
1268772.8814.12
1274340.21428571428572.78571428571428
1286753.073170731707313.9268292682927
1294640.21428571428575.78571428571428
1305653.07317073170732.92682926829269
1316053.07317073170736.92682926829269
1326553.073170731707311.9268292682927
1336053.07317073170736.92682926829269
1345453.07317073170730.926829268292686
1355253.0731707317073-1.07317073170731
1366153.07317073170737.92682926829269
1376172.88-11.88
1388172.888.12
1394040.2142857142857-0.214285714285715
1404053.0731707317073-13.0731707317073
1416853.073170731707314.9268292682927
1427972.886.12
1434753.0731707317073-6.07317073170731
1444153.0731707317073-12.0731707317073
1452940.2142857142857-11.2142857142857
1466053.07317073170736.92682926829269
1477972.886.12
1484753.0731707317073-6.07317073170731
1494040.2142857142857-0.214285714285715
1504253.0731707317073-11.0731707317073
1514953.0731707317073-4.07317073170731
1525753.07317073170733.92682926829269
1534053.0731707317073-13.0731707317073
1543353.0731707317073-20.0731707317073
1557772.884.12
1564540.21428571428574.78571428571428
1574540.21428571428574.78571428571428
1585053.0731707317073-3.07317073170731
1597172.88-1.88
1606772.88-5.88
1616253.07317073170738.92682926829269
1625453.07317073170730.926829268292686
163412.25-8.25
1642512.2512.75
1654053.0731707317073-13.0731707317073
1665953.07317073170735.92682926829269
1672412.2511.75
1685853.07317073170734.92682926829269
1694253.0731707317073-11.0731707317073
170412.25-8.25
1716353.07317073170739.92682926829269
1725453.07317073170730.926829268292686
1733953.0731707317073-14.0731707317073

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 94 & 96.0588235294118 & -2.05882352941177 \tabularnewline
2 & 103 & 96.0588235294118 & 6.94117647058823 \tabularnewline
3 & 93 & 103.125 & -10.125 \tabularnewline
4 & 91 & 96.0588235294118 & -5.05882352941177 \tabularnewline
5 & 93 & 96.0588235294118 & -3.05882352941177 \tabularnewline
6 & 60 & 72.88 & -12.88 \tabularnewline
7 & 123 & 120.611111111111 & 2.38888888888889 \tabularnewline
8 & 90 & 96.0588235294118 & -6.05882352941177 \tabularnewline
9 & 168 & 144.428571428571 & 23.5714285714286 \tabularnewline
10 & 115 & 96.0588235294118 & 18.9411764705882 \tabularnewline
11 & 71 & 96.0588235294118 & -25.0588235294118 \tabularnewline
12 & 66 & 72.88 & -6.88 \tabularnewline
13 & 117 & 120.611111111111 & -3.61111111111111 \tabularnewline
14 & 108 & 96.0588235294118 & 11.9411764705882 \tabularnewline
15 & 84 & 72.88 & 11.12 \tabularnewline
16 & 120 & 120.611111111111 & -0.611111111111114 \tabularnewline
17 & 114 & 96.0588235294118 & 17.9411764705882 \tabularnewline
18 & 94 & 96.0588235294118 & -2.05882352941177 \tabularnewline
19 & 120 & 120.611111111111 & -0.611111111111114 \tabularnewline
20 & 81 & 70.2857142857143 & 10.7142857142857 \tabularnewline
21 & 133 & 120.611111111111 & 12.3888888888889 \tabularnewline
22 & 122 & 144.428571428571 & -22.4285714285714 \tabularnewline
23 & 124 & 120.611111111111 & 3.38888888888889 \tabularnewline
24 & 126 & 120.611111111111 & 5.38888888888889 \tabularnewline
25 & 0 & 12.25 & -12.25 \tabularnewline
26 & 37 & 40.2142857142857 & -3.21428571428572 \tabularnewline
27 & 38 & 40.2142857142857 & -2.21428571428572 \tabularnewline
28 & 120 & 120.611111111111 & -0.611111111111114 \tabularnewline
29 & 95 & 103.125 & -8.125 \tabularnewline
30 & 77 & 72.88 & 4.12 \tabularnewline
31 & 90 & 96.0588235294118 & -6.05882352941177 \tabularnewline
32 & 80 & 70.2857142857143 & 9.71428571428571 \tabularnewline
33 & 110 & 103.125 & 6.875 \tabularnewline
34 & 138 & 144.428571428571 & -6.42857142857142 \tabularnewline
35 & 100 & 96.0588235294118 & 3.94117647058823 \tabularnewline
36 & 7 & 12.25 & -5.25 \tabularnewline
37 & 140 & 144.428571428571 & -4.42857142857142 \tabularnewline
38 & 96 & 96.0588235294118 & -0.058823529411768 \tabularnewline
39 & 164 & 144.428571428571 & 19.5714285714286 \tabularnewline
40 & 78 & 72.88 & 5.12 \tabularnewline
41 & 49 & 53.0731707317073 & -4.07317073170731 \tabularnewline
42 & 124 & 120.611111111111 & 3.38888888888889 \tabularnewline
43 & 62 & 72.88 & -10.88 \tabularnewline
44 & 99 & 96.0588235294118 & 2.94117647058823 \tabularnewline
45 & 70 & 70.2857142857143 & -0.285714285714292 \tabularnewline
46 & 104 & 96.0588235294118 & 7.94117647058823 \tabularnewline
47 & 116 & 120.611111111111 & -4.61111111111111 \tabularnewline
48 & 91 & 96.0588235294118 & -5.05882352941177 \tabularnewline
49 & 67 & 72.88 & -5.88 \tabularnewline
50 & 72 & 96.0588235294118 & -24.0588235294118 \tabularnewline
51 & 120 & 120.611111111111 & -0.611111111111114 \tabularnewline
52 & 105 & 96.0588235294118 & 8.94117647058823 \tabularnewline
53 & 104 & 103.125 & 0.875 \tabularnewline
54 & 98 & 96.0588235294118 & 1.94117647058823 \tabularnewline
55 & 111 & 120.611111111111 & -9.61111111111111 \tabularnewline
56 & 71 & 72.88 & -1.88 \tabularnewline
57 & 69 & 72.88 & -3.88 \tabularnewline
58 & 107 & 96.0588235294118 & 10.9411764705882 \tabularnewline
59 & 73 & 53.0731707317073 & 19.9268292682927 \tabularnewline
60 & 107 & 96.0588235294118 & 10.9411764705882 \tabularnewline
61 & 129 & 120.611111111111 & 8.38888888888889 \tabularnewline
62 & 118 & 120.611111111111 & -2.61111111111111 \tabularnewline
63 & 73 & 72.88 & 0.120000000000005 \tabularnewline
64 & 119 & 103.125 & 15.875 \tabularnewline
65 & 104 & 96.0588235294118 & 7.94117647058823 \tabularnewline
66 & 107 & 96.0588235294118 & 10.9411764705882 \tabularnewline
67 & 90 & 96.0588235294118 & -6.05882352941177 \tabularnewline
68 & 197 & 144.428571428571 & 52.5714285714286 \tabularnewline
69 & 36 & 53.0731707317073 & -17.0731707317073 \tabularnewline
70 & 85 & 72.88 & 12.12 \tabularnewline
71 & 139 & 144.428571428571 & -5.42857142857142 \tabularnewline
72 & 106 & 120.611111111111 & -14.6111111111111 \tabularnewline
73 & 50 & 70.2857142857143 & -20.2857142857143 \tabularnewline
74 & 63 & 96.0588235294118 & -33.0588235294118 \tabularnewline
75 & 63 & 53.0731707317073 & 9.92682926829269 \tabularnewline
76 & 69 & 72.88 & -3.88 \tabularnewline
77 & 41 & 40.2142857142857 & 0.785714285714285 \tabularnewline
78 & 56 & 53.0731707317073 & 2.92682926829269 \tabularnewline
79 & 25 & 12.25 & 12.75 \tabularnewline
80 & 93 & 96.0588235294118 & -3.05882352941177 \tabularnewline
81 & 44 & 40.2142857142857 & 3.78571428571428 \tabularnewline
82 & 87 & 72.88 & 14.12 \tabularnewline
83 & 110 & 144.428571428571 & -34.4285714285714 \tabularnewline
84 & 0 & 12.25 & -12.25 \tabularnewline
85 & 83 & 96.0588235294118 & -13.0588235294118 \tabularnewline
86 & 80 & 70.2857142857143 & 9.71428571428571 \tabularnewline
87 & 98 & 103.125 & -5.125 \tabularnewline
88 & 82 & 96.0588235294118 & -14.0588235294118 \tabularnewline
89 & 0 & 12.25 & -12.25 \tabularnewline
90 & 60 & 72.88 & -12.88 \tabularnewline
91 & 28 & 12.25 & 15.75 \tabularnewline
92 & 9 & 12.25 & -3.25 \tabularnewline
93 & 33 & 40.2142857142857 & -7.21428571428572 \tabularnewline
94 & 59 & 53.0731707317073 & 5.92682926829269 \tabularnewline
95 & 115 & 120.611111111111 & -5.61111111111111 \tabularnewline
96 & 120 & 103.125 & 16.875 \tabularnewline
97 & 66 & 72.88 & -6.88 \tabularnewline
98 & 152 & 144.428571428571 & 7.57142857142858 \tabularnewline
99 & 139 & 144.428571428571 & -5.42857142857142 \tabularnewline
100 & 38 & 40.2142857142857 & -2.21428571428572 \tabularnewline
101 & 144 & 144.428571428571 & -0.428571428571416 \tabularnewline
102 & 160 & 144.428571428571 & 15.5714285714286 \tabularnewline
103 & 114 & 96.0588235294118 & 17.9411764705882 \tabularnewline
104 & 119 & 96.0588235294118 & 22.9411764705882 \tabularnewline
105 & 101 & 96.0588235294118 & 4.94117647058823 \tabularnewline
106 & 56 & 53.0731707317073 & 2.92682926829269 \tabularnewline
107 & 133 & 120.611111111111 & 12.3888888888889 \tabularnewline
108 & 83 & 70.2857142857143 & 12.7142857142857 \tabularnewline
109 & 116 & 120.611111111111 & -4.61111111111111 \tabularnewline
110 & 50 & 53.0731707317073 & -3.07317073170731 \tabularnewline
111 & 61 & 53.0731707317073 & 7.92682926829269 \tabularnewline
112 & 97 & 96.0588235294118 & 0.941176470588232 \tabularnewline
113 & 98 & 96.0588235294118 & 1.94117647058823 \tabularnewline
114 & 78 & 72.88 & 5.12 \tabularnewline
115 & 117 & 144.428571428571 & -27.4285714285714 \tabularnewline
116 & 55 & 53.0731707317073 & 1.92682926829269 \tabularnewline
117 & 132 & 144.428571428571 & -12.4285714285714 \tabularnewline
118 & 44 & 40.2142857142857 & 3.78571428571428 \tabularnewline
119 & 21 & 12.25 & 8.75 \tabularnewline
120 & 50 & 53.0731707317073 & -3.07317073170731 \tabularnewline
121 & 73 & 96.0588235294118 & -23.0588235294118 \tabularnewline
122 & 86 & 103.125 & -17.125 \tabularnewline
123 & 48 & 70.2857142857143 & -22.2857142857143 \tabularnewline
124 & 48 & 53.0731707317073 & -5.07317073170731 \tabularnewline
125 & 68 & 72.88 & -4.88 \tabularnewline
126 & 87 & 72.88 & 14.12 \tabularnewline
127 & 43 & 40.2142857142857 & 2.78571428571428 \tabularnewline
128 & 67 & 53.0731707317073 & 13.9268292682927 \tabularnewline
129 & 46 & 40.2142857142857 & 5.78571428571428 \tabularnewline
130 & 56 & 53.0731707317073 & 2.92682926829269 \tabularnewline
131 & 60 & 53.0731707317073 & 6.92682926829269 \tabularnewline
132 & 65 & 53.0731707317073 & 11.9268292682927 \tabularnewline
133 & 60 & 53.0731707317073 & 6.92682926829269 \tabularnewline
134 & 54 & 53.0731707317073 & 0.926829268292686 \tabularnewline
135 & 52 & 53.0731707317073 & -1.07317073170731 \tabularnewline
136 & 61 & 53.0731707317073 & 7.92682926829269 \tabularnewline
137 & 61 & 72.88 & -11.88 \tabularnewline
138 & 81 & 72.88 & 8.12 \tabularnewline
139 & 40 & 40.2142857142857 & -0.214285714285715 \tabularnewline
140 & 40 & 53.0731707317073 & -13.0731707317073 \tabularnewline
141 & 68 & 53.0731707317073 & 14.9268292682927 \tabularnewline
142 & 79 & 72.88 & 6.12 \tabularnewline
143 & 47 & 53.0731707317073 & -6.07317073170731 \tabularnewline
144 & 41 & 53.0731707317073 & -12.0731707317073 \tabularnewline
145 & 29 & 40.2142857142857 & -11.2142857142857 \tabularnewline
146 & 60 & 53.0731707317073 & 6.92682926829269 \tabularnewline
147 & 79 & 72.88 & 6.12 \tabularnewline
148 & 47 & 53.0731707317073 & -6.07317073170731 \tabularnewline
149 & 40 & 40.2142857142857 & -0.214285714285715 \tabularnewline
150 & 42 & 53.0731707317073 & -11.0731707317073 \tabularnewline
151 & 49 & 53.0731707317073 & -4.07317073170731 \tabularnewline
152 & 57 & 53.0731707317073 & 3.92682926829269 \tabularnewline
153 & 40 & 53.0731707317073 & -13.0731707317073 \tabularnewline
154 & 33 & 53.0731707317073 & -20.0731707317073 \tabularnewline
155 & 77 & 72.88 & 4.12 \tabularnewline
156 & 45 & 40.2142857142857 & 4.78571428571428 \tabularnewline
157 & 45 & 40.2142857142857 & 4.78571428571428 \tabularnewline
158 & 50 & 53.0731707317073 & -3.07317073170731 \tabularnewline
159 & 71 & 72.88 & -1.88 \tabularnewline
160 & 67 & 72.88 & -5.88 \tabularnewline
161 & 62 & 53.0731707317073 & 8.92682926829269 \tabularnewline
162 & 54 & 53.0731707317073 & 0.926829268292686 \tabularnewline
163 & 4 & 12.25 & -8.25 \tabularnewline
164 & 25 & 12.25 & 12.75 \tabularnewline
165 & 40 & 53.0731707317073 & -13.0731707317073 \tabularnewline
166 & 59 & 53.0731707317073 & 5.92682926829269 \tabularnewline
167 & 24 & 12.25 & 11.75 \tabularnewline
168 & 58 & 53.0731707317073 & 4.92682926829269 \tabularnewline
169 & 42 & 53.0731707317073 & -11.0731707317073 \tabularnewline
170 & 4 & 12.25 & -8.25 \tabularnewline
171 & 63 & 53.0731707317073 & 9.92682926829269 \tabularnewline
172 & 54 & 53.0731707317073 & 0.926829268292686 \tabularnewline
173 & 39 & 53.0731707317073 & -14.0731707317073 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155231&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]94[/C][C]96.0588235294118[/C][C]-2.05882352941177[/C][/ROW]
[ROW][C]2[/C][C]103[/C][C]96.0588235294118[/C][C]6.94117647058823[/C][/ROW]
[ROW][C]3[/C][C]93[/C][C]103.125[/C][C]-10.125[/C][/ROW]
[ROW][C]4[/C][C]91[/C][C]96.0588235294118[/C][C]-5.05882352941177[/C][/ROW]
[ROW][C]5[/C][C]93[/C][C]96.0588235294118[/C][C]-3.05882352941177[/C][/ROW]
[ROW][C]6[/C][C]60[/C][C]72.88[/C][C]-12.88[/C][/ROW]
[ROW][C]7[/C][C]123[/C][C]120.611111111111[/C][C]2.38888888888889[/C][/ROW]
[ROW][C]8[/C][C]90[/C][C]96.0588235294118[/C][C]-6.05882352941177[/C][/ROW]
[ROW][C]9[/C][C]168[/C][C]144.428571428571[/C][C]23.5714285714286[/C][/ROW]
[ROW][C]10[/C][C]115[/C][C]96.0588235294118[/C][C]18.9411764705882[/C][/ROW]
[ROW][C]11[/C][C]71[/C][C]96.0588235294118[/C][C]-25.0588235294118[/C][/ROW]
[ROW][C]12[/C][C]66[/C][C]72.88[/C][C]-6.88[/C][/ROW]
[ROW][C]13[/C][C]117[/C][C]120.611111111111[/C][C]-3.61111111111111[/C][/ROW]
[ROW][C]14[/C][C]108[/C][C]96.0588235294118[/C][C]11.9411764705882[/C][/ROW]
[ROW][C]15[/C][C]84[/C][C]72.88[/C][C]11.12[/C][/ROW]
[ROW][C]16[/C][C]120[/C][C]120.611111111111[/C][C]-0.611111111111114[/C][/ROW]
[ROW][C]17[/C][C]114[/C][C]96.0588235294118[/C][C]17.9411764705882[/C][/ROW]
[ROW][C]18[/C][C]94[/C][C]96.0588235294118[/C][C]-2.05882352941177[/C][/ROW]
[ROW][C]19[/C][C]120[/C][C]120.611111111111[/C][C]-0.611111111111114[/C][/ROW]
[ROW][C]20[/C][C]81[/C][C]70.2857142857143[/C][C]10.7142857142857[/C][/ROW]
[ROW][C]21[/C][C]133[/C][C]120.611111111111[/C][C]12.3888888888889[/C][/ROW]
[ROW][C]22[/C][C]122[/C][C]144.428571428571[/C][C]-22.4285714285714[/C][/ROW]
[ROW][C]23[/C][C]124[/C][C]120.611111111111[/C][C]3.38888888888889[/C][/ROW]
[ROW][C]24[/C][C]126[/C][C]120.611111111111[/C][C]5.38888888888889[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]12.25[/C][C]-12.25[/C][/ROW]
[ROW][C]26[/C][C]37[/C][C]40.2142857142857[/C][C]-3.21428571428572[/C][/ROW]
[ROW][C]27[/C][C]38[/C][C]40.2142857142857[/C][C]-2.21428571428572[/C][/ROW]
[ROW][C]28[/C][C]120[/C][C]120.611111111111[/C][C]-0.611111111111114[/C][/ROW]
[ROW][C]29[/C][C]95[/C][C]103.125[/C][C]-8.125[/C][/ROW]
[ROW][C]30[/C][C]77[/C][C]72.88[/C][C]4.12[/C][/ROW]
[ROW][C]31[/C][C]90[/C][C]96.0588235294118[/C][C]-6.05882352941177[/C][/ROW]
[ROW][C]32[/C][C]80[/C][C]70.2857142857143[/C][C]9.71428571428571[/C][/ROW]
[ROW][C]33[/C][C]110[/C][C]103.125[/C][C]6.875[/C][/ROW]
[ROW][C]34[/C][C]138[/C][C]144.428571428571[/C][C]-6.42857142857142[/C][/ROW]
[ROW][C]35[/C][C]100[/C][C]96.0588235294118[/C][C]3.94117647058823[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.25[/C][C]-5.25[/C][/ROW]
[ROW][C]37[/C][C]140[/C][C]144.428571428571[/C][C]-4.42857142857142[/C][/ROW]
[ROW][C]38[/C][C]96[/C][C]96.0588235294118[/C][C]-0.058823529411768[/C][/ROW]
[ROW][C]39[/C][C]164[/C][C]144.428571428571[/C][C]19.5714285714286[/C][/ROW]
[ROW][C]40[/C][C]78[/C][C]72.88[/C][C]5.12[/C][/ROW]
[ROW][C]41[/C][C]49[/C][C]53.0731707317073[/C][C]-4.07317073170731[/C][/ROW]
[ROW][C]42[/C][C]124[/C][C]120.611111111111[/C][C]3.38888888888889[/C][/ROW]
[ROW][C]43[/C][C]62[/C][C]72.88[/C][C]-10.88[/C][/ROW]
[ROW][C]44[/C][C]99[/C][C]96.0588235294118[/C][C]2.94117647058823[/C][/ROW]
[ROW][C]45[/C][C]70[/C][C]70.2857142857143[/C][C]-0.285714285714292[/C][/ROW]
[ROW][C]46[/C][C]104[/C][C]96.0588235294118[/C][C]7.94117647058823[/C][/ROW]
[ROW][C]47[/C][C]116[/C][C]120.611111111111[/C][C]-4.61111111111111[/C][/ROW]
[ROW][C]48[/C][C]91[/C][C]96.0588235294118[/C][C]-5.05882352941177[/C][/ROW]
[ROW][C]49[/C][C]67[/C][C]72.88[/C][C]-5.88[/C][/ROW]
[ROW][C]50[/C][C]72[/C][C]96.0588235294118[/C][C]-24.0588235294118[/C][/ROW]
[ROW][C]51[/C][C]120[/C][C]120.611111111111[/C][C]-0.611111111111114[/C][/ROW]
[ROW][C]52[/C][C]105[/C][C]96.0588235294118[/C][C]8.94117647058823[/C][/ROW]
[ROW][C]53[/C][C]104[/C][C]103.125[/C][C]0.875[/C][/ROW]
[ROW][C]54[/C][C]98[/C][C]96.0588235294118[/C][C]1.94117647058823[/C][/ROW]
[ROW][C]55[/C][C]111[/C][C]120.611111111111[/C][C]-9.61111111111111[/C][/ROW]
[ROW][C]56[/C][C]71[/C][C]72.88[/C][C]-1.88[/C][/ROW]
[ROW][C]57[/C][C]69[/C][C]72.88[/C][C]-3.88[/C][/ROW]
[ROW][C]58[/C][C]107[/C][C]96.0588235294118[/C][C]10.9411764705882[/C][/ROW]
[ROW][C]59[/C][C]73[/C][C]53.0731707317073[/C][C]19.9268292682927[/C][/ROW]
[ROW][C]60[/C][C]107[/C][C]96.0588235294118[/C][C]10.9411764705882[/C][/ROW]
[ROW][C]61[/C][C]129[/C][C]120.611111111111[/C][C]8.38888888888889[/C][/ROW]
[ROW][C]62[/C][C]118[/C][C]120.611111111111[/C][C]-2.61111111111111[/C][/ROW]
[ROW][C]63[/C][C]73[/C][C]72.88[/C][C]0.120000000000005[/C][/ROW]
[ROW][C]64[/C][C]119[/C][C]103.125[/C][C]15.875[/C][/ROW]
[ROW][C]65[/C][C]104[/C][C]96.0588235294118[/C][C]7.94117647058823[/C][/ROW]
[ROW][C]66[/C][C]107[/C][C]96.0588235294118[/C][C]10.9411764705882[/C][/ROW]
[ROW][C]67[/C][C]90[/C][C]96.0588235294118[/C][C]-6.05882352941177[/C][/ROW]
[ROW][C]68[/C][C]197[/C][C]144.428571428571[/C][C]52.5714285714286[/C][/ROW]
[ROW][C]69[/C][C]36[/C][C]53.0731707317073[/C][C]-17.0731707317073[/C][/ROW]
[ROW][C]70[/C][C]85[/C][C]72.88[/C][C]12.12[/C][/ROW]
[ROW][C]71[/C][C]139[/C][C]144.428571428571[/C][C]-5.42857142857142[/C][/ROW]
[ROW][C]72[/C][C]106[/C][C]120.611111111111[/C][C]-14.6111111111111[/C][/ROW]
[ROW][C]73[/C][C]50[/C][C]70.2857142857143[/C][C]-20.2857142857143[/C][/ROW]
[ROW][C]74[/C][C]63[/C][C]96.0588235294118[/C][C]-33.0588235294118[/C][/ROW]
[ROW][C]75[/C][C]63[/C][C]53.0731707317073[/C][C]9.92682926829269[/C][/ROW]
[ROW][C]76[/C][C]69[/C][C]72.88[/C][C]-3.88[/C][/ROW]
[ROW][C]77[/C][C]41[/C][C]40.2142857142857[/C][C]0.785714285714285[/C][/ROW]
[ROW][C]78[/C][C]56[/C][C]53.0731707317073[/C][C]2.92682926829269[/C][/ROW]
[ROW][C]79[/C][C]25[/C][C]12.25[/C][C]12.75[/C][/ROW]
[ROW][C]80[/C][C]93[/C][C]96.0588235294118[/C][C]-3.05882352941177[/C][/ROW]
[ROW][C]81[/C][C]44[/C][C]40.2142857142857[/C][C]3.78571428571428[/C][/ROW]
[ROW][C]82[/C][C]87[/C][C]72.88[/C][C]14.12[/C][/ROW]
[ROW][C]83[/C][C]110[/C][C]144.428571428571[/C][C]-34.4285714285714[/C][/ROW]
[ROW][C]84[/C][C]0[/C][C]12.25[/C][C]-12.25[/C][/ROW]
[ROW][C]85[/C][C]83[/C][C]96.0588235294118[/C][C]-13.0588235294118[/C][/ROW]
[ROW][C]86[/C][C]80[/C][C]70.2857142857143[/C][C]9.71428571428571[/C][/ROW]
[ROW][C]87[/C][C]98[/C][C]103.125[/C][C]-5.125[/C][/ROW]
[ROW][C]88[/C][C]82[/C][C]96.0588235294118[/C][C]-14.0588235294118[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]12.25[/C][C]-12.25[/C][/ROW]
[ROW][C]90[/C][C]60[/C][C]72.88[/C][C]-12.88[/C][/ROW]
[ROW][C]91[/C][C]28[/C][C]12.25[/C][C]15.75[/C][/ROW]
[ROW][C]92[/C][C]9[/C][C]12.25[/C][C]-3.25[/C][/ROW]
[ROW][C]93[/C][C]33[/C][C]40.2142857142857[/C][C]-7.21428571428572[/C][/ROW]
[ROW][C]94[/C][C]59[/C][C]53.0731707317073[/C][C]5.92682926829269[/C][/ROW]
[ROW][C]95[/C][C]115[/C][C]120.611111111111[/C][C]-5.61111111111111[/C][/ROW]
[ROW][C]96[/C][C]120[/C][C]103.125[/C][C]16.875[/C][/ROW]
[ROW][C]97[/C][C]66[/C][C]72.88[/C][C]-6.88[/C][/ROW]
[ROW][C]98[/C][C]152[/C][C]144.428571428571[/C][C]7.57142857142858[/C][/ROW]
[ROW][C]99[/C][C]139[/C][C]144.428571428571[/C][C]-5.42857142857142[/C][/ROW]
[ROW][C]100[/C][C]38[/C][C]40.2142857142857[/C][C]-2.21428571428572[/C][/ROW]
[ROW][C]101[/C][C]144[/C][C]144.428571428571[/C][C]-0.428571428571416[/C][/ROW]
[ROW][C]102[/C][C]160[/C][C]144.428571428571[/C][C]15.5714285714286[/C][/ROW]
[ROW][C]103[/C][C]114[/C][C]96.0588235294118[/C][C]17.9411764705882[/C][/ROW]
[ROW][C]104[/C][C]119[/C][C]96.0588235294118[/C][C]22.9411764705882[/C][/ROW]
[ROW][C]105[/C][C]101[/C][C]96.0588235294118[/C][C]4.94117647058823[/C][/ROW]
[ROW][C]106[/C][C]56[/C][C]53.0731707317073[/C][C]2.92682926829269[/C][/ROW]
[ROW][C]107[/C][C]133[/C][C]120.611111111111[/C][C]12.3888888888889[/C][/ROW]
[ROW][C]108[/C][C]83[/C][C]70.2857142857143[/C][C]12.7142857142857[/C][/ROW]
[ROW][C]109[/C][C]116[/C][C]120.611111111111[/C][C]-4.61111111111111[/C][/ROW]
[ROW][C]110[/C][C]50[/C][C]53.0731707317073[/C][C]-3.07317073170731[/C][/ROW]
[ROW][C]111[/C][C]61[/C][C]53.0731707317073[/C][C]7.92682926829269[/C][/ROW]
[ROW][C]112[/C][C]97[/C][C]96.0588235294118[/C][C]0.941176470588232[/C][/ROW]
[ROW][C]113[/C][C]98[/C][C]96.0588235294118[/C][C]1.94117647058823[/C][/ROW]
[ROW][C]114[/C][C]78[/C][C]72.88[/C][C]5.12[/C][/ROW]
[ROW][C]115[/C][C]117[/C][C]144.428571428571[/C][C]-27.4285714285714[/C][/ROW]
[ROW][C]116[/C][C]55[/C][C]53.0731707317073[/C][C]1.92682926829269[/C][/ROW]
[ROW][C]117[/C][C]132[/C][C]144.428571428571[/C][C]-12.4285714285714[/C][/ROW]
[ROW][C]118[/C][C]44[/C][C]40.2142857142857[/C][C]3.78571428571428[/C][/ROW]
[ROW][C]119[/C][C]21[/C][C]12.25[/C][C]8.75[/C][/ROW]
[ROW][C]120[/C][C]50[/C][C]53.0731707317073[/C][C]-3.07317073170731[/C][/ROW]
[ROW][C]121[/C][C]73[/C][C]96.0588235294118[/C][C]-23.0588235294118[/C][/ROW]
[ROW][C]122[/C][C]86[/C][C]103.125[/C][C]-17.125[/C][/ROW]
[ROW][C]123[/C][C]48[/C][C]70.2857142857143[/C][C]-22.2857142857143[/C][/ROW]
[ROW][C]124[/C][C]48[/C][C]53.0731707317073[/C][C]-5.07317073170731[/C][/ROW]
[ROW][C]125[/C][C]68[/C][C]72.88[/C][C]-4.88[/C][/ROW]
[ROW][C]126[/C][C]87[/C][C]72.88[/C][C]14.12[/C][/ROW]
[ROW][C]127[/C][C]43[/C][C]40.2142857142857[/C][C]2.78571428571428[/C][/ROW]
[ROW][C]128[/C][C]67[/C][C]53.0731707317073[/C][C]13.9268292682927[/C][/ROW]
[ROW][C]129[/C][C]46[/C][C]40.2142857142857[/C][C]5.78571428571428[/C][/ROW]
[ROW][C]130[/C][C]56[/C][C]53.0731707317073[/C][C]2.92682926829269[/C][/ROW]
[ROW][C]131[/C][C]60[/C][C]53.0731707317073[/C][C]6.92682926829269[/C][/ROW]
[ROW][C]132[/C][C]65[/C][C]53.0731707317073[/C][C]11.9268292682927[/C][/ROW]
[ROW][C]133[/C][C]60[/C][C]53.0731707317073[/C][C]6.92682926829269[/C][/ROW]
[ROW][C]134[/C][C]54[/C][C]53.0731707317073[/C][C]0.926829268292686[/C][/ROW]
[ROW][C]135[/C][C]52[/C][C]53.0731707317073[/C][C]-1.07317073170731[/C][/ROW]
[ROW][C]136[/C][C]61[/C][C]53.0731707317073[/C][C]7.92682926829269[/C][/ROW]
[ROW][C]137[/C][C]61[/C][C]72.88[/C][C]-11.88[/C][/ROW]
[ROW][C]138[/C][C]81[/C][C]72.88[/C][C]8.12[/C][/ROW]
[ROW][C]139[/C][C]40[/C][C]40.2142857142857[/C][C]-0.214285714285715[/C][/ROW]
[ROW][C]140[/C][C]40[/C][C]53.0731707317073[/C][C]-13.0731707317073[/C][/ROW]
[ROW][C]141[/C][C]68[/C][C]53.0731707317073[/C][C]14.9268292682927[/C][/ROW]
[ROW][C]142[/C][C]79[/C][C]72.88[/C][C]6.12[/C][/ROW]
[ROW][C]143[/C][C]47[/C][C]53.0731707317073[/C][C]-6.07317073170731[/C][/ROW]
[ROW][C]144[/C][C]41[/C][C]53.0731707317073[/C][C]-12.0731707317073[/C][/ROW]
[ROW][C]145[/C][C]29[/C][C]40.2142857142857[/C][C]-11.2142857142857[/C][/ROW]
[ROW][C]146[/C][C]60[/C][C]53.0731707317073[/C][C]6.92682926829269[/C][/ROW]
[ROW][C]147[/C][C]79[/C][C]72.88[/C][C]6.12[/C][/ROW]
[ROW][C]148[/C][C]47[/C][C]53.0731707317073[/C][C]-6.07317073170731[/C][/ROW]
[ROW][C]149[/C][C]40[/C][C]40.2142857142857[/C][C]-0.214285714285715[/C][/ROW]
[ROW][C]150[/C][C]42[/C][C]53.0731707317073[/C][C]-11.0731707317073[/C][/ROW]
[ROW][C]151[/C][C]49[/C][C]53.0731707317073[/C][C]-4.07317073170731[/C][/ROW]
[ROW][C]152[/C][C]57[/C][C]53.0731707317073[/C][C]3.92682926829269[/C][/ROW]
[ROW][C]153[/C][C]40[/C][C]53.0731707317073[/C][C]-13.0731707317073[/C][/ROW]
[ROW][C]154[/C][C]33[/C][C]53.0731707317073[/C][C]-20.0731707317073[/C][/ROW]
[ROW][C]155[/C][C]77[/C][C]72.88[/C][C]4.12[/C][/ROW]
[ROW][C]156[/C][C]45[/C][C]40.2142857142857[/C][C]4.78571428571428[/C][/ROW]
[ROW][C]157[/C][C]45[/C][C]40.2142857142857[/C][C]4.78571428571428[/C][/ROW]
[ROW][C]158[/C][C]50[/C][C]53.0731707317073[/C][C]-3.07317073170731[/C][/ROW]
[ROW][C]159[/C][C]71[/C][C]72.88[/C][C]-1.88[/C][/ROW]
[ROW][C]160[/C][C]67[/C][C]72.88[/C][C]-5.88[/C][/ROW]
[ROW][C]161[/C][C]62[/C][C]53.0731707317073[/C][C]8.92682926829269[/C][/ROW]
[ROW][C]162[/C][C]54[/C][C]53.0731707317073[/C][C]0.926829268292686[/C][/ROW]
[ROW][C]163[/C][C]4[/C][C]12.25[/C][C]-8.25[/C][/ROW]
[ROW][C]164[/C][C]25[/C][C]12.25[/C][C]12.75[/C][/ROW]
[ROW][C]165[/C][C]40[/C][C]53.0731707317073[/C][C]-13.0731707317073[/C][/ROW]
[ROW][C]166[/C][C]59[/C][C]53.0731707317073[/C][C]5.92682926829269[/C][/ROW]
[ROW][C]167[/C][C]24[/C][C]12.25[/C][C]11.75[/C][/ROW]
[ROW][C]168[/C][C]58[/C][C]53.0731707317073[/C][C]4.92682926829269[/C][/ROW]
[ROW][C]169[/C][C]42[/C][C]53.0731707317073[/C][C]-11.0731707317073[/C][/ROW]
[ROW][C]170[/C][C]4[/C][C]12.25[/C][C]-8.25[/C][/ROW]
[ROW][C]171[/C][C]63[/C][C]53.0731707317073[/C][C]9.92682926829269[/C][/ROW]
[ROW][C]172[/C][C]54[/C][C]53.0731707317073[/C][C]0.926829268292686[/C][/ROW]
[ROW][C]173[/C][C]39[/C][C]53.0731707317073[/C][C]-14.0731707317073[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155231&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155231&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
19496.0588235294118-2.05882352941177
210396.05882352941186.94117647058823
393103.125-10.125
49196.0588235294118-5.05882352941177
59396.0588235294118-3.05882352941177
66072.88-12.88
7123120.6111111111112.38888888888889
89096.0588235294118-6.05882352941177
9168144.42857142857123.5714285714286
1011596.058823529411818.9411764705882
117196.0588235294118-25.0588235294118
126672.88-6.88
13117120.611111111111-3.61111111111111
1410896.058823529411811.9411764705882
158472.8811.12
16120120.611111111111-0.611111111111114
1711496.058823529411817.9411764705882
189496.0588235294118-2.05882352941177
19120120.611111111111-0.611111111111114
208170.285714285714310.7142857142857
21133120.61111111111112.3888888888889
22122144.428571428571-22.4285714285714
23124120.6111111111113.38888888888889
24126120.6111111111115.38888888888889
25012.25-12.25
263740.2142857142857-3.21428571428572
273840.2142857142857-2.21428571428572
28120120.611111111111-0.611111111111114
2995103.125-8.125
307772.884.12
319096.0588235294118-6.05882352941177
328070.28571428571439.71428571428571
33110103.1256.875
34138144.428571428571-6.42857142857142
3510096.05882352941183.94117647058823
36712.25-5.25
37140144.428571428571-4.42857142857142
389696.0588235294118-0.058823529411768
39164144.42857142857119.5714285714286
407872.885.12
414953.0731707317073-4.07317073170731
42124120.6111111111113.38888888888889
436272.88-10.88
449996.05882352941182.94117647058823
457070.2857142857143-0.285714285714292
4610496.05882352941187.94117647058823
47116120.611111111111-4.61111111111111
489196.0588235294118-5.05882352941177
496772.88-5.88
507296.0588235294118-24.0588235294118
51120120.611111111111-0.611111111111114
5210596.05882352941188.94117647058823
53104103.1250.875
549896.05882352941181.94117647058823
55111120.611111111111-9.61111111111111
567172.88-1.88
576972.88-3.88
5810796.058823529411810.9411764705882
597353.073170731707319.9268292682927
6010796.058823529411810.9411764705882
61129120.6111111111118.38888888888889
62118120.611111111111-2.61111111111111
637372.880.120000000000005
64119103.12515.875
6510496.05882352941187.94117647058823
6610796.058823529411810.9411764705882
679096.0588235294118-6.05882352941177
68197144.42857142857152.5714285714286
693653.0731707317073-17.0731707317073
708572.8812.12
71139144.428571428571-5.42857142857142
72106120.611111111111-14.6111111111111
735070.2857142857143-20.2857142857143
746396.0588235294118-33.0588235294118
756353.07317073170739.92682926829269
766972.88-3.88
774140.21428571428570.785714285714285
785653.07317073170732.92682926829269
792512.2512.75
809396.0588235294118-3.05882352941177
814440.21428571428573.78571428571428
828772.8814.12
83110144.428571428571-34.4285714285714
84012.25-12.25
858396.0588235294118-13.0588235294118
868070.28571428571439.71428571428571
8798103.125-5.125
888296.0588235294118-14.0588235294118
89012.25-12.25
906072.88-12.88
912812.2515.75
92912.25-3.25
933340.2142857142857-7.21428571428572
945953.07317073170735.92682926829269
95115120.611111111111-5.61111111111111
96120103.12516.875
976672.88-6.88
98152144.4285714285717.57142857142858
99139144.428571428571-5.42857142857142
1003840.2142857142857-2.21428571428572
101144144.428571428571-0.428571428571416
102160144.42857142857115.5714285714286
10311496.058823529411817.9411764705882
10411996.058823529411822.9411764705882
10510196.05882352941184.94117647058823
1065653.07317073170732.92682926829269
107133120.61111111111112.3888888888889
1088370.285714285714312.7142857142857
109116120.611111111111-4.61111111111111
1105053.0731707317073-3.07317073170731
1116153.07317073170737.92682926829269
1129796.05882352941180.941176470588232
1139896.05882352941181.94117647058823
1147872.885.12
115117144.428571428571-27.4285714285714
1165553.07317073170731.92682926829269
117132144.428571428571-12.4285714285714
1184440.21428571428573.78571428571428
1192112.258.75
1205053.0731707317073-3.07317073170731
1217396.0588235294118-23.0588235294118
12286103.125-17.125
1234870.2857142857143-22.2857142857143
1244853.0731707317073-5.07317073170731
1256872.88-4.88
1268772.8814.12
1274340.21428571428572.78571428571428
1286753.073170731707313.9268292682927
1294640.21428571428575.78571428571428
1305653.07317073170732.92682926829269
1316053.07317073170736.92682926829269
1326553.073170731707311.9268292682927
1336053.07317073170736.92682926829269
1345453.07317073170730.926829268292686
1355253.0731707317073-1.07317073170731
1366153.07317073170737.92682926829269
1376172.88-11.88
1388172.888.12
1394040.2142857142857-0.214285714285715
1404053.0731707317073-13.0731707317073
1416853.073170731707314.9268292682927
1427972.886.12
1434753.0731707317073-6.07317073170731
1444153.0731707317073-12.0731707317073
1452940.2142857142857-11.2142857142857
1466053.07317073170736.92682926829269
1477972.886.12
1484753.0731707317073-6.07317073170731
1494040.2142857142857-0.214285714285715
1504253.0731707317073-11.0731707317073
1514953.0731707317073-4.07317073170731
1525753.07317073170733.92682926829269
1534053.0731707317073-13.0731707317073
1543353.0731707317073-20.0731707317073
1557772.884.12
1564540.21428571428574.78571428571428
1574540.21428571428574.78571428571428
1585053.0731707317073-3.07317073170731
1597172.88-1.88
1606772.88-5.88
1616253.07317073170738.92682926829269
1625453.07317073170730.926829268292686
163412.25-8.25
1642512.2512.75
1654053.0731707317073-13.0731707317073
1665953.07317073170735.92682926829269
1672412.2511.75
1685853.07317073170734.92682926829269
1694253.0731707317073-11.0731707317073
170412.25-8.25
1716353.07317073170739.92682926829269
1725453.07317073170730.926829268292686
1733953.0731707317073-14.0731707317073


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = kendall ;
Parameters (R input):
par1 = 10 ; par2 = none ; par3 = 2 ; par4 = yes ;
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')
}