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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 computationSat, 17 Dec 2011 13:23:55 -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/17/t13241462884ilutcaby2rjg7z.htm/, Retrieved Fri, 26 Apr 2024 15:35:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156544, Retrieved Fri, 26 Apr 2024 15:35:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact53

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD    [Recursive Partitioning (Regression Trees)] [] [2011-12-17 18:23:55] [43f1c1fe5c2aaa4d7bd6f731e1a494da] [Current]
-   P       [Recursive Partitioning (Regression Trees)] [] [2011-12-18 11:35:05] [74be16979710d4c4e7c6647856088456]
-   P         [Recursive Partitioning (Regression Trees)] [] [2011-12-18 14:40:09] [1dc3906a3b5a6ec06dc921f387100c9e]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1724	270018	69	476	90	3	96	38	144	116	140824	32033	186099	165	165
1209	179444	64	429	63	4	71	34	133	127	110459	20654	113854	135	132
1844	222373	69	673	59	16	70	42	162	106	105079	16346	99776	121	121
2683	218443	104	1137	135	2	134	38	148	133	112098	35926	106194	148	145
1149	162874	51	348	48	1	61	27	88	64	43929	10621	100792	73	71
631	70849	28	179	46	3	8	35	129	89	76173	10024	47552	49	47
4513	498732	117	2201	109	0	149	33	128	122	187326	43068	250931	185	177
381	33186	19	111	46	0	1	18	67	22	22807	1271	6853	5	5
1997	207822	57	735	75	7	74	34	132	117	144408	34416	115466	125	124
1758	213274	44	595	72	0	82	33	120	82	66485	20318	110896	93	92
2064	296074	107	776	79	0	92	43	158	139	79089	24409	169351	154	149
2128	237633	114	660	61	7	117	55	210	184	81625	20648	94853	98	93
1659	164107	67	633	60	8	50	37	122	113	68788	12347	72591	70	70
2934	358752	77	1163	114	4	139	52	179	162	103297	21857	101345	148	148
1944	222781	83	622	46	10	73	43	162	87	69446	11034	113713	100	100
4763	369889	176	1650	127	0	168	59	223	199	114948	33433	165354	150	142
2122	305704	68	746	58	6	113	36	140	139	167949	35902	164263	197	194
2956	322896	157	1157	90	4	98	39	144	92	125081	22355	135213	114	113
1438	176082	55	507	41	3	103	29	111	85	125818	31219	111669	169	162
2320	263411	85	683	62	8	135	49	191	185	136588	21983	134163	200	186
2471	271965	69	828	99	0	123	45	171	148	112431	40085	140303	148	147
2769	425544	103	1203	101	1	86	39	144	144	103037	18507	150773	140	137
1442	179306	41	461	62	5	66	25	89	84	82317	16278	111848	74	71
1717	189897	54	601	65	9	103	52	208	208	118906	24662	102509	128	123
3202	219420	114	1196	150	1	138	41	153	144	83515	31452	96785	140	134
2592	207280	115	960	72	0	86	38	146	139	104581	32580	116136	116	115
2824	267198	126	1061	91	5	99	41	158	127	103129	22883	158376	147	138
1968	270750	85	617	73	0	117	43	154	148	83243	27652	153990	132	125
1495	155915	51	559	53	0	57	32	117	99	37110	9845	64057	70	66
2733	327474	64	1026	140	0	125	41	158	135	113344	20190	230054	144	137
2272	278019	74	908	49	3	123	45	175	165	139165	46201	184531	155	152
1830	204039	76	615	83	6	44	49	182	146	86652	10971	114198	165	159
2090	318563	83	779	53	1	133	48	185	178	112302	34811	198299	161	159
945	97717	36	310	40	4	43	37	141	137	69652	3029	33750	31	31
3092	369331	93	1198	72	4	132	39	151	148	119442	38941	189723	199	185
2764	273950	56	1186	87	0	83	42	159	127	69867	4958	100826	78	78
3657	422946	119	1317	74	0	112	43	158	148	101629	32344	188355	121	117
1842	215710	82	611	67	2	79	36	139	89	70168	19433	104470	112	109
934	115469	32	276	36	1	33	17	55	46	31081	12558	58391	41	41
3315	336047	190	1174	45	2	124	39	151	143	103925	36524	164808	158	149
3246	324178	79	1490	42	10	123	39	145	122	92622	26041	134097	123	123
1578	166266	65	623	75	9	67	41	148	111	79011	16637	80238	104	103
1735	195153	73	635	82	5	75	36	115	108	93487	28395	133252	94	87
1714	173510	60	470	85	6	68	42	157	126	64520	16747	54518	73	71
2496	153778	82	1022	82	1	50	45	73	45	93473	9105	121850	52	51
5501	455168	151	2068	848	2	101	41	147	131	114360	11941	79367	71	70
918	78800	42	330	57	2	20	26	82	66	33032	7935	56968	21	21
2228	208051	76	648	80	0	101	52	201	180	96125	19499	106314	155	155
3942	334657	116	1342	116	10	142	47	181	165	151911	22938	191889	174	172
2081	175523	54	868	68	3	99	45	164	146	89256	25314	104864	136	133
1816	213060	73	559	48	0	94	40	158	137	95671	28524	160791	128	125
496	24188	24	218	20	0	8	4	12	7	5950	2694	15049	7	7
2531	372238	308	833	81	8	85	44	163	157	149695	20867	191179	165	158
744	65029	17	255	21	5	21	18	67	61	32551	3597	25109	21	21
1161	101097	64	454	70	3	30	14	52	41	31701	5296	45824	35	35
3027	279012	58	1108	125	1	97	37	134	120	100087	32982	129711	137	133
2433	302218	84	642	80	5	122	56	210	208	169707	38975	210012	174	169
3574	323485	179	1079	220	5	115	39	145	137	150491	42721	194679	257	256
2606	339837	138	1046	63	0	148	42	153	150	120192	41455	197680	207	190
2175	252529	83	822	77	12	89	36	139	127	95893	23923	81180	103	100
3937	370483	139	1298	65	10	154	46	178	161	151715	26719	197765	171	171
3161	303406	117	1143	146	12	139	28	101	73	176225	53405	214738	279	267
2670	233632	106	1073	72	11	77	43	169	97	59900	12526	96252	83	80
2610	264889	88	931	59	8	92	42	163	142	104767	26584	124527	130	126
1426	228595	66	557	58	2	52	37	139	125	114799	37062	153242	131	132
1646	216027	65	436	58	0	96	30	116	87	72128	25696	145707	126	121
1855	187965	127	562	54	6	79	35	137	128	143592	24634	113963	158	156
2712	237323	144	824	89	9	85	44	167	148	89626	27269	134904	138	133
2277	232765	78	834	78	2	135	36	135	116	131072	25270	114268	200	199
1675	175699	68	621	62	5	143	28	102	89	126817	24634	94333	104	98
2537	239314	68	865	64	13	99	45	173	154	81351	17828	102204	111	109
893	73566	32	385	39	6	22	23	88	67	22618	3007	23824	26	25
2189	242585	83	716	58	7	78	45	175	171	88977	20065	111563	115	113
1694	187167	52	705	94	2	79	38	133	90	92059	24648	91313	127	126
1948	191920	62	683	61	1	127	38	148	133	81897	21588	89770	140	137
2314	359644	84	982	95	4	140	45	166	143	108146	25217	100125	121	121
2645	341637	90	1056	48	3	130	36	140	133	126372	30927	165278	183	178
1804	206059	106	522	50	6	78	41	154	125	249771	18487	181712	68	63
2250	201783	61	690	58	2	133	38	148	134	71154	18050	80906	112	109
1787	182231	64	644	67	0	83	37	134	110	71571	17696	75881	103	101
1678	153613	46	622	41	1	62	28	109	89	55918	17326	83963	63	61
3843	447353	115	1216	114	0	147	45	175	138	160141	39361	175721	166	157
1369	145943	69	653	45	5	30	26	99	99	38692	9648	68580	38	38
2306	280343	103	656	57	2	117	44	122	92	102812	26759	136323	163	159
870	80953	25	437	31	0	49	8	28	27	56622	7905	55792	59	58
1966	150216	54	822	175	0	52	27	101	77	15986	4527	25157	27	27
1337	156923	57	390	68	6	71	36	132	130	123534	41517	100922	108	108
3727	365370	195	1467	278	1	72	37	143	137	108535	21261	118845	88	83
2616	318651	112	907	91	0	130	57	206	122	93879	36099	170492	92	88
3085	179797	104	1044	72	1	165	45	171	159	144551	39039	81716	170	164
2312	251466	89	786	58	1	76	37	138	85	56750	13841	115750	98	96
2136	254506	75	655	71	3	160	38	141	131	127654	23841	105590	205	192
1808	185890	62	590	86	9	48	31	114	90	65594	8589	92795	96	94
2992	263577	74	1072	89	1	132	36	140	135	59938	15049	82390	107	107
2474	314255	80	947	134	4	73	36	140	132	146975	39038	135599	150	144
1624	189252	36	555	64	3	83	36	140	139	143372	30391	111542	123	123
1606	222504	50	552	72	5	94	35	127	127	168553	39932	162519	176	170
2091	285198	78	771	61	0	158	39	141	104	183500	43840	211381	213	210
3845	368878	147	1263	126	12	151	60	231	229	165986	43146	189944	208	193
3705	397681	108	1415	73	13	165	30	114	106	184923	50099	226168	307	297
2676	287015	134	846	83	8	117	51	198	176	140358	40312	117495	125	125
2295	285330	64	838	85	0	145	41	155	130	149959	32616	195894	208	204
1997	186856	177	640	116	0	73	36	138	59	57224	11338	80684	73	70
602	43287	14	214	43	4	13	19	71	64	43750	7409	19630	49	49
2146	185468	80	716	85	4	89	23	84	36	48029	18213	88634	82	82
2131	219475	139	749	72	0	92	40	151	88	104978	45873	139292	206	205
2549	259692	47	1140	110	0	128	40	155	125	100046	39844	128602	112	111
2649	301614	88	1030	55	0	169	40	150	124	101047	28317	135848	139	135
1110	121726	67	356	44	0	28	30	112	83	197426	24797	178377	60	59
3102	154165	88	906	79	0	116	41	161	127	160902	7471	106330	70	70
1860	306952	67	606	58	4	76	40	149	143	147172	27259	178303	112	108
2295	297982	87	684	70	0	147	45	164	115	109432	23201	116938	142	141
398	23623	11	156	9	0	12	1	0	0	1168	238	5841	11	11
2205	195817	73	779	54	0	146	40	155	103	83248	28830	106020	130	130
530	61857	25	192	25	4	23	11	32	30	25162	3913	24610	31	28
1596	163766	48	457	107	0	83	45	169	119	45724	9935	74151	132	101
2949	384053	114	1162	63	1	135	38	140	102	110529	27738	232241	219	216
387	21054	16	146	2	0	4	0	0	0	855	338	6622	4	4
2137	252805	52	866	67	5	81	30	111	77	101382	13326	127097	102	97
492	31961	22	200	22	0	18	8	25	9	14116	3988	13155	39	39
3397	311281	113	1211	153	3	106	39	146	137	89506	24347	160501	125	119
2089	240153	65	696	79	7	76	48	183	163	135356	27111	91502	121	118
1638	174892	88	485	112	13	55	48	181	146	116066	3938	24469	42	41
1685	152043	53	670	47	3	44	29	107	84	144244	17416	88229	111	107
568	38214	34	276	52	0	16	8	27	21	8773	1888	13983	16	16
1907	198094	40	659	113	2	66	43	163	151	102153	18700	80716	70	69
2758	353021	81	1010	115	0	137	52	198	187	117440	36809	157384	162	160
1288	196269	58	445	64	0	50	53	205	171	104128	24959	122975	173	158
3554	403932	80	1564	134	4	137	48	187	167	134238	37343	191469	171	161
2387	316105	97	820	120	0	154	48	187	145	134047	21849	231257	172	165
3328	396725	123	1151	111	3	137	50	186	175	279488	49809	258287	254	246
1250	187992	35	473	49	0	71	40	151	137	79756	21654	122531	90	89
1121	102424	42	401	55	0	42	36	131	100	66089	8728	61394	50	49
2862	283950	329	947	149	4	84	40	155	150	102070	20920	86480	113	107
4023	401260	164	1429	155	4	103	46	172	163	146760	27195	195791	187	182
1721	137843	52	534	104	15	63	40	152	141	154771	1037	18284	16	16
4060	383703	130	1698	146	0	127	46	172	161	165933	42570	147581	175	173
1830	157429	76	689	76	4	55	39	143	112	64593	17672	72558	90	90
1627	236370	46	528	83	1	104	41	151	135	92280	34245	147341	140	140
2535	282399	94	897	192	1	110	46	158	124	67150	16786	114651	145	142
1807	217478	112	610	69	0	38	32	125	45	128692	20954	100187	141	126
3873	366774	116	1548	117	9	95	39	145	120	124089	16378	130332	125	123
2181	236660	88	759	67	1	121	39	145	126	125386	31852	134218	241	239
2035	173260	63	716	37	3	41	21	79	78	37238	2805	10901	16	15
2960	323545	99	955	56	11	145	45	174	136	140015	38086	145758	175	170
1915	168994	57	720	122	5	147	50	192	179	150047	21166	75767	132	123
2604	246745	86	1011	52	2	116	36	132	118	154451	34672	134969	154	151
2633	301703	105	818	64	1	185	44	159	147	156349	36171	169216	198	194
2	1	0	0	0	9	0	0	0	0	0	0	0	0	0
207	14688	10	85	0	0	4	0	0	0	6023	2065	7953	5	5
5	98	1	0	0	0	0	0	0	0	0	0	0	0	0
8	455	2	0	0	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2030	233143	83	699	58	2	65	37	133	88	84601	19354	105406	125	122
3161	368328	145	1049	115	3	130	47	185	115	68946	22124	174586	174	173
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4	203	4	0	0	0	0	0	0	0	0	0	0	0	0
151	7199	5	74	0	0	7	0	0	0	1644	556	4245	6	6
474	46660	20	259	7	0	12	5	15	13	6179	2089	21509	13	13
141	17547	5	69	3	0	0	1	4	4	3926	2658	7670	3	3
1047	116678	42	285	89	0	37	43	152	76	52789	1813	15673	35	35
29	969	2	0	0	0	0	0	0	0	0	0	0	0	0
1757	199726	65	580	47	2	48	32	117	66	100350	17372	75882	80	72

Tables (Output of Computation)





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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156544&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156544&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156544&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Goodness of Fit
Correlation0.8691
R-squared0.7553
RMSE25651.0533

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8691 \tabularnewline
R-squared & 0.7553 \tabularnewline
RMSE & 25651.0533 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156544&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8691[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7553[/C][/ROW]
[ROW][C]RMSE[/C][C]25651.0533[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156544&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156544&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
Correlation0.8691
R-squared0.7553
RMSE25651.0533






Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1140824156589.185185185-15765.1851851852
2110459108419.5471698112039.45283018867
310507983722.757575757621356.2424242424
4112098108419.5471698113678.45283018867
54392983722.7575757576-39793.7575757576
67617372925.27272727273247.72727272728
7187326156589.18518518530736.8148148148
8228073034.519772.5
9144408108419.54716981135988.4528301887
106648583722.7575757576-17237.7575757576
1179089108419.547169811-29330.5471698113
128162583722.7575757576-2097.75757575757
136878872925.2727272727-4137.27272727272
14103297108419.547169811-5122.54716981133
156944683722.7575757576-14276.7575757576
16114948108419.5471698116528.45283018867
17167949140986.27272727326962.7272727273
18125081108419.54716981116661.4528301887
19125818108419.54716981117398.4528301887
20136588140986.272727273-4398.27272727274
21112431108419.5471698114011.45283018867
2210303783722.757575757619314.2424242424
238231783722.7575757576-1405.75757575757
24118906108419.54716981110486.4528301887
2583515108419.547169811-24904.5471698113
26104581108419.547169811-3838.54716981133
27103129108419.547169811-5290.54716981133
2883243108419.547169811-25176.5471698113
293711072925.2727272727-35815.2727272727
30113344156589.185185185-43245.1851851852
31139165156589.185185185-17424.1851851852
328665283722.75757575762929.24242424243
33112302156589.185185185-44287.1851851852
346965272925.2727272727-3273.27272727272
35119442156589.185185185-37147.1851851852
366986783722.7575757576-13855.7575757576
37101629156589.185185185-54960.1851851852
387016883722.7575757576-13554.7575757576
393108131186.0769230769-105.076923076922
40103925108419.547169811-4494.54716981133
4192622108419.547169811-15797.5471698113
427901183722.7575757576-4711.75757575757
4393487108419.547169811-14932.5471698113
446452072925.2727272727-8405.27272727272
459347383722.75757575769750.24242424243
4611436083722.757575757630637.2424242424
473303231186.07692307691845.92307692308
489612583722.757575757612402.2424242424
49151911156589.185185185-4678.1851851852
5089256108419.547169811-19163.5471698113
5195671108419.547169811-12748.5471698113
5259503034.52915.5
53149695156589.185185185-6894.1851851852
543255131186.07692307691364.92307692308
553170131186.0769230769514.923076923078
56100087108419.547169811-8332.54716981133
57169707156589.18518518513117.8148148148
58150491156589.185185185-6098.1851851852
59120192156589.185185185-36397.1851851852
6095893108419.547169811-12526.5471698113
61151715156589.185185185-4874.1851851852
62176225156589.18518518519635.8148148148
635990083722.7575757576-23822.7575757576
64104767108419.547169811-3652.54716981133
65114799108419.5471698116379.45283018867
6672128108419.547169811-36291.5471698113
67143592108419.54716981135172.4528301887
6889626108419.547169811-18793.5471698113
69131072140986.272727273-9914.27272727274
70126817108419.54716981118397.4528301887
718135183722.7575757576-2371.75757575757
722261831186.0769230769-8568.07692307692
738897783722.75757575765254.24242424243
7492059108419.547169811-16360.5471698113
7581897108419.547169811-26522.5471698113
76108146108419.547169811-273.547169811325
77126372140986.272727273-14614.2727272727
78249771156589.18518518593181.8148148148
797115483722.7575757576-12568.7575757576
807157183722.7575757576-12151.7575757576
815591883722.7575757576-27804.7575757576
82160141156589.1851851853551.8148148148
833869272925.2727272727-34233.2727272727
84102812108419.547169811-5607.54716981133
855662231186.076923076925435.9230769231
861598631186.0769230769-15200.0769230769
87123534108419.54716981115114.4528301887
88108535108419.547169811115.452830188675
8993879108419.547169811-14540.5471698113
90144551108419.54716981136131.4528301887
915675083722.7575757576-26972.7575757576
92127654140986.272727273-13332.2727272727
936559483722.7575757576-18128.7575757576
945993883722.7575757576-23784.7575757576
95146975108419.54716981138555.4528301887
96143372108419.54716981134952.4528301887
97168553140986.27272727327566.7272727273
98183500156589.18518518526910.8148148148
99165986156589.1851851859396.8148148148
100184923156589.18518518528333.8148148148
101140358108419.54716981131938.4528301887
102149959156589.185185185-6630.1851851852
1035722483722.7575757576-26498.7575757576
1044375031186.076923076912563.9230769231
1054802983722.7575757576-35693.7575757576
106104978140986.272727273-36008.2727272727
107100046108419.547169811-8373.54716981133
108101047108419.547169811-7372.54716981133
109197426156589.18518518540836.8148148148
11016090283722.757575757677179.2424242424
111147172156589.185185185-9417.1851851852
112109432108419.5471698111012.45283018867
11311683034.5-1866.5
11483248108419.547169811-25171.5471698113
1152516231186.0769230769-6024.07692307692
1164572472925.2727272727-27201.2727272727
117110529156589.185185185-46060.1851851852
1188553034.5-2179.5
11910138283722.757575757617659.2424242424
1201411631186.0769230769-17070.0769230769
12189506108419.547169811-18913.5471698113
122135356108419.54716981126936.4528301887
12311606672925.272727272743140.7272727273
12414424483722.757575757660521.2424242424
125877331186.0769230769-22413.0769230769
12610215383722.757575757618430.2424242424
127117440108419.5471698119020.45283018867
128104128108419.547169811-4291.54716981133
129134238156589.185185185-22351.1851851852
130134047156589.185185185-22542.1851851852
131279488156589.185185185122898.814814815
13279756108419.547169811-28663.5471698113
1336608972925.2727272727-6836.27272727272
134102070108419.547169811-6349.54716981133
135146760156589.185185185-9829.1851851852
13615477172925.272727272781845.7272727273
137165933140986.27272727324946.7272727273
1386459372925.2727272727-8332.27272727272
13992280108419.547169811-16139.5471698113
1406715083722.7575757576-16572.7575757576
141128692108419.54716981120272.4528301887
14212408983722.757575757640366.2424242424
143125386140986.272727273-15600.2727272727
1443723831186.07692307696051.92307692308
145140015140986.272727273-971.272727272735
146150047108419.54716981141627.4528301887
147154451108419.54716981146031.4528301887
148156349140986.27272727315362.7272727273
14903034.5-3034.5
15060233034.52988.5
15103034.5-3034.5
15203034.5-3034.5
15303034.5-3034.5
15403034.5-3034.5
1558460183722.7575757576878.242424242431
15668946108419.547169811-39473.5471698113
15703034.5-3034.5
15803034.5-3034.5
15916443034.5-1390.5
16061793034.53144.5
16139263034.5891.5
1625278931186.076923076921602.9230769231
16303034.5-3034.5
16410035083722.757575757616627.2424242424

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 140824 & 156589.185185185 & -15765.1851851852 \tabularnewline
2 & 110459 & 108419.547169811 & 2039.45283018867 \tabularnewline
3 & 105079 & 83722.7575757576 & 21356.2424242424 \tabularnewline
4 & 112098 & 108419.547169811 & 3678.45283018867 \tabularnewline
5 & 43929 & 83722.7575757576 & -39793.7575757576 \tabularnewline
6 & 76173 & 72925.2727272727 & 3247.72727272728 \tabularnewline
7 & 187326 & 156589.185185185 & 30736.8148148148 \tabularnewline
8 & 22807 & 3034.5 & 19772.5 \tabularnewline
9 & 144408 & 108419.547169811 & 35988.4528301887 \tabularnewline
10 & 66485 & 83722.7575757576 & -17237.7575757576 \tabularnewline
11 & 79089 & 108419.547169811 & -29330.5471698113 \tabularnewline
12 & 81625 & 83722.7575757576 & -2097.75757575757 \tabularnewline
13 & 68788 & 72925.2727272727 & -4137.27272727272 \tabularnewline
14 & 103297 & 108419.547169811 & -5122.54716981133 \tabularnewline
15 & 69446 & 83722.7575757576 & -14276.7575757576 \tabularnewline
16 & 114948 & 108419.547169811 & 6528.45283018867 \tabularnewline
17 & 167949 & 140986.272727273 & 26962.7272727273 \tabularnewline
18 & 125081 & 108419.547169811 & 16661.4528301887 \tabularnewline
19 & 125818 & 108419.547169811 & 17398.4528301887 \tabularnewline
20 & 136588 & 140986.272727273 & -4398.27272727274 \tabularnewline
21 & 112431 & 108419.547169811 & 4011.45283018867 \tabularnewline
22 & 103037 & 83722.7575757576 & 19314.2424242424 \tabularnewline
23 & 82317 & 83722.7575757576 & -1405.75757575757 \tabularnewline
24 & 118906 & 108419.547169811 & 10486.4528301887 \tabularnewline
25 & 83515 & 108419.547169811 & -24904.5471698113 \tabularnewline
26 & 104581 & 108419.547169811 & -3838.54716981133 \tabularnewline
27 & 103129 & 108419.547169811 & -5290.54716981133 \tabularnewline
28 & 83243 & 108419.547169811 & -25176.5471698113 \tabularnewline
29 & 37110 & 72925.2727272727 & -35815.2727272727 \tabularnewline
30 & 113344 & 156589.185185185 & -43245.1851851852 \tabularnewline
31 & 139165 & 156589.185185185 & -17424.1851851852 \tabularnewline
32 & 86652 & 83722.7575757576 & 2929.24242424243 \tabularnewline
33 & 112302 & 156589.185185185 & -44287.1851851852 \tabularnewline
34 & 69652 & 72925.2727272727 & -3273.27272727272 \tabularnewline
35 & 119442 & 156589.185185185 & -37147.1851851852 \tabularnewline
36 & 69867 & 83722.7575757576 & -13855.7575757576 \tabularnewline
37 & 101629 & 156589.185185185 & -54960.1851851852 \tabularnewline
38 & 70168 & 83722.7575757576 & -13554.7575757576 \tabularnewline
39 & 31081 & 31186.0769230769 & -105.076923076922 \tabularnewline
40 & 103925 & 108419.547169811 & -4494.54716981133 \tabularnewline
41 & 92622 & 108419.547169811 & -15797.5471698113 \tabularnewline
42 & 79011 & 83722.7575757576 & -4711.75757575757 \tabularnewline
43 & 93487 & 108419.547169811 & -14932.5471698113 \tabularnewline
44 & 64520 & 72925.2727272727 & -8405.27272727272 \tabularnewline
45 & 93473 & 83722.7575757576 & 9750.24242424243 \tabularnewline
46 & 114360 & 83722.7575757576 & 30637.2424242424 \tabularnewline
47 & 33032 & 31186.0769230769 & 1845.92307692308 \tabularnewline
48 & 96125 & 83722.7575757576 & 12402.2424242424 \tabularnewline
49 & 151911 & 156589.185185185 & -4678.1851851852 \tabularnewline
50 & 89256 & 108419.547169811 & -19163.5471698113 \tabularnewline
51 & 95671 & 108419.547169811 & -12748.5471698113 \tabularnewline
52 & 5950 & 3034.5 & 2915.5 \tabularnewline
53 & 149695 & 156589.185185185 & -6894.1851851852 \tabularnewline
54 & 32551 & 31186.0769230769 & 1364.92307692308 \tabularnewline
55 & 31701 & 31186.0769230769 & 514.923076923078 \tabularnewline
56 & 100087 & 108419.547169811 & -8332.54716981133 \tabularnewline
57 & 169707 & 156589.185185185 & 13117.8148148148 \tabularnewline
58 & 150491 & 156589.185185185 & -6098.1851851852 \tabularnewline
59 & 120192 & 156589.185185185 & -36397.1851851852 \tabularnewline
60 & 95893 & 108419.547169811 & -12526.5471698113 \tabularnewline
61 & 151715 & 156589.185185185 & -4874.1851851852 \tabularnewline
62 & 176225 & 156589.185185185 & 19635.8148148148 \tabularnewline
63 & 59900 & 83722.7575757576 & -23822.7575757576 \tabularnewline
64 & 104767 & 108419.547169811 & -3652.54716981133 \tabularnewline
65 & 114799 & 108419.547169811 & 6379.45283018867 \tabularnewline
66 & 72128 & 108419.547169811 & -36291.5471698113 \tabularnewline
67 & 143592 & 108419.547169811 & 35172.4528301887 \tabularnewline
68 & 89626 & 108419.547169811 & -18793.5471698113 \tabularnewline
69 & 131072 & 140986.272727273 & -9914.27272727274 \tabularnewline
70 & 126817 & 108419.547169811 & 18397.4528301887 \tabularnewline
71 & 81351 & 83722.7575757576 & -2371.75757575757 \tabularnewline
72 & 22618 & 31186.0769230769 & -8568.07692307692 \tabularnewline
73 & 88977 & 83722.7575757576 & 5254.24242424243 \tabularnewline
74 & 92059 & 108419.547169811 & -16360.5471698113 \tabularnewline
75 & 81897 & 108419.547169811 & -26522.5471698113 \tabularnewline
76 & 108146 & 108419.547169811 & -273.547169811325 \tabularnewline
77 & 126372 & 140986.272727273 & -14614.2727272727 \tabularnewline
78 & 249771 & 156589.185185185 & 93181.8148148148 \tabularnewline
79 & 71154 & 83722.7575757576 & -12568.7575757576 \tabularnewline
80 & 71571 & 83722.7575757576 & -12151.7575757576 \tabularnewline
81 & 55918 & 83722.7575757576 & -27804.7575757576 \tabularnewline
82 & 160141 & 156589.185185185 & 3551.8148148148 \tabularnewline
83 & 38692 & 72925.2727272727 & -34233.2727272727 \tabularnewline
84 & 102812 & 108419.547169811 & -5607.54716981133 \tabularnewline
85 & 56622 & 31186.0769230769 & 25435.9230769231 \tabularnewline
86 & 15986 & 31186.0769230769 & -15200.0769230769 \tabularnewline
87 & 123534 & 108419.547169811 & 15114.4528301887 \tabularnewline
88 & 108535 & 108419.547169811 & 115.452830188675 \tabularnewline
89 & 93879 & 108419.547169811 & -14540.5471698113 \tabularnewline
90 & 144551 & 108419.547169811 & 36131.4528301887 \tabularnewline
91 & 56750 & 83722.7575757576 & -26972.7575757576 \tabularnewline
92 & 127654 & 140986.272727273 & -13332.2727272727 \tabularnewline
93 & 65594 & 83722.7575757576 & -18128.7575757576 \tabularnewline
94 & 59938 & 83722.7575757576 & -23784.7575757576 \tabularnewline
95 & 146975 & 108419.547169811 & 38555.4528301887 \tabularnewline
96 & 143372 & 108419.547169811 & 34952.4528301887 \tabularnewline
97 & 168553 & 140986.272727273 & 27566.7272727273 \tabularnewline
98 & 183500 & 156589.185185185 & 26910.8148148148 \tabularnewline
99 & 165986 & 156589.185185185 & 9396.8148148148 \tabularnewline
100 & 184923 & 156589.185185185 & 28333.8148148148 \tabularnewline
101 & 140358 & 108419.547169811 & 31938.4528301887 \tabularnewline
102 & 149959 & 156589.185185185 & -6630.1851851852 \tabularnewline
103 & 57224 & 83722.7575757576 & -26498.7575757576 \tabularnewline
104 & 43750 & 31186.0769230769 & 12563.9230769231 \tabularnewline
105 & 48029 & 83722.7575757576 & -35693.7575757576 \tabularnewline
106 & 104978 & 140986.272727273 & -36008.2727272727 \tabularnewline
107 & 100046 & 108419.547169811 & -8373.54716981133 \tabularnewline
108 & 101047 & 108419.547169811 & -7372.54716981133 \tabularnewline
109 & 197426 & 156589.185185185 & 40836.8148148148 \tabularnewline
110 & 160902 & 83722.7575757576 & 77179.2424242424 \tabularnewline
111 & 147172 & 156589.185185185 & -9417.1851851852 \tabularnewline
112 & 109432 & 108419.547169811 & 1012.45283018867 \tabularnewline
113 & 1168 & 3034.5 & -1866.5 \tabularnewline
114 & 83248 & 108419.547169811 & -25171.5471698113 \tabularnewline
115 & 25162 & 31186.0769230769 & -6024.07692307692 \tabularnewline
116 & 45724 & 72925.2727272727 & -27201.2727272727 \tabularnewline
117 & 110529 & 156589.185185185 & -46060.1851851852 \tabularnewline
118 & 855 & 3034.5 & -2179.5 \tabularnewline
119 & 101382 & 83722.7575757576 & 17659.2424242424 \tabularnewline
120 & 14116 & 31186.0769230769 & -17070.0769230769 \tabularnewline
121 & 89506 & 108419.547169811 & -18913.5471698113 \tabularnewline
122 & 135356 & 108419.547169811 & 26936.4528301887 \tabularnewline
123 & 116066 & 72925.2727272727 & 43140.7272727273 \tabularnewline
124 & 144244 & 83722.7575757576 & 60521.2424242424 \tabularnewline
125 & 8773 & 31186.0769230769 & -22413.0769230769 \tabularnewline
126 & 102153 & 83722.7575757576 & 18430.2424242424 \tabularnewline
127 & 117440 & 108419.547169811 & 9020.45283018867 \tabularnewline
128 & 104128 & 108419.547169811 & -4291.54716981133 \tabularnewline
129 & 134238 & 156589.185185185 & -22351.1851851852 \tabularnewline
130 & 134047 & 156589.185185185 & -22542.1851851852 \tabularnewline
131 & 279488 & 156589.185185185 & 122898.814814815 \tabularnewline
132 & 79756 & 108419.547169811 & -28663.5471698113 \tabularnewline
133 & 66089 & 72925.2727272727 & -6836.27272727272 \tabularnewline
134 & 102070 & 108419.547169811 & -6349.54716981133 \tabularnewline
135 & 146760 & 156589.185185185 & -9829.1851851852 \tabularnewline
136 & 154771 & 72925.2727272727 & 81845.7272727273 \tabularnewline
137 & 165933 & 140986.272727273 & 24946.7272727273 \tabularnewline
138 & 64593 & 72925.2727272727 & -8332.27272727272 \tabularnewline
139 & 92280 & 108419.547169811 & -16139.5471698113 \tabularnewline
140 & 67150 & 83722.7575757576 & -16572.7575757576 \tabularnewline
141 & 128692 & 108419.547169811 & 20272.4528301887 \tabularnewline
142 & 124089 & 83722.7575757576 & 40366.2424242424 \tabularnewline
143 & 125386 & 140986.272727273 & -15600.2727272727 \tabularnewline
144 & 37238 & 31186.0769230769 & 6051.92307692308 \tabularnewline
145 & 140015 & 140986.272727273 & -971.272727272735 \tabularnewline
146 & 150047 & 108419.547169811 & 41627.4528301887 \tabularnewline
147 & 154451 & 108419.547169811 & 46031.4528301887 \tabularnewline
148 & 156349 & 140986.272727273 & 15362.7272727273 \tabularnewline
149 & 0 & 3034.5 & -3034.5 \tabularnewline
150 & 6023 & 3034.5 & 2988.5 \tabularnewline
151 & 0 & 3034.5 & -3034.5 \tabularnewline
152 & 0 & 3034.5 & -3034.5 \tabularnewline
153 & 0 & 3034.5 & -3034.5 \tabularnewline
154 & 0 & 3034.5 & -3034.5 \tabularnewline
155 & 84601 & 83722.7575757576 & 878.242424242431 \tabularnewline
156 & 68946 & 108419.547169811 & -39473.5471698113 \tabularnewline
157 & 0 & 3034.5 & -3034.5 \tabularnewline
158 & 0 & 3034.5 & -3034.5 \tabularnewline
159 & 1644 & 3034.5 & -1390.5 \tabularnewline
160 & 6179 & 3034.5 & 3144.5 \tabularnewline
161 & 3926 & 3034.5 & 891.5 \tabularnewline
162 & 52789 & 31186.0769230769 & 21602.9230769231 \tabularnewline
163 & 0 & 3034.5 & -3034.5 \tabularnewline
164 & 100350 & 83722.7575757576 & 16627.2424242424 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156544&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]140824[/C][C]156589.185185185[/C][C]-15765.1851851852[/C][/ROW]
[ROW][C]2[/C][C]110459[/C][C]108419.547169811[/C][C]2039.45283018867[/C][/ROW]
[ROW][C]3[/C][C]105079[/C][C]83722.7575757576[/C][C]21356.2424242424[/C][/ROW]
[ROW][C]4[/C][C]112098[/C][C]108419.547169811[/C][C]3678.45283018867[/C][/ROW]
[ROW][C]5[/C][C]43929[/C][C]83722.7575757576[/C][C]-39793.7575757576[/C][/ROW]
[ROW][C]6[/C][C]76173[/C][C]72925.2727272727[/C][C]3247.72727272728[/C][/ROW]
[ROW][C]7[/C][C]187326[/C][C]156589.185185185[/C][C]30736.8148148148[/C][/ROW]
[ROW][C]8[/C][C]22807[/C][C]3034.5[/C][C]19772.5[/C][/ROW]
[ROW][C]9[/C][C]144408[/C][C]108419.547169811[/C][C]35988.4528301887[/C][/ROW]
[ROW][C]10[/C][C]66485[/C][C]83722.7575757576[/C][C]-17237.7575757576[/C][/ROW]
[ROW][C]11[/C][C]79089[/C][C]108419.547169811[/C][C]-29330.5471698113[/C][/ROW]
[ROW][C]12[/C][C]81625[/C][C]83722.7575757576[/C][C]-2097.75757575757[/C][/ROW]
[ROW][C]13[/C][C]68788[/C][C]72925.2727272727[/C][C]-4137.27272727272[/C][/ROW]
[ROW][C]14[/C][C]103297[/C][C]108419.547169811[/C][C]-5122.54716981133[/C][/ROW]
[ROW][C]15[/C][C]69446[/C][C]83722.7575757576[/C][C]-14276.7575757576[/C][/ROW]
[ROW][C]16[/C][C]114948[/C][C]108419.547169811[/C][C]6528.45283018867[/C][/ROW]
[ROW][C]17[/C][C]167949[/C][C]140986.272727273[/C][C]26962.7272727273[/C][/ROW]
[ROW][C]18[/C][C]125081[/C][C]108419.547169811[/C][C]16661.4528301887[/C][/ROW]
[ROW][C]19[/C][C]125818[/C][C]108419.547169811[/C][C]17398.4528301887[/C][/ROW]
[ROW][C]20[/C][C]136588[/C][C]140986.272727273[/C][C]-4398.27272727274[/C][/ROW]
[ROW][C]21[/C][C]112431[/C][C]108419.547169811[/C][C]4011.45283018867[/C][/ROW]
[ROW][C]22[/C][C]103037[/C][C]83722.7575757576[/C][C]19314.2424242424[/C][/ROW]
[ROW][C]23[/C][C]82317[/C][C]83722.7575757576[/C][C]-1405.75757575757[/C][/ROW]
[ROW][C]24[/C][C]118906[/C][C]108419.547169811[/C][C]10486.4528301887[/C][/ROW]
[ROW][C]25[/C][C]83515[/C][C]108419.547169811[/C][C]-24904.5471698113[/C][/ROW]
[ROW][C]26[/C][C]104581[/C][C]108419.547169811[/C][C]-3838.54716981133[/C][/ROW]
[ROW][C]27[/C][C]103129[/C][C]108419.547169811[/C][C]-5290.54716981133[/C][/ROW]
[ROW][C]28[/C][C]83243[/C][C]108419.547169811[/C][C]-25176.5471698113[/C][/ROW]
[ROW][C]29[/C][C]37110[/C][C]72925.2727272727[/C][C]-35815.2727272727[/C][/ROW]
[ROW][C]30[/C][C]113344[/C][C]156589.185185185[/C][C]-43245.1851851852[/C][/ROW]
[ROW][C]31[/C][C]139165[/C][C]156589.185185185[/C][C]-17424.1851851852[/C][/ROW]
[ROW][C]32[/C][C]86652[/C][C]83722.7575757576[/C][C]2929.24242424243[/C][/ROW]
[ROW][C]33[/C][C]112302[/C][C]156589.185185185[/C][C]-44287.1851851852[/C][/ROW]
[ROW][C]34[/C][C]69652[/C][C]72925.2727272727[/C][C]-3273.27272727272[/C][/ROW]
[ROW][C]35[/C][C]119442[/C][C]156589.185185185[/C][C]-37147.1851851852[/C][/ROW]
[ROW][C]36[/C][C]69867[/C][C]83722.7575757576[/C][C]-13855.7575757576[/C][/ROW]
[ROW][C]37[/C][C]101629[/C][C]156589.185185185[/C][C]-54960.1851851852[/C][/ROW]
[ROW][C]38[/C][C]70168[/C][C]83722.7575757576[/C][C]-13554.7575757576[/C][/ROW]
[ROW][C]39[/C][C]31081[/C][C]31186.0769230769[/C][C]-105.076923076922[/C][/ROW]
[ROW][C]40[/C][C]103925[/C][C]108419.547169811[/C][C]-4494.54716981133[/C][/ROW]
[ROW][C]41[/C][C]92622[/C][C]108419.547169811[/C][C]-15797.5471698113[/C][/ROW]
[ROW][C]42[/C][C]79011[/C][C]83722.7575757576[/C][C]-4711.75757575757[/C][/ROW]
[ROW][C]43[/C][C]93487[/C][C]108419.547169811[/C][C]-14932.5471698113[/C][/ROW]
[ROW][C]44[/C][C]64520[/C][C]72925.2727272727[/C][C]-8405.27272727272[/C][/ROW]
[ROW][C]45[/C][C]93473[/C][C]83722.7575757576[/C][C]9750.24242424243[/C][/ROW]
[ROW][C]46[/C][C]114360[/C][C]83722.7575757576[/C][C]30637.2424242424[/C][/ROW]
[ROW][C]47[/C][C]33032[/C][C]31186.0769230769[/C][C]1845.92307692308[/C][/ROW]
[ROW][C]48[/C][C]96125[/C][C]83722.7575757576[/C][C]12402.2424242424[/C][/ROW]
[ROW][C]49[/C][C]151911[/C][C]156589.185185185[/C][C]-4678.1851851852[/C][/ROW]
[ROW][C]50[/C][C]89256[/C][C]108419.547169811[/C][C]-19163.5471698113[/C][/ROW]
[ROW][C]51[/C][C]95671[/C][C]108419.547169811[/C][C]-12748.5471698113[/C][/ROW]
[ROW][C]52[/C][C]5950[/C][C]3034.5[/C][C]2915.5[/C][/ROW]
[ROW][C]53[/C][C]149695[/C][C]156589.185185185[/C][C]-6894.1851851852[/C][/ROW]
[ROW][C]54[/C][C]32551[/C][C]31186.0769230769[/C][C]1364.92307692308[/C][/ROW]
[ROW][C]55[/C][C]31701[/C][C]31186.0769230769[/C][C]514.923076923078[/C][/ROW]
[ROW][C]56[/C][C]100087[/C][C]108419.547169811[/C][C]-8332.54716981133[/C][/ROW]
[ROW][C]57[/C][C]169707[/C][C]156589.185185185[/C][C]13117.8148148148[/C][/ROW]
[ROW][C]58[/C][C]150491[/C][C]156589.185185185[/C][C]-6098.1851851852[/C][/ROW]
[ROW][C]59[/C][C]120192[/C][C]156589.185185185[/C][C]-36397.1851851852[/C][/ROW]
[ROW][C]60[/C][C]95893[/C][C]108419.547169811[/C][C]-12526.5471698113[/C][/ROW]
[ROW][C]61[/C][C]151715[/C][C]156589.185185185[/C][C]-4874.1851851852[/C][/ROW]
[ROW][C]62[/C][C]176225[/C][C]156589.185185185[/C][C]19635.8148148148[/C][/ROW]
[ROW][C]63[/C][C]59900[/C][C]83722.7575757576[/C][C]-23822.7575757576[/C][/ROW]
[ROW][C]64[/C][C]104767[/C][C]108419.547169811[/C][C]-3652.54716981133[/C][/ROW]
[ROW][C]65[/C][C]114799[/C][C]108419.547169811[/C][C]6379.45283018867[/C][/ROW]
[ROW][C]66[/C][C]72128[/C][C]108419.547169811[/C][C]-36291.5471698113[/C][/ROW]
[ROW][C]67[/C][C]143592[/C][C]108419.547169811[/C][C]35172.4528301887[/C][/ROW]
[ROW][C]68[/C][C]89626[/C][C]108419.547169811[/C][C]-18793.5471698113[/C][/ROW]
[ROW][C]69[/C][C]131072[/C][C]140986.272727273[/C][C]-9914.27272727274[/C][/ROW]
[ROW][C]70[/C][C]126817[/C][C]108419.547169811[/C][C]18397.4528301887[/C][/ROW]
[ROW][C]71[/C][C]81351[/C][C]83722.7575757576[/C][C]-2371.75757575757[/C][/ROW]
[ROW][C]72[/C][C]22618[/C][C]31186.0769230769[/C][C]-8568.07692307692[/C][/ROW]
[ROW][C]73[/C][C]88977[/C][C]83722.7575757576[/C][C]5254.24242424243[/C][/ROW]
[ROW][C]74[/C][C]92059[/C][C]108419.547169811[/C][C]-16360.5471698113[/C][/ROW]
[ROW][C]75[/C][C]81897[/C][C]108419.547169811[/C][C]-26522.5471698113[/C][/ROW]
[ROW][C]76[/C][C]108146[/C][C]108419.547169811[/C][C]-273.547169811325[/C][/ROW]
[ROW][C]77[/C][C]126372[/C][C]140986.272727273[/C][C]-14614.2727272727[/C][/ROW]
[ROW][C]78[/C][C]249771[/C][C]156589.185185185[/C][C]93181.8148148148[/C][/ROW]
[ROW][C]79[/C][C]71154[/C][C]83722.7575757576[/C][C]-12568.7575757576[/C][/ROW]
[ROW][C]80[/C][C]71571[/C][C]83722.7575757576[/C][C]-12151.7575757576[/C][/ROW]
[ROW][C]81[/C][C]55918[/C][C]83722.7575757576[/C][C]-27804.7575757576[/C][/ROW]
[ROW][C]82[/C][C]160141[/C][C]156589.185185185[/C][C]3551.8148148148[/C][/ROW]
[ROW][C]83[/C][C]38692[/C][C]72925.2727272727[/C][C]-34233.2727272727[/C][/ROW]
[ROW][C]84[/C][C]102812[/C][C]108419.547169811[/C][C]-5607.54716981133[/C][/ROW]
[ROW][C]85[/C][C]56622[/C][C]31186.0769230769[/C][C]25435.9230769231[/C][/ROW]
[ROW][C]86[/C][C]15986[/C][C]31186.0769230769[/C][C]-15200.0769230769[/C][/ROW]
[ROW][C]87[/C][C]123534[/C][C]108419.547169811[/C][C]15114.4528301887[/C][/ROW]
[ROW][C]88[/C][C]108535[/C][C]108419.547169811[/C][C]115.452830188675[/C][/ROW]
[ROW][C]89[/C][C]93879[/C][C]108419.547169811[/C][C]-14540.5471698113[/C][/ROW]
[ROW][C]90[/C][C]144551[/C][C]108419.547169811[/C][C]36131.4528301887[/C][/ROW]
[ROW][C]91[/C][C]56750[/C][C]83722.7575757576[/C][C]-26972.7575757576[/C][/ROW]
[ROW][C]92[/C][C]127654[/C][C]140986.272727273[/C][C]-13332.2727272727[/C][/ROW]
[ROW][C]93[/C][C]65594[/C][C]83722.7575757576[/C][C]-18128.7575757576[/C][/ROW]
[ROW][C]94[/C][C]59938[/C][C]83722.7575757576[/C][C]-23784.7575757576[/C][/ROW]
[ROW][C]95[/C][C]146975[/C][C]108419.547169811[/C][C]38555.4528301887[/C][/ROW]
[ROW][C]96[/C][C]143372[/C][C]108419.547169811[/C][C]34952.4528301887[/C][/ROW]
[ROW][C]97[/C][C]168553[/C][C]140986.272727273[/C][C]27566.7272727273[/C][/ROW]
[ROW][C]98[/C][C]183500[/C][C]156589.185185185[/C][C]26910.8148148148[/C][/ROW]
[ROW][C]99[/C][C]165986[/C][C]156589.185185185[/C][C]9396.8148148148[/C][/ROW]
[ROW][C]100[/C][C]184923[/C][C]156589.185185185[/C][C]28333.8148148148[/C][/ROW]
[ROW][C]101[/C][C]140358[/C][C]108419.547169811[/C][C]31938.4528301887[/C][/ROW]
[ROW][C]102[/C][C]149959[/C][C]156589.185185185[/C][C]-6630.1851851852[/C][/ROW]
[ROW][C]103[/C][C]57224[/C][C]83722.7575757576[/C][C]-26498.7575757576[/C][/ROW]
[ROW][C]104[/C][C]43750[/C][C]31186.0769230769[/C][C]12563.9230769231[/C][/ROW]
[ROW][C]105[/C][C]48029[/C][C]83722.7575757576[/C][C]-35693.7575757576[/C][/ROW]
[ROW][C]106[/C][C]104978[/C][C]140986.272727273[/C][C]-36008.2727272727[/C][/ROW]
[ROW][C]107[/C][C]100046[/C][C]108419.547169811[/C][C]-8373.54716981133[/C][/ROW]
[ROW][C]108[/C][C]101047[/C][C]108419.547169811[/C][C]-7372.54716981133[/C][/ROW]
[ROW][C]109[/C][C]197426[/C][C]156589.185185185[/C][C]40836.8148148148[/C][/ROW]
[ROW][C]110[/C][C]160902[/C][C]83722.7575757576[/C][C]77179.2424242424[/C][/ROW]
[ROW][C]111[/C][C]147172[/C][C]156589.185185185[/C][C]-9417.1851851852[/C][/ROW]
[ROW][C]112[/C][C]109432[/C][C]108419.547169811[/C][C]1012.45283018867[/C][/ROW]
[ROW][C]113[/C][C]1168[/C][C]3034.5[/C][C]-1866.5[/C][/ROW]
[ROW][C]114[/C][C]83248[/C][C]108419.547169811[/C][C]-25171.5471698113[/C][/ROW]
[ROW][C]115[/C][C]25162[/C][C]31186.0769230769[/C][C]-6024.07692307692[/C][/ROW]
[ROW][C]116[/C][C]45724[/C][C]72925.2727272727[/C][C]-27201.2727272727[/C][/ROW]
[ROW][C]117[/C][C]110529[/C][C]156589.185185185[/C][C]-46060.1851851852[/C][/ROW]
[ROW][C]118[/C][C]855[/C][C]3034.5[/C][C]-2179.5[/C][/ROW]
[ROW][C]119[/C][C]101382[/C][C]83722.7575757576[/C][C]17659.2424242424[/C][/ROW]
[ROW][C]120[/C][C]14116[/C][C]31186.0769230769[/C][C]-17070.0769230769[/C][/ROW]
[ROW][C]121[/C][C]89506[/C][C]108419.547169811[/C][C]-18913.5471698113[/C][/ROW]
[ROW][C]122[/C][C]135356[/C][C]108419.547169811[/C][C]26936.4528301887[/C][/ROW]
[ROW][C]123[/C][C]116066[/C][C]72925.2727272727[/C][C]43140.7272727273[/C][/ROW]
[ROW][C]124[/C][C]144244[/C][C]83722.7575757576[/C][C]60521.2424242424[/C][/ROW]
[ROW][C]125[/C][C]8773[/C][C]31186.0769230769[/C][C]-22413.0769230769[/C][/ROW]
[ROW][C]126[/C][C]102153[/C][C]83722.7575757576[/C][C]18430.2424242424[/C][/ROW]
[ROW][C]127[/C][C]117440[/C][C]108419.547169811[/C][C]9020.45283018867[/C][/ROW]
[ROW][C]128[/C][C]104128[/C][C]108419.547169811[/C][C]-4291.54716981133[/C][/ROW]
[ROW][C]129[/C][C]134238[/C][C]156589.185185185[/C][C]-22351.1851851852[/C][/ROW]
[ROW][C]130[/C][C]134047[/C][C]156589.185185185[/C][C]-22542.1851851852[/C][/ROW]
[ROW][C]131[/C][C]279488[/C][C]156589.185185185[/C][C]122898.814814815[/C][/ROW]
[ROW][C]132[/C][C]79756[/C][C]108419.547169811[/C][C]-28663.5471698113[/C][/ROW]
[ROW][C]133[/C][C]66089[/C][C]72925.2727272727[/C][C]-6836.27272727272[/C][/ROW]
[ROW][C]134[/C][C]102070[/C][C]108419.547169811[/C][C]-6349.54716981133[/C][/ROW]
[ROW][C]135[/C][C]146760[/C][C]156589.185185185[/C][C]-9829.1851851852[/C][/ROW]
[ROW][C]136[/C][C]154771[/C][C]72925.2727272727[/C][C]81845.7272727273[/C][/ROW]
[ROW][C]137[/C][C]165933[/C][C]140986.272727273[/C][C]24946.7272727273[/C][/ROW]
[ROW][C]138[/C][C]64593[/C][C]72925.2727272727[/C][C]-8332.27272727272[/C][/ROW]
[ROW][C]139[/C][C]92280[/C][C]108419.547169811[/C][C]-16139.5471698113[/C][/ROW]
[ROW][C]140[/C][C]67150[/C][C]83722.7575757576[/C][C]-16572.7575757576[/C][/ROW]
[ROW][C]141[/C][C]128692[/C][C]108419.547169811[/C][C]20272.4528301887[/C][/ROW]
[ROW][C]142[/C][C]124089[/C][C]83722.7575757576[/C][C]40366.2424242424[/C][/ROW]
[ROW][C]143[/C][C]125386[/C][C]140986.272727273[/C][C]-15600.2727272727[/C][/ROW]
[ROW][C]144[/C][C]37238[/C][C]31186.0769230769[/C][C]6051.92307692308[/C][/ROW]
[ROW][C]145[/C][C]140015[/C][C]140986.272727273[/C][C]-971.272727272735[/C][/ROW]
[ROW][C]146[/C][C]150047[/C][C]108419.547169811[/C][C]41627.4528301887[/C][/ROW]
[ROW][C]147[/C][C]154451[/C][C]108419.547169811[/C][C]46031.4528301887[/C][/ROW]
[ROW][C]148[/C][C]156349[/C][C]140986.272727273[/C][C]15362.7272727273[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]3034.5[/C][C]-3034.5[/C][/ROW]
[ROW][C]150[/C][C]6023[/C][C]3034.5[/C][C]2988.5[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]3034.5[/C][C]-3034.5[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]3034.5[/C][C]-3034.5[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]3034.5[/C][C]-3034.5[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]3034.5[/C][C]-3034.5[/C][/ROW]
[ROW][C]155[/C][C]84601[/C][C]83722.7575757576[/C][C]878.242424242431[/C][/ROW]
[ROW][C]156[/C][C]68946[/C][C]108419.547169811[/C][C]-39473.5471698113[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]3034.5[/C][C]-3034.5[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]3034.5[/C][C]-3034.5[/C][/ROW]
[ROW][C]159[/C][C]1644[/C][C]3034.5[/C][C]-1390.5[/C][/ROW]
[ROW][C]160[/C][C]6179[/C][C]3034.5[/C][C]3144.5[/C][/ROW]
[ROW][C]161[/C][C]3926[/C][C]3034.5[/C][C]891.5[/C][/ROW]
[ROW][C]162[/C][C]52789[/C][C]31186.0769230769[/C][C]21602.9230769231[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]3034.5[/C][C]-3034.5[/C][/ROW]
[ROW][C]164[/C][C]100350[/C][C]83722.7575757576[/C][C]16627.2424242424[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156544&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156544&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
1140824156589.185185185-15765.1851851852
2110459108419.5471698112039.45283018867
310507983722.757575757621356.2424242424
4112098108419.5471698113678.45283018867
54392983722.7575757576-39793.7575757576
67617372925.27272727273247.72727272728
7187326156589.18518518530736.8148148148
8228073034.519772.5
9144408108419.54716981135988.4528301887
106648583722.7575757576-17237.7575757576
1179089108419.547169811-29330.5471698113
128162583722.7575757576-2097.75757575757
136878872925.2727272727-4137.27272727272
14103297108419.547169811-5122.54716981133
156944683722.7575757576-14276.7575757576
16114948108419.5471698116528.45283018867
17167949140986.27272727326962.7272727273
18125081108419.54716981116661.4528301887
19125818108419.54716981117398.4528301887
20136588140986.272727273-4398.27272727274
21112431108419.5471698114011.45283018867
2210303783722.757575757619314.2424242424
238231783722.7575757576-1405.75757575757
24118906108419.54716981110486.4528301887
2583515108419.547169811-24904.5471698113
26104581108419.547169811-3838.54716981133
27103129108419.547169811-5290.54716981133
2883243108419.547169811-25176.5471698113
293711072925.2727272727-35815.2727272727
30113344156589.185185185-43245.1851851852
31139165156589.185185185-17424.1851851852
328665283722.75757575762929.24242424243
33112302156589.185185185-44287.1851851852
346965272925.2727272727-3273.27272727272
35119442156589.185185185-37147.1851851852
366986783722.7575757576-13855.7575757576
37101629156589.185185185-54960.1851851852
387016883722.7575757576-13554.7575757576
393108131186.0769230769-105.076923076922
40103925108419.547169811-4494.54716981133
4192622108419.547169811-15797.5471698113
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16303034.5-3034.5
16410035083722.757575757616627.2424242424


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = pearson ;
Parameters (R input):
par1 = 11 ; par2 = none ; par3 = 3 ; par4 = no ;
R code (references can be found in the software module):
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}