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Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Thu, 22 Dec 2011 13:02:02 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t1324576945vokehgrrcyvtdpg.htm/, Retrieved Fri, 03 May 2024 10:18:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159790, Retrieved Fri, 03 May 2024 10:18:57 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Recursive Partitioning (Regression Trees)] [] [2011-12-22 18:02:02] [694c30abd2a3b2ee5cb46fc74cb5bfb9] [Current]

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Dataseries X:

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1845	162687	95	595	115	0	48	21	82	73	20465	6200	23975	39	37
1797	201906	63	545	76	1	58	20	80	56	33629	10265	85634	46	43
192	7215	18	72	1	0	0	0	0	0	1423	603	1929	0	0
2444	146367	97	679	155	0	67	27	84	63	25629	8874	36294	54	54
3567	257045	139	1201	125	0	83	31	124	116	54002	20323	72255	93	86
6953	528532	268	1975	278	1	137	36	140	138	151036	26258	189748	198	181
1873	191582	60	602	93	1	65	26	100	76	33287	10165	61834	42	42
1740	195674	60	496	59	0	86	30	115	107	31172	8247	68167	59	59
2079	177020	45	670	87	0	62	30	109	50	28113	8683	38462	49	46
3120	330255	100	1047	130	1	72	27	108	81	57803	16957	101219	83	77
1946	121844	75	634	158	2	50	24	63	58	49830	8058	43270	49	49
2370	203938	72	743	120	0	88	30	118	91	52143	20488	76183	83	79
1962	116737	108	692	87	0	62	22	71	41	21055	7945	31476	39	37
3198	220751	120	1086	264	4	79	28	112	100	47007	13448	62157	93	92
1496	173259	65	420	51	4	56	18	63	61	28735	5389	46261	31	31
1574	156326	89	474	85	3	54	22	86	74	59147	6185	50063	29	28
1808	145178	59	442	100	0	81	37	148	147	78950	24369	64483	104	103
1309	89171	61	373	72	5	13	15	54	45	13497	70	2341	2	2
2820	172624	88	899	147	0	74	34	134	110	46154	17327	48149	46	48
799	43391	28	253	49	0	18	18	57	41	53249	3878	12743	27	25
1162	87927	62	399	40	0	31	15	59	37	10726	3149	18743	16	16
2818	241285	103	850	99	0	99	30	113	84	83700	20517	97057	108	106
1780	200429	75	648	127	1	38	25	96	67	40400	2570	17675	36	35
2316	146946	57	717	164	1	59	34	96	69	33797	5162	33106	33	33
1994	159763	89	619	41	1	54	21	78	58	36205	5299	53311	46	45
1806	207078	34	657	160	0	63	21	80	60	30165	7233	42754	65	64
2153	212394	167	691	92	0	66	25	93	88	58534	15657	59056	80	73
1458	201536	96	366	59	0	90	31	109	75	44663	15329	101621	81	78
3000	394662	121	994	89	0	72	31	115	98	92556	14881	118120	69	63
2236	217892	46	929	90	0	61	20	79	67	40078	16318	79572	69	69
1685	182286	45	490	76	0	61	28	103	84	34711	9556	42744	37	36
1667	188748	48	574	116	2	63	22	71	62	31076	10462	65931	45	41
2257	137978	107	738	92	4	53	17	66	35	74608	7192	38575	62	59
3402	259627	132	1039	361	0	120	25	100	74	58092	4362	28795	33	33
2571	236489	55	844	85	1	73	25	100	93	42009	14349	94440	77	76
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
2143	230761	65	1000	63	0	54	31	121	87	36022	10881	38229	34	27
1878	132807	54	629	138	3	54	14	51	39	23333	8022	31972	44	44
2224	161703	52	547	270	9	46	35	119	101	53349	13073	40071	43	43
2186	253254	68	811	64	0	83	34	136	135	92596	26641	132480	117	104
2533	269329	72	837	96	2	106	22	84	76	49598	14426	62797	125	120
1823	161273	61	682	62	0	44	34	136	118	44093	15604	40429	49	44
1095	107181	33	400	35	2	27	23	84	76	84205	9184	45545	76	71
2303	213097	81	831	66	1	73	24	92	65	63369	5989	57568	81	78
1365	139667	51	419	56	2	71	26	103	97	60132	11270	39019	111	106
1245	171101	99	334	41	2	44	23	85	70	37403	13958	53866	61	61
756	81407	33	216	49	1	23	35	106	63	24460	7162	38345	56	53
2419	247596	106	786	121	0	78	24	96	96	46456	13275	50210	54	51
2327	239807	90	752	113	1	60	31	124	112	66616	21224	80947	47	46
2787	172743	60	964	190	8	73	30	106	82	41554	10615	43461	55	55
658	48188	28	205	37	0	12	22	82	39	22346	2102	14812	14	14
2013	169355	71	506	52	0	104	23	87	69	30874	12396	37819	44	44
2616	325322	77	830	89	0	86	27	97	93	68701	18717	102738	115	113
2072	241518	80	694	73	0	57	30	107	76	35728	9724	54509	57	55
1912	195583	60	691	49	1	67	33	126	117	29010	9863	62956	48	46
1775	159913	57	547	77	8	44	12	43	31	23110	8374	55411	40	39
1943	223936	71	547	58	0	53	26	96	65	38844	8030	50611	51	51
1047	101694	26	329	75	1	26	26	100	78	27084	7509	26692	32	31
1190	157258	68	427	32	0	67	23	91	87	35139	14146	60056	36	36
2932	211586	101	993	59	10	36	38	136	85	57476	7768	25155	47	47
1868	181076	66	564	71	6	56	32	128	119	33277	13823	42840	51	53
2255	150518	84	836	91	0	52	21	83	65	31141	7230	39358	37	38
1392	141491	64	376	87	11	54	22	74	60	61281	10170	47241	52	52
1355	130108	40	471	48	3	61	26	96	67	25820	7573	49611	42	37
1321	166351	38	431	63	0	27	28	102	94	23284	5753	41833	11	11
1526	124197	43	483	41	0	58	33	122	100	35378	9791	48930	47	45
2335	195043	71	504	86	8	76	36	144	135	74990	19365	110600	59	59
2898	138708	66	887	152	2	93	25	90	71	29653	9422	52235	82	82
1118	116552	40	271	49	0	59	25	97	78	64622	12310	53986	49	49
340	31970	15	101	40	0	5	21	78	42	4157	1283	4105	6	6
2977	258158	115	1097	135	3	57	19	72	42	29245	6372	59331	83	81
1452	151194	79	470	83	1	42	12	45	8	50008	5413	47796	56	56
1550	135926	68	528	62	2	88	30	120	86	52338	10837	38302	114	105
1685	119629	73	475	91	1	53	21	59	41	13310	3394	14063	46	46
2728	171518	71	698	95	0	81	39	150	131	92901	12964	54414	46	46
1574	108949	45	425	82	2	35	32	117	91	10956	3495	9903	2	2
2413	183471	60	709	112	1	102	28	123	102	34241	11580	53987	51	51
2563	159966	98	824	70	0	71	29	114	91	75043	9970	88937	96	95
1080	93786	35	336	78	0	28	21	75	46	21152	4911	21928	20	18
1235	84971	72	395	105	0	34	31	114	60	42249	10138	29487	57	55
980	88882	76	234	49	0	54	26	94	69	42005	14697	35334	49	48
2246	304603	65	830	60	0	49	29	116	95	41152	8464	57596	51	48
1076	75101	30	334	49	1	30	23	86	17	14399	4204	29750	40	39
1638	145043	41	524	132	0	57	25	90	61	28263	10226	41029	40	40
1208	95827	48	393	49	0	54	22	87	55	17215	3456	12416	36	36
1866	173924	59	574	71	0	38	26	99	55	48140	8895	51158	64	60
2727	241957	238	672	102	0	63	33	132	124	62897	22557	79935	117	114
1208	115367	115	284	74	0	58	24	96	73	22883	6900	26552	40	39
1427	118689	65	452	49	7	46	24	91	73	41622	8620	25807	46	45
1610	164078	54	653	74	0	46	21	77	67	40715	7820	50620	61	59
1865	158931	42	684	59	5	51	28	104	66	65897	12112	61467	59	59
2413	184139	83	706	91	1	87	28	100	77	76542	13178	65292	94	93
1238	152856	58	417	68	0	39	25	94	83	37477	7028	55516	36	35
1468	146159	61	551	81	0	28	15	60	55	53216	6616	42006	51	47
974	62535	43	394	33	0	26	13	46	27	40911	9570	26273	39	36
2319	245196	117	730	166	0	52	36	135	115	57021	14612	90248	62	59
1890	199841	71	571	97	0	96	27	99	85	73116	11219	61476	79	79
223	19349	12	67	15	0	13	1	2	0	3895	786	9604	14	14
2527	247280	109	877	105	3	43	24	96	83	46609	11252	45108	45	42
2105	164457	86	865	61	0	42	31	109	90	29351	9289	47232	43	41
778	72128	30	306	11	0	30	4	15	4	2325	593	3439	8	8
1194	104253	26	382	45	0	59	21	68	60	31747	6562	30553	41	41
1424	151090	57	435	89	0	73	27	102	74	32665	8208	24751	25	24
1386	147990	68	348	72	1	40	26	93	55	19249	7488	34458	22	22
839	87448	42	227	27	1	36	12	46	24	15292	4574	24649	18	18
596	27676	22	194	59	0	2	16	59	17	5842	522	2342	3	1
1684	170326	52	413	127	0	103	29	116	105	33994	12840	52739	54	53
1168	132148	38	273	48	1	30	26	29	20	13018	1350	6245	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1106	95778	34	343	58	0	46	25	91	51	98177	10623	35381	50	49
1149	109001	68	376	57	0	25	21	76	76	37941	5322	19595	33	33
1485	158833	46	495	60	0	59	24	86	61	31032	7987	50848	54	50
1529	150013	66	448	77	1	60	21	84	70	32683	10566	39443	63	64
962	89887	63	313	71	0	36	21	65	38	34545	1900	27023	56	53
78	3616	5	14	5	0	0	0	0	0	0	0	0	0	0
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1184	199005	45	410	70	0	45	23	84	81	27525	10698	61022	49	48
1672	160930	93	606	76	0	79	33	114	78	66856	14884	63528	90	90
2142	177948	102	593	124	2	30	32	132	76	28549	6852	34835	51	46
1016	136061	40	312	56	0	43	23	92	89	38610	6873	37172	29	29
778	43410	19	292	63	0	7	1	3	3	2781	4	13	1	1
1857	184277	75	547	92	1	80	29	109	87	41211	9188	62548	68	64
1084	109873	45	315	58	0	32	20	81	55	22698	5141	31334	29	29
2297	151030	59	660	64	8	84	33	121	73	41194	4260	20839	27	27
731	60493	40	174	29	3	3	12	48	32	32689	443	5084	4	4
285	19764	12	75	19	1	10	2	8	4	5752	2416	9927	10	10
1872	177559	56	572	64	3	47	21	80	70	26757	9831	53229	47	47
1181	140281	35	389	79	0	35	28	107	102	22527	5953	29877	44	44
1725	164249	54	562	104	0	54	35	140	109	44810	9435	37310	53	51
256	11796	9	79	22	0	1	2	8	1	0	0	0	0	0
98	10674	9	33	7	0	0	0	0	0	0	0	0	0	0
1435	151322	59	487	37	0	46	18	56	39	100674	7642	50067	40	38
41	6836	3	11	5	0	0	1	4	0	0	0	0	0	0
1931	174712	68	664	48	6	51	21	70	45	57786	6837	47708	57	57
42	5118	3	6	1	0	5	0	0	0	0	0	0	0	0
528	40248	16	183	34	1	8	4	14	7	5444	775	6012	6	6
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
1122	127628	51	342	53	0	38	29	104	86	28470	8191	27749	24	22
1305	88837	38	269	44	0	21	26	89	52	61849	1661	47555	34	34
81	7131	4	27	0	1	0	0	0	0	0	0	0	0	0
262	9056	15	99	18	0	0	4	12	1	2179	548	1336	10	10
1104	88589	29	306	52	1	18	19	60	49	8019	3080	11017	16	16
1290	144470	53	327	56	0	53	22	84	72	39644	13400	55184	93	93
1248	111408	20	459	50	1	17	22	88	56	23494	8181	43485	28	22

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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=159790&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=159790&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159790&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Goodness of Fit
Correlation	0.8775
R-squared	0.77
RMSE	13.543

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8775 \tabularnewline
R-squared & 0.77 \tabularnewline
RMSE & 13.543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159790&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8775[/C][/ROW]
[ROW][C]R-squared[/C][C]0.77[/C][/ROW]
[ROW][C]RMSE[/C][C]13.543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159790&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159790&T=1

As an alternative you can also use a QR Code:

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

Goodness of Fit
Correlation	0.8775
R-squared	0.77
RMSE	13.543

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	48	55.9473684210526	-7.94736842105263
2	58	55.9473684210526	2.05263157894737
3	0	1.07142857142857	-1.07142857142857
4	67	74.7272727272727	-7.72727272727273
5	83	80.1428571428571	2.85714285714286
6	137	91.625	45.375
7	65	55.9473684210526	9.05263157894737
8	86	55.9473684210526	30.0526315789474
9	62	55.9473684210526	6.05263157894737
10	72	80.1428571428571	-8.14285714285714
11	50	55.9473684210526	-5.94736842105263
12	88	80.1428571428571	7.85714285714286
13	62	55.9473684210526	6.05263157894737
14	79	80.1428571428571	-1.14285714285714
15	56	55.9473684210526	0.0526315789473699
16	54	55.9473684210526	-1.94736842105263
17	81	80.1428571428571	0.857142857142861
18	13	32.1538461538462	-19.1538461538462
19	74	74.7272727272727	-0.727272727272734
20	18	9.85714285714286	8.14285714285714
21	31	32.1538461538462	-1.15384615384615
22	99	91.625	7.375
23	38	55.9473684210526	-17.9473684210526
24	59	55.9473684210526	3.05263157894737
25	54	55.9473684210526	-1.94736842105263
26	63	55.9473684210526	7.05263157894737
27	66	55.9473684210526	10.0526315789474
28	90	80.1428571428571	9.85714285714286
29	72	74.7272727272727	-2.72727272727273
30	61	55.9473684210526	5.05263157894737
31	61	55.9473684210526	5.05263157894737
32	63	55.9473684210526	7.05263157894737
33	53	55.9473684210526	-2.94736842105263
34	120	74.7272727272727	45.2727272727273
35	73	80.1428571428571	-7.14285714285714
36	0	1.07142857142857	-1.07142857142857
37	54	55.9473684210526	-1.94736842105263
38	54	55.9473684210526	-1.94736842105263
39	46	55.9473684210526	-9.94736842105263
40	83	91.625	-8.625
41	106	91.625	14.375
42	44	55.9473684210526	-11.9473684210526
43	27	32.1538461538462	-5.15384615384615
44	73	80.1428571428571	-7.14285714285714
45	71	91.625	-20.625
46	44	52.5714285714286	-8.57142857142857
47	23	32.1538461538462	-9.15384615384615
48	78	74.7272727272727	3.27272727272727
49	60	55.9473684210526	4.05263157894737
50	73	74.7272727272727	-1.72727272727273
51	12	9.85714285714286	2.14285714285714
52	104	55.9473684210526	48.0526315789474
53	86	91.625	-5.625
54	57	55.9473684210526	1.05263157894737
55	67	55.9473684210526	11.0526315789474
56	44	55.9473684210526	-11.9473684210526
57	53	55.9473684210526	-2.94736842105263
58	26	32.1538461538462	-6.15384615384615
59	67	52.5714285714286	14.4285714285714
60	36	74.7272727272727	-38.7272727272727
61	56	55.9473684210526	0.0526315789473699
62	52	55.9473684210526	-3.94736842105263
63	54	55.9473684210526	-1.94736842105263
64	61	55.9473684210526	5.05263157894737
65	27	32.1538461538462	-5.15384615384615
66	58	55.9473684210526	2.05263157894737
67	76	74.7272727272727	1.27272727272727
68	93	80.1428571428571	12.8571428571429
69	59	52.5714285714286	6.42857142857143
70	5	9.85714285714286	-4.85714285714286
71	57	80.1428571428571	-23.1428571428571
72	42	55.9473684210526	-13.9473684210526
73	88	91.625	-3.625
74	53	55.9473684210526	-2.94736842105263
75	81	74.7272727272727	6.27272727272727
76	35	55.9473684210526	-20.9473684210526
77	102	74.7272727272727	27.2727272727273
78	71	80.1428571428571	-9.14285714285714
79	28	32.1538461538462	-4.15384615384615
80	34	32.1538461538462	1.84615384615385
81	54	52.5714285714286	1.42857142857143
82	49	55.9473684210526	-6.94736842105263
83	30	32.1538461538462	-2.15384615384615
84	57	55.9473684210526	1.05263157894737
85	54	32.1538461538462	21.8461538461538
86	38	55.9473684210526	-17.9473684210526
87	63	91.625	-28.625
88	58	32.1538461538462	25.8461538461538
89	46	55.9473684210526	-9.94736842105263
90	46	55.9473684210526	-9.94736842105263
91	51	55.9473684210526	-4.94736842105263
92	87	80.1428571428571	6.85714285714286
93	39	32.1538461538462	6.84615384615385
94	28	55.9473684210526	-27.9473684210526
95	26	32.1538461538462	-6.15384615384615
96	52	55.9473684210526	-3.94736842105263
97	96	80.1428571428571	15.8571428571429
98	13	9.85714285714286	3.14285714285714
99	43	74.7272727272727	-31.7272727272727
100	42	55.9473684210526	-13.9473684210526
101	30	32.1538461538462	-2.15384615384615
102	59	32.1538461538462	26.8461538461538
103	73	55.9473684210526	17.0526315789474
104	40	55.9473684210526	-15.9473684210526
105	36	32.1538461538462	3.84615384615385
106	2	1.07142857142857	0.928571428571429
107	103	55.9473684210526	47.0526315789474
108	30	32.1538461538462	-2.15384615384615
109	0	1.07142857142857	-1.07142857142857
110	46	52.5714285714286	-6.57142857142857
111	25	32.1538461538462	-7.15384615384615
112	59	55.9473684210526	3.05263157894737
113	60	55.9473684210526	4.05263157894737
114	36	32.1538461538462	3.84615384615385
115	0	1.07142857142857	-1.07142857142857
116	0	1.07142857142857	-1.07142857142857
117	45	52.5714285714286	-7.57142857142857
118	79	80.1428571428571	-1.14285714285714
119	30	55.9473684210526	-25.9473684210526
120	43	32.1538461538462	10.8461538461538
121	7	1.07142857142857	5.92857142857143
122	80	55.9473684210526	24.0526315789474
123	32	32.1538461538462	-0.153846153846153
124	84	55.9473684210526	28.0526315789474
125	3	9.85714285714286	-6.85714285714286
126	10	9.85714285714286	0.142857142857142
127	47	55.9473684210526	-8.94736842105263
128	35	32.1538461538462	2.84615384615385
129	54	55.9473684210526	-1.94736842105263
130	1	1.07142857142857	-0.0714285714285714
131	0	1.07142857142857	-1.07142857142857
132	46	55.9473684210526	-9.94736842105263
133	0	1.07142857142857	-1.07142857142857
134	51	55.9473684210526	-4.94736842105263
135	5	1.07142857142857	3.92857142857143
136	8	9.85714285714286	-1.85714285714286
137	0	1.07142857142857	-1.07142857142857
138	38	32.1538461538462	5.84615384615385
139	21	32.1538461538462	-11.1538461538462
140	0	1.07142857142857	-1.07142857142857
141	0	1.07142857142857	-1.07142857142857
142	18	32.1538461538462	-14.1538461538462
143	53	52.5714285714286	0.428571428571431
144	17	32.1538461538462	-15.1538461538462

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 48 & 55.9473684210526 & -7.94736842105263 \tabularnewline
2 & 58 & 55.9473684210526 & 2.05263157894737 \tabularnewline
3 & 0 & 1.07142857142857 & -1.07142857142857 \tabularnewline
4 & 67 & 74.7272727272727 & -7.72727272727273 \tabularnewline
5 & 83 & 80.1428571428571 & 2.85714285714286 \tabularnewline
6 & 137 & 91.625 & 45.375 \tabularnewline
7 & 65 & 55.9473684210526 & 9.05263157894737 \tabularnewline
8 & 86 & 55.9473684210526 & 30.0526315789474 \tabularnewline
9 & 62 & 55.9473684210526 & 6.05263157894737 \tabularnewline
10 & 72 & 80.1428571428571 & -8.14285714285714 \tabularnewline
11 & 50 & 55.9473684210526 & -5.94736842105263 \tabularnewline
12 & 88 & 80.1428571428571 & 7.85714285714286 \tabularnewline
13 & 62 & 55.9473684210526 & 6.05263157894737 \tabularnewline
14 & 79 & 80.1428571428571 & -1.14285714285714 \tabularnewline
15 & 56 & 55.9473684210526 & 0.0526315789473699 \tabularnewline
16 & 54 & 55.9473684210526 & -1.94736842105263 \tabularnewline
17 & 81 & 80.1428571428571 & 0.857142857142861 \tabularnewline
18 & 13 & 32.1538461538462 & -19.1538461538462 \tabularnewline
19 & 74 & 74.7272727272727 & -0.727272727272734 \tabularnewline
20 & 18 & 9.85714285714286 & 8.14285714285714 \tabularnewline
21 & 31 & 32.1538461538462 & -1.15384615384615 \tabularnewline
22 & 99 & 91.625 & 7.375 \tabularnewline
23 & 38 & 55.9473684210526 & -17.9473684210526 \tabularnewline
24 & 59 & 55.9473684210526 & 3.05263157894737 \tabularnewline
25 & 54 & 55.9473684210526 & -1.94736842105263 \tabularnewline
26 & 63 & 55.9473684210526 & 7.05263157894737 \tabularnewline
27 & 66 & 55.9473684210526 & 10.0526315789474 \tabularnewline
28 & 90 & 80.1428571428571 & 9.85714285714286 \tabularnewline
29 & 72 & 74.7272727272727 & -2.72727272727273 \tabularnewline
30 & 61 & 55.9473684210526 & 5.05263157894737 \tabularnewline
31 & 61 & 55.9473684210526 & 5.05263157894737 \tabularnewline
32 & 63 & 55.9473684210526 & 7.05263157894737 \tabularnewline
33 & 53 & 55.9473684210526 & -2.94736842105263 \tabularnewline
34 & 120 & 74.7272727272727 & 45.2727272727273 \tabularnewline
35 & 73 & 80.1428571428571 & -7.14285714285714 \tabularnewline
36 & 0 & 1.07142857142857 & -1.07142857142857 \tabularnewline
37 & 54 & 55.9473684210526 & -1.94736842105263 \tabularnewline
38 & 54 & 55.9473684210526 & -1.94736842105263 \tabularnewline
39 & 46 & 55.9473684210526 & -9.94736842105263 \tabularnewline
40 & 83 & 91.625 & -8.625 \tabularnewline
41 & 106 & 91.625 & 14.375 \tabularnewline
42 & 44 & 55.9473684210526 & -11.9473684210526 \tabularnewline
43 & 27 & 32.1538461538462 & -5.15384615384615 \tabularnewline
44 & 73 & 80.1428571428571 & -7.14285714285714 \tabularnewline
45 & 71 & 91.625 & -20.625 \tabularnewline
46 & 44 & 52.5714285714286 & -8.57142857142857 \tabularnewline
47 & 23 & 32.1538461538462 & -9.15384615384615 \tabularnewline
48 & 78 & 74.7272727272727 & 3.27272727272727 \tabularnewline
49 & 60 & 55.9473684210526 & 4.05263157894737 \tabularnewline
50 & 73 & 74.7272727272727 & -1.72727272727273 \tabularnewline
51 & 12 & 9.85714285714286 & 2.14285714285714 \tabularnewline
52 & 104 & 55.9473684210526 & 48.0526315789474 \tabularnewline
53 & 86 & 91.625 & -5.625 \tabularnewline
54 & 57 & 55.9473684210526 & 1.05263157894737 \tabularnewline
55 & 67 & 55.9473684210526 & 11.0526315789474 \tabularnewline
56 & 44 & 55.9473684210526 & -11.9473684210526 \tabularnewline
57 & 53 & 55.9473684210526 & -2.94736842105263 \tabularnewline
58 & 26 & 32.1538461538462 & -6.15384615384615 \tabularnewline
59 & 67 & 52.5714285714286 & 14.4285714285714 \tabularnewline
60 & 36 & 74.7272727272727 & -38.7272727272727 \tabularnewline
61 & 56 & 55.9473684210526 & 0.0526315789473699 \tabularnewline
62 & 52 & 55.9473684210526 & -3.94736842105263 \tabularnewline
63 & 54 & 55.9473684210526 & -1.94736842105263 \tabularnewline
64 & 61 & 55.9473684210526 & 5.05263157894737 \tabularnewline
65 & 27 & 32.1538461538462 & -5.15384615384615 \tabularnewline
66 & 58 & 55.9473684210526 & 2.05263157894737 \tabularnewline
67 & 76 & 74.7272727272727 & 1.27272727272727 \tabularnewline
68 & 93 & 80.1428571428571 & 12.8571428571429 \tabularnewline
69 & 59 & 52.5714285714286 & 6.42857142857143 \tabularnewline
70 & 5 & 9.85714285714286 & -4.85714285714286 \tabularnewline
71 & 57 & 80.1428571428571 & -23.1428571428571 \tabularnewline
72 & 42 & 55.9473684210526 & -13.9473684210526 \tabularnewline
73 & 88 & 91.625 & -3.625 \tabularnewline
74 & 53 & 55.9473684210526 & -2.94736842105263 \tabularnewline
75 & 81 & 74.7272727272727 & 6.27272727272727 \tabularnewline
76 & 35 & 55.9473684210526 & -20.9473684210526 \tabularnewline
77 & 102 & 74.7272727272727 & 27.2727272727273 \tabularnewline
78 & 71 & 80.1428571428571 & -9.14285714285714 \tabularnewline
79 & 28 & 32.1538461538462 & -4.15384615384615 \tabularnewline
80 & 34 & 32.1538461538462 & 1.84615384615385 \tabularnewline
81 & 54 & 52.5714285714286 & 1.42857142857143 \tabularnewline
82 & 49 & 55.9473684210526 & -6.94736842105263 \tabularnewline
83 & 30 & 32.1538461538462 & -2.15384615384615 \tabularnewline
84 & 57 & 55.9473684210526 & 1.05263157894737 \tabularnewline
85 & 54 & 32.1538461538462 & 21.8461538461538 \tabularnewline
86 & 38 & 55.9473684210526 & -17.9473684210526 \tabularnewline
87 & 63 & 91.625 & -28.625 \tabularnewline
88 & 58 & 32.1538461538462 & 25.8461538461538 \tabularnewline
89 & 46 & 55.9473684210526 & -9.94736842105263 \tabularnewline
90 & 46 & 55.9473684210526 & -9.94736842105263 \tabularnewline
91 & 51 & 55.9473684210526 & -4.94736842105263 \tabularnewline
92 & 87 & 80.1428571428571 & 6.85714285714286 \tabularnewline
93 & 39 & 32.1538461538462 & 6.84615384615385 \tabularnewline
94 & 28 & 55.9473684210526 & -27.9473684210526 \tabularnewline
95 & 26 & 32.1538461538462 & -6.15384615384615 \tabularnewline
96 & 52 & 55.9473684210526 & -3.94736842105263 \tabularnewline
97 & 96 & 80.1428571428571 & 15.8571428571429 \tabularnewline
98 & 13 & 9.85714285714286 & 3.14285714285714 \tabularnewline
99 & 43 & 74.7272727272727 & -31.7272727272727 \tabularnewline
100 & 42 & 55.9473684210526 & -13.9473684210526 \tabularnewline
101 & 30 & 32.1538461538462 & -2.15384615384615 \tabularnewline
102 & 59 & 32.1538461538462 & 26.8461538461538 \tabularnewline
103 & 73 & 55.9473684210526 & 17.0526315789474 \tabularnewline
104 & 40 & 55.9473684210526 & -15.9473684210526 \tabularnewline
105 & 36 & 32.1538461538462 & 3.84615384615385 \tabularnewline
106 & 2 & 1.07142857142857 & 0.928571428571429 \tabularnewline
107 & 103 & 55.9473684210526 & 47.0526315789474 \tabularnewline
108 & 30 & 32.1538461538462 & -2.15384615384615 \tabularnewline
109 & 0 & 1.07142857142857 & -1.07142857142857 \tabularnewline
110 & 46 & 52.5714285714286 & -6.57142857142857 \tabularnewline
111 & 25 & 32.1538461538462 & -7.15384615384615 \tabularnewline
112 & 59 & 55.9473684210526 & 3.05263157894737 \tabularnewline
113 & 60 & 55.9473684210526 & 4.05263157894737 \tabularnewline
114 & 36 & 32.1538461538462 & 3.84615384615385 \tabularnewline
115 & 0 & 1.07142857142857 & -1.07142857142857 \tabularnewline
116 & 0 & 1.07142857142857 & -1.07142857142857 \tabularnewline
117 & 45 & 52.5714285714286 & -7.57142857142857 \tabularnewline
118 & 79 & 80.1428571428571 & -1.14285714285714 \tabularnewline
119 & 30 & 55.9473684210526 & -25.9473684210526 \tabularnewline
120 & 43 & 32.1538461538462 & 10.8461538461538 \tabularnewline
121 & 7 & 1.07142857142857 & 5.92857142857143 \tabularnewline
122 & 80 & 55.9473684210526 & 24.0526315789474 \tabularnewline
123 & 32 & 32.1538461538462 & -0.153846153846153 \tabularnewline
124 & 84 & 55.9473684210526 & 28.0526315789474 \tabularnewline
125 & 3 & 9.85714285714286 & -6.85714285714286 \tabularnewline
126 & 10 & 9.85714285714286 & 0.142857142857142 \tabularnewline
127 & 47 & 55.9473684210526 & -8.94736842105263 \tabularnewline
128 & 35 & 32.1538461538462 & 2.84615384615385 \tabularnewline
129 & 54 & 55.9473684210526 & -1.94736842105263 \tabularnewline
130 & 1 & 1.07142857142857 & -0.0714285714285714 \tabularnewline
131 & 0 & 1.07142857142857 & -1.07142857142857 \tabularnewline
132 & 46 & 55.9473684210526 & -9.94736842105263 \tabularnewline
133 & 0 & 1.07142857142857 & -1.07142857142857 \tabularnewline
134 & 51 & 55.9473684210526 & -4.94736842105263 \tabularnewline
135 & 5 & 1.07142857142857 & 3.92857142857143 \tabularnewline
136 & 8 & 9.85714285714286 & -1.85714285714286 \tabularnewline
137 & 0 & 1.07142857142857 & -1.07142857142857 \tabularnewline
138 & 38 & 32.1538461538462 & 5.84615384615385 \tabularnewline
139 & 21 & 32.1538461538462 & -11.1538461538462 \tabularnewline
140 & 0 & 1.07142857142857 & -1.07142857142857 \tabularnewline
141 & 0 & 1.07142857142857 & -1.07142857142857 \tabularnewline
142 & 18 & 32.1538461538462 & -14.1538461538462 \tabularnewline
143 & 53 & 52.5714285714286 & 0.428571428571431 \tabularnewline
144 & 17 & 32.1538461538462 & -15.1538461538462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159790&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]48[/C][C]55.9473684210526[/C][C]-7.94736842105263[/C][/ROW]
[ROW][C]2[/C][C]58[/C][C]55.9473684210526[/C][C]2.05263157894737[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]1.07142857142857[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]4[/C][C]67[/C][C]74.7272727272727[/C][C]-7.72727272727273[/C][/ROW]
[ROW][C]5[/C][C]83[/C][C]80.1428571428571[/C][C]2.85714285714286[/C][/ROW]
[ROW][C]6[/C][C]137[/C][C]91.625[/C][C]45.375[/C][/ROW]
[ROW][C]7[/C][C]65[/C][C]55.9473684210526[/C][C]9.05263157894737[/C][/ROW]
[ROW][C]8[/C][C]86[/C][C]55.9473684210526[/C][C]30.0526315789474[/C][/ROW]
[ROW][C]9[/C][C]62[/C][C]55.9473684210526[/C][C]6.05263157894737[/C][/ROW]
[ROW][C]10[/C][C]72[/C][C]80.1428571428571[/C][C]-8.14285714285714[/C][/ROW]
[ROW][C]11[/C][C]50[/C][C]55.9473684210526[/C][C]-5.94736842105263[/C][/ROW]
[ROW][C]12[/C][C]88[/C][C]80.1428571428571[/C][C]7.85714285714286[/C][/ROW]
[ROW][C]13[/C][C]62[/C][C]55.9473684210526[/C][C]6.05263157894737[/C][/ROW]
[ROW][C]14[/C][C]79[/C][C]80.1428571428571[/C][C]-1.14285714285714[/C][/ROW]
[ROW][C]15[/C][C]56[/C][C]55.9473684210526[/C][C]0.0526315789473699[/C][/ROW]
[ROW][C]16[/C][C]54[/C][C]55.9473684210526[/C][C]-1.94736842105263[/C][/ROW]
[ROW][C]17[/C][C]81[/C][C]80.1428571428571[/C][C]0.857142857142861[/C][/ROW]
[ROW][C]18[/C][C]13[/C][C]32.1538461538462[/C][C]-19.1538461538462[/C][/ROW]
[ROW][C]19[/C][C]74[/C][C]74.7272727272727[/C][C]-0.727272727272734[/C][/ROW]
[ROW][C]20[/C][C]18[/C][C]9.85714285714286[/C][C]8.14285714285714[/C][/ROW]
[ROW][C]21[/C][C]31[/C][C]32.1538461538462[/C][C]-1.15384615384615[/C][/ROW]
[ROW][C]22[/C][C]99[/C][C]91.625[/C][C]7.375[/C][/ROW]
[ROW][C]23[/C][C]38[/C][C]55.9473684210526[/C][C]-17.9473684210526[/C][/ROW]
[ROW][C]24[/C][C]59[/C][C]55.9473684210526[/C][C]3.05263157894737[/C][/ROW]
[ROW][C]25[/C][C]54[/C][C]55.9473684210526[/C][C]-1.94736842105263[/C][/ROW]
[ROW][C]26[/C][C]63[/C][C]55.9473684210526[/C][C]7.05263157894737[/C][/ROW]
[ROW][C]27[/C][C]66[/C][C]55.9473684210526[/C][C]10.0526315789474[/C][/ROW]
[ROW][C]28[/C][C]90[/C][C]80.1428571428571[/C][C]9.85714285714286[/C][/ROW]
[ROW][C]29[/C][C]72[/C][C]74.7272727272727[/C][C]-2.72727272727273[/C][/ROW]
[ROW][C]30[/C][C]61[/C][C]55.9473684210526[/C][C]5.05263157894737[/C][/ROW]
[ROW][C]31[/C][C]61[/C][C]55.9473684210526[/C][C]5.05263157894737[/C][/ROW]
[ROW][C]32[/C][C]63[/C][C]55.9473684210526[/C][C]7.05263157894737[/C][/ROW]
[ROW][C]33[/C][C]53[/C][C]55.9473684210526[/C][C]-2.94736842105263[/C][/ROW]
[ROW][C]34[/C][C]120[/C][C]74.7272727272727[/C][C]45.2727272727273[/C][/ROW]
[ROW][C]35[/C][C]73[/C][C]80.1428571428571[/C][C]-7.14285714285714[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]1.07142857142857[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]37[/C][C]54[/C][C]55.9473684210526[/C][C]-1.94736842105263[/C][/ROW]
[ROW][C]38[/C][C]54[/C][C]55.9473684210526[/C][C]-1.94736842105263[/C][/ROW]
[ROW][C]39[/C][C]46[/C][C]55.9473684210526[/C][C]-9.94736842105263[/C][/ROW]
[ROW][C]40[/C][C]83[/C][C]91.625[/C][C]-8.625[/C][/ROW]
[ROW][C]41[/C][C]106[/C][C]91.625[/C][C]14.375[/C][/ROW]
[ROW][C]42[/C][C]44[/C][C]55.9473684210526[/C][C]-11.9473684210526[/C][/ROW]
[ROW][C]43[/C][C]27[/C][C]32.1538461538462[/C][C]-5.15384615384615[/C][/ROW]
[ROW][C]44[/C][C]73[/C][C]80.1428571428571[/C][C]-7.14285714285714[/C][/ROW]
[ROW][C]45[/C][C]71[/C][C]91.625[/C][C]-20.625[/C][/ROW]
[ROW][C]46[/C][C]44[/C][C]52.5714285714286[/C][C]-8.57142857142857[/C][/ROW]
[ROW][C]47[/C][C]23[/C][C]32.1538461538462[/C][C]-9.15384615384615[/C][/ROW]
[ROW][C]48[/C][C]78[/C][C]74.7272727272727[/C][C]3.27272727272727[/C][/ROW]
[ROW][C]49[/C][C]60[/C][C]55.9473684210526[/C][C]4.05263157894737[/C][/ROW]
[ROW][C]50[/C][C]73[/C][C]74.7272727272727[/C][C]-1.72727272727273[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]9.85714285714286[/C][C]2.14285714285714[/C][/ROW]
[ROW][C]52[/C][C]104[/C][C]55.9473684210526[/C][C]48.0526315789474[/C][/ROW]
[ROW][C]53[/C][C]86[/C][C]91.625[/C][C]-5.625[/C][/ROW]
[ROW][C]54[/C][C]57[/C][C]55.9473684210526[/C][C]1.05263157894737[/C][/ROW]
[ROW][C]55[/C][C]67[/C][C]55.9473684210526[/C][C]11.0526315789474[/C][/ROW]
[ROW][C]56[/C][C]44[/C][C]55.9473684210526[/C][C]-11.9473684210526[/C][/ROW]
[ROW][C]57[/C][C]53[/C][C]55.9473684210526[/C][C]-2.94736842105263[/C][/ROW]
[ROW][C]58[/C][C]26[/C][C]32.1538461538462[/C][C]-6.15384615384615[/C][/ROW]
[ROW][C]59[/C][C]67[/C][C]52.5714285714286[/C][C]14.4285714285714[/C][/ROW]
[ROW][C]60[/C][C]36[/C][C]74.7272727272727[/C][C]-38.7272727272727[/C][/ROW]
[ROW][C]61[/C][C]56[/C][C]55.9473684210526[/C][C]0.0526315789473699[/C][/ROW]
[ROW][C]62[/C][C]52[/C][C]55.9473684210526[/C][C]-3.94736842105263[/C][/ROW]
[ROW][C]63[/C][C]54[/C][C]55.9473684210526[/C][C]-1.94736842105263[/C][/ROW]
[ROW][C]64[/C][C]61[/C][C]55.9473684210526[/C][C]5.05263157894737[/C][/ROW]
[ROW][C]65[/C][C]27[/C][C]32.1538461538462[/C][C]-5.15384615384615[/C][/ROW]
[ROW][C]66[/C][C]58[/C][C]55.9473684210526[/C][C]2.05263157894737[/C][/ROW]
[ROW][C]67[/C][C]76[/C][C]74.7272727272727[/C][C]1.27272727272727[/C][/ROW]
[ROW][C]68[/C][C]93[/C][C]80.1428571428571[/C][C]12.8571428571429[/C][/ROW]
[ROW][C]69[/C][C]59[/C][C]52.5714285714286[/C][C]6.42857142857143[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]9.85714285714286[/C][C]-4.85714285714286[/C][/ROW]
[ROW][C]71[/C][C]57[/C][C]80.1428571428571[/C][C]-23.1428571428571[/C][/ROW]
[ROW][C]72[/C][C]42[/C][C]55.9473684210526[/C][C]-13.9473684210526[/C][/ROW]
[ROW][C]73[/C][C]88[/C][C]91.625[/C][C]-3.625[/C][/ROW]
[ROW][C]74[/C][C]53[/C][C]55.9473684210526[/C][C]-2.94736842105263[/C][/ROW]
[ROW][C]75[/C][C]81[/C][C]74.7272727272727[/C][C]6.27272727272727[/C][/ROW]
[ROW][C]76[/C][C]35[/C][C]55.9473684210526[/C][C]-20.9473684210526[/C][/ROW]
[ROW][C]77[/C][C]102[/C][C]74.7272727272727[/C][C]27.2727272727273[/C][/ROW]
[ROW][C]78[/C][C]71[/C][C]80.1428571428571[/C][C]-9.14285714285714[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]32.1538461538462[/C][C]-4.15384615384615[/C][/ROW]
[ROW][C]80[/C][C]34[/C][C]32.1538461538462[/C][C]1.84615384615385[/C][/ROW]
[ROW][C]81[/C][C]54[/C][C]52.5714285714286[/C][C]1.42857142857143[/C][/ROW]
[ROW][C]82[/C][C]49[/C][C]55.9473684210526[/C][C]-6.94736842105263[/C][/ROW]
[ROW][C]83[/C][C]30[/C][C]32.1538461538462[/C][C]-2.15384615384615[/C][/ROW]
[ROW][C]84[/C][C]57[/C][C]55.9473684210526[/C][C]1.05263157894737[/C][/ROW]
[ROW][C]85[/C][C]54[/C][C]32.1538461538462[/C][C]21.8461538461538[/C][/ROW]
[ROW][C]86[/C][C]38[/C][C]55.9473684210526[/C][C]-17.9473684210526[/C][/ROW]
[ROW][C]87[/C][C]63[/C][C]91.625[/C][C]-28.625[/C][/ROW]
[ROW][C]88[/C][C]58[/C][C]32.1538461538462[/C][C]25.8461538461538[/C][/ROW]
[ROW][C]89[/C][C]46[/C][C]55.9473684210526[/C][C]-9.94736842105263[/C][/ROW]
[ROW][C]90[/C][C]46[/C][C]55.9473684210526[/C][C]-9.94736842105263[/C][/ROW]
[ROW][C]91[/C][C]51[/C][C]55.9473684210526[/C][C]-4.94736842105263[/C][/ROW]
[ROW][C]92[/C][C]87[/C][C]80.1428571428571[/C][C]6.85714285714286[/C][/ROW]
[ROW][C]93[/C][C]39[/C][C]32.1538461538462[/C][C]6.84615384615385[/C][/ROW]
[ROW][C]94[/C][C]28[/C][C]55.9473684210526[/C][C]-27.9473684210526[/C][/ROW]
[ROW][C]95[/C][C]26[/C][C]32.1538461538462[/C][C]-6.15384615384615[/C][/ROW]
[ROW][C]96[/C][C]52[/C][C]55.9473684210526[/C][C]-3.94736842105263[/C][/ROW]
[ROW][C]97[/C][C]96[/C][C]80.1428571428571[/C][C]15.8571428571429[/C][/ROW]
[ROW][C]98[/C][C]13[/C][C]9.85714285714286[/C][C]3.14285714285714[/C][/ROW]
[ROW][C]99[/C][C]43[/C][C]74.7272727272727[/C][C]-31.7272727272727[/C][/ROW]
[ROW][C]100[/C][C]42[/C][C]55.9473684210526[/C][C]-13.9473684210526[/C][/ROW]
[ROW][C]101[/C][C]30[/C][C]32.1538461538462[/C][C]-2.15384615384615[/C][/ROW]
[ROW][C]102[/C][C]59[/C][C]32.1538461538462[/C][C]26.8461538461538[/C][/ROW]
[ROW][C]103[/C][C]73[/C][C]55.9473684210526[/C][C]17.0526315789474[/C][/ROW]
[ROW][C]104[/C][C]40[/C][C]55.9473684210526[/C][C]-15.9473684210526[/C][/ROW]
[ROW][C]105[/C][C]36[/C][C]32.1538461538462[/C][C]3.84615384615385[/C][/ROW]
[ROW][C]106[/C][C]2[/C][C]1.07142857142857[/C][C]0.928571428571429[/C][/ROW]
[ROW][C]107[/C][C]103[/C][C]55.9473684210526[/C][C]47.0526315789474[/C][/ROW]
[ROW][C]108[/C][C]30[/C][C]32.1538461538462[/C][C]-2.15384615384615[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]1.07142857142857[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]110[/C][C]46[/C][C]52.5714285714286[/C][C]-6.57142857142857[/C][/ROW]
[ROW][C]111[/C][C]25[/C][C]32.1538461538462[/C][C]-7.15384615384615[/C][/ROW]
[ROW][C]112[/C][C]59[/C][C]55.9473684210526[/C][C]3.05263157894737[/C][/ROW]
[ROW][C]113[/C][C]60[/C][C]55.9473684210526[/C][C]4.05263157894737[/C][/ROW]
[ROW][C]114[/C][C]36[/C][C]32.1538461538462[/C][C]3.84615384615385[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]1.07142857142857[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]1.07142857142857[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]117[/C][C]45[/C][C]52.5714285714286[/C][C]-7.57142857142857[/C][/ROW]
[ROW][C]118[/C][C]79[/C][C]80.1428571428571[/C][C]-1.14285714285714[/C][/ROW]
[ROW][C]119[/C][C]30[/C][C]55.9473684210526[/C][C]-25.9473684210526[/C][/ROW]
[ROW][C]120[/C][C]43[/C][C]32.1538461538462[/C][C]10.8461538461538[/C][/ROW]
[ROW][C]121[/C][C]7[/C][C]1.07142857142857[/C][C]5.92857142857143[/C][/ROW]
[ROW][C]122[/C][C]80[/C][C]55.9473684210526[/C][C]24.0526315789474[/C][/ROW]
[ROW][C]123[/C][C]32[/C][C]32.1538461538462[/C][C]-0.153846153846153[/C][/ROW]
[ROW][C]124[/C][C]84[/C][C]55.9473684210526[/C][C]28.0526315789474[/C][/ROW]
[ROW][C]125[/C][C]3[/C][C]9.85714285714286[/C][C]-6.85714285714286[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]9.85714285714286[/C][C]0.142857142857142[/C][/ROW]
[ROW][C]127[/C][C]47[/C][C]55.9473684210526[/C][C]-8.94736842105263[/C][/ROW]
[ROW][C]128[/C][C]35[/C][C]32.1538461538462[/C][C]2.84615384615385[/C][/ROW]
[ROW][C]129[/C][C]54[/C][C]55.9473684210526[/C][C]-1.94736842105263[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]1.07142857142857[/C][C]-0.0714285714285714[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]1.07142857142857[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]132[/C][C]46[/C][C]55.9473684210526[/C][C]-9.94736842105263[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]1.07142857142857[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]134[/C][C]51[/C][C]55.9473684210526[/C][C]-4.94736842105263[/C][/ROW]
[ROW][C]135[/C][C]5[/C][C]1.07142857142857[/C][C]3.92857142857143[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]9.85714285714286[/C][C]-1.85714285714286[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]1.07142857142857[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]138[/C][C]38[/C][C]32.1538461538462[/C][C]5.84615384615385[/C][/ROW]
[ROW][C]139[/C][C]21[/C][C]32.1538461538462[/C][C]-11.1538461538462[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]1.07142857142857[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]141[/C][C]0[/C][C]1.07142857142857[/C][C]-1.07142857142857[/C][/ROW]
[ROW][C]142[/C][C]18[/C][C]32.1538461538462[/C][C]-14.1538461538462[/C][/ROW]
[ROW][C]143[/C][C]53[/C][C]52.5714285714286[/C][C]0.428571428571431[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]32.1538461538462[/C][C]-15.1538461538462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159790&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159790&T=2

As an alternative you can also use a QR Code:

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

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	48	55.9473684210526	-7.94736842105263
2	58	55.9473684210526	2.05263157894737
3	0	1.07142857142857	-1.07142857142857
4	67	74.7272727272727	-7.72727272727273
5	83	80.1428571428571	2.85714285714286
6	137	91.625	45.375
7	65	55.9473684210526	9.05263157894737
8	86	55.9473684210526	30.0526315789474
9	62	55.9473684210526	6.05263157894737
10	72	80.1428571428571	-8.14285714285714
11	50	55.9473684210526	-5.94736842105263
12	88	80.1428571428571	7.85714285714286
13	62	55.9473684210526	6.05263157894737
14	79	80.1428571428571	-1.14285714285714
15	56	55.9473684210526	0.0526315789473699
16	54	55.9473684210526	-1.94736842105263
17	81	80.1428571428571	0.857142857142861
18	13	32.1538461538462	-19.1538461538462
19	74	74.7272727272727	-0.727272727272734
20	18	9.85714285714286	8.14285714285714
21	31	32.1538461538462	-1.15384615384615
22	99	91.625	7.375
23	38	55.9473684210526	-17.9473684210526
24	59	55.9473684210526	3.05263157894737
25	54	55.9473684210526	-1.94736842105263
26	63	55.9473684210526	7.05263157894737
27	66	55.9473684210526	10.0526315789474
28	90	80.1428571428571	9.85714285714286
29	72	74.7272727272727	-2.72727272727273
30	61	55.9473684210526	5.05263157894737
31	61	55.9473684210526	5.05263157894737
32	63	55.9473684210526	7.05263157894737
33	53	55.9473684210526	-2.94736842105263
34	120	74.7272727272727	45.2727272727273
35	73	80.1428571428571	-7.14285714285714
36	0	1.07142857142857	-1.07142857142857
37	54	55.9473684210526	-1.94736842105263
38	54	55.9473684210526	-1.94736842105263
39	46	55.9473684210526	-9.94736842105263
40	83	91.625	-8.625
41	106	91.625	14.375
42	44	55.9473684210526	-11.9473684210526
43	27	32.1538461538462	-5.15384615384615
44	73	80.1428571428571	-7.14285714285714
45	71	91.625	-20.625
46	44	52.5714285714286	-8.57142857142857
47	23	32.1538461538462	-9.15384615384615
48	78	74.7272727272727	3.27272727272727
49	60	55.9473684210526	4.05263157894737
50	73	74.7272727272727	-1.72727272727273
51	12	9.85714285714286	2.14285714285714
52	104	55.9473684210526	48.0526315789474
53	86	91.625	-5.625
54	57	55.9473684210526	1.05263157894737
55	67	55.9473684210526	11.0526315789474
56	44	55.9473684210526	-11.9473684210526
57	53	55.9473684210526	-2.94736842105263
58	26	32.1538461538462	-6.15384615384615
59	67	52.5714285714286	14.4285714285714
60	36	74.7272727272727	-38.7272727272727
61	56	55.9473684210526	0.0526315789473699
62	52	55.9473684210526	-3.94736842105263
63	54	55.9473684210526	-1.94736842105263
64	61	55.9473684210526	5.05263157894737
65	27	32.1538461538462	-5.15384615384615
66	58	55.9473684210526	2.05263157894737
67	76	74.7272727272727	1.27272727272727
68	93	80.1428571428571	12.8571428571429
69	59	52.5714285714286	6.42857142857143
70	5	9.85714285714286	-4.85714285714286
71	57	80.1428571428571	-23.1428571428571
72	42	55.9473684210526	-13.9473684210526
73	88	91.625	-3.625
74	53	55.9473684210526	-2.94736842105263
75	81	74.7272727272727	6.27272727272727
76	35	55.9473684210526	-20.9473684210526
77	102	74.7272727272727	27.2727272727273
78	71	80.1428571428571	-9.14285714285714
79	28	32.1538461538462	-4.15384615384615
80	34	32.1538461538462	1.84615384615385
81	54	52.5714285714286	1.42857142857143
82	49	55.9473684210526	-6.94736842105263
83	30	32.1538461538462	-2.15384615384615
84	57	55.9473684210526	1.05263157894737
85	54	32.1538461538462	21.8461538461538
86	38	55.9473684210526	-17.9473684210526
87	63	91.625	-28.625
88	58	32.1538461538462	25.8461538461538
89	46	55.9473684210526	-9.94736842105263
90	46	55.9473684210526	-9.94736842105263
91	51	55.9473684210526	-4.94736842105263
92	87	80.1428571428571	6.85714285714286
93	39	32.1538461538462	6.84615384615385
94	28	55.9473684210526	-27.9473684210526
95	26	32.1538461538462	-6.15384615384615
96	52	55.9473684210526	-3.94736842105263
97	96	80.1428571428571	15.8571428571429
98	13	9.85714285714286	3.14285714285714
99	43	74.7272727272727	-31.7272727272727
100	42	55.9473684210526	-13.9473684210526
101	30	32.1538461538462	-2.15384615384615
102	59	32.1538461538462	26.8461538461538
103	73	55.9473684210526	17.0526315789474
104	40	55.9473684210526	-15.9473684210526
105	36	32.1538461538462	3.84615384615385
106	2	1.07142857142857	0.928571428571429
107	103	55.9473684210526	47.0526315789474
108	30	32.1538461538462	-2.15384615384615
109	0	1.07142857142857	-1.07142857142857
110	46	52.5714285714286	-6.57142857142857
111	25	32.1538461538462	-7.15384615384615
112	59	55.9473684210526	3.05263157894737
113	60	55.9473684210526	4.05263157894737
114	36	32.1538461538462	3.84615384615385
115	0	1.07142857142857	-1.07142857142857
116	0	1.07142857142857	-1.07142857142857
117	45	52.5714285714286	-7.57142857142857
118	79	80.1428571428571	-1.14285714285714
119	30	55.9473684210526	-25.9473684210526
120	43	32.1538461538462	10.8461538461538
121	7	1.07142857142857	5.92857142857143
122	80	55.9473684210526	24.0526315789474
123	32	32.1538461538462	-0.153846153846153
124	84	55.9473684210526	28.0526315789474
125	3	9.85714285714286	-6.85714285714286
126	10	9.85714285714286	0.142857142857142
127	47	55.9473684210526	-8.94736842105263
128	35	32.1538461538462	2.84615384615385
129	54	55.9473684210526	-1.94736842105263
130	1	1.07142857142857	-0.0714285714285714
131	0	1.07142857142857	-1.07142857142857
132	46	55.9473684210526	-9.94736842105263
133	0	1.07142857142857	-1.07142857142857
134	51	55.9473684210526	-4.94736842105263
135	5	1.07142857142857	3.92857142857143
136	8	9.85714285714286	-1.85714285714286
137	0	1.07142857142857	-1.07142857142857
138	38	32.1538461538462	5.84615384615385
139	21	32.1538461538462	-11.1538461538462
140	0	1.07142857142857	-1.07142857142857
141	0	1.07142857142857	-1.07142857142857
142	18	32.1538461538462	-14.1538461538462
143	53	52.5714285714286	0.428571428571431
144	17	32.1538461538462	-15.1538461538462

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 3 ; par2 = none ; par3 = 0 ; par4 = no ;

Parameters (R input):

par1 = 7 ; par2 = none ; par3 = 0 ; par4 = no ;

R code (references can be found in the software module):

library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code