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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 13:07:13 -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/t1324577254tpemxx1zfni72jr.htm/, Retrieved Fri, 03 May 2024 06:49:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159800, Retrieved Fri, 03 May 2024 06:49:05 +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 18:07:13] [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	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159800&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159800&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159800&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	'Herman Ole Andreas Wold' @ wold.wessa.net

Goodness of Fit
Correlation	0.8688
R-squared	0.7548
RMSE	14674.0647

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8688 \tabularnewline
R-squared & 0.7548 \tabularnewline
RMSE & 14674.0647 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159800&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8688[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7548[/C][/ROW]
[ROW][C]RMSE[/C][C]14674.0647[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159800&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159800&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	0.8688
R-squared	0.7548
RMSE	14674.0647

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	23975	48725.25	-24750.25
2	85634	48725.25	36908.75
3	1929	2037.21052631579	-108.210526315789
4	36294	32277.0769230769	4016.92307692308
5	72255	100920	-28665
6	189748	100920	88828
7	61834	48725.25	13108.75
8	68167	48725.25	19441.75
9	38462	48725.25	-10263.25
10	101219	100920	299
11	43270	32277.0769230769	10992.9230769231
12	76183	71539	4644
13	31476	32277.0769230769	-801.076923076922
14	62157	71539	-9382
15	46261	48725.25	-2464.25
16	50063	48725.25	1337.75
17	64483	43458.1333333333	21024.8666666667
18	2341	15162.0833333333	-12821.0833333333
19	48149	69185	-21036
20	12743	15162.0833333333	-2419.08333333333
21	18743	15162.0833333333	3580.91666666667
22	97057	100920	-3863
23	17675	48725.25	-31050.25
24	33106	32277.0769230769	828.923076923078
25	53311	48725.25	4585.75
26	42754	48725.25	-5971.25
27	59056	71539	-12483
28	101621	71539	30082
29	118120	100920	17200
30	79572	71539	8033
31	42744	48725.25	-5981.25
32	65931	48725.25	17205.75
33	38575	32277.0769230769	6297.92307692308
34	28795	48725.25	-19930.25
35	94440	100920	-6480
36	0	2037.21052631579	-2037.21052631579
37	38229	48725.25	-10496.25
38	31972	32277.0769230769	-305.076923076922
39	40071	48725.25	-8654.25
40	132480	100920	31560
41	62797	100920	-38123
42	40429	69185	-28756
43	45545	43458.1333333333	2086.86666666667
44	57568	71539	-13971
45	39019	43458.1333333333	-4439.13333333333
46	53866	69185	-15319
47	38345	32277.0769230769	6067.92307692308
48	50210	48725.25	1484.75
49	80947	69185	11762
50	43461	48725.25	-5264.25
51	14812	15162.0833333333	-350.083333333334
52	37819	48725.25	-10906.25
53	102738	100920	1818
54	54509	48725.25	5783.75
55	62956	48725.25	14230.75
56	55411	48725.25	6685.75
57	50611	48725.25	1885.75
58	26692	32277.0769230769	-5585.07692307692
59	60056	69185	-9129
60	25155	48725.25	-23570.25
61	42840	48725.25	-5885.25
62	39358	32277.0769230769	7080.92307692308
63	47241	43458.1333333333	3782.86666666667
64	49611	32277.0769230769	17333.9230769231
65	41833	48725.25	-6892.25
66	48930	43458.1333333333	5471.86666666667
67	110600	69185	41415
68	52235	43458.1333333333	8776.86666666667
69	53986	43458.1333333333	10527.8666666667
70	4105	2037.21052631579	2067.78947368421
71	59331	100920	-41589
72	47796	48725.25	-929.25
73	38302	43458.1333333333	-5156.13333333333
74	14063	15162.0833333333	-1099.08333333333
75	54414	48725.25	5688.75
76	9903	15162.0833333333	-5259.08333333333
77	53987	48725.25	5261.75
78	88937	71539	17398
79	21928	32277.0769230769	-10349.0769230769
80	29487	43458.1333333333	-13971.1333333333
81	35334	43458.1333333333	-8124.13333333333
82	57596	48725.25	8870.75
83	29750	32277.0769230769	-2527.07692307692
84	41029	43458.1333333333	-2429.13333333333
85	12416	15162.0833333333	-2746.08333333333
86	51158	48725.25	2432.75
87	79935	100920	-20985
88	26552	32277.0769230769	-5725.07692307692
89	25807	32277.0769230769	-6470.07692307692
90	50620	48725.25	1894.75
91	61467	48725.25	12741.75
92	65292	71539	-6247
93	55516	48725.25	6790.75
94	42006	32277.0769230769	9728.92307692308
95	26273	43458.1333333333	-17185.1333333333
96	90248	69185	21063
97	61476	71539	-10063
98	9604	2037.21052631579	7566.78947368421
99	45108	48725.25	-3617.25
100	47232	48725.25	-1493.25
101	3439	2037.21052631579	1401.78947368421
102	30553	32277.0769230769	-1724.07692307692
103	24751	32277.0769230769	-7526.07692307692
104	34458	32277.0769230769	2180.92307692308
105	24649	32277.0769230769	-7628.07692307692
106	2342	2037.21052631579	304.789473684211
107	52739	48725.25	4013.75
108	6245	15162.0833333333	-8917.08333333333
109	0	2037.21052631579	-2037.21052631579
110	35381	43458.1333333333	-8077.13333333333
111	19595	32277.0769230769	-12682.0769230769
112	50848	48725.25	2122.75
113	39443	43458.1333333333	-4015.13333333333
114	27023	15162.0833333333	11860.9166666667
115	0	2037.21052631579	-2037.21052631579
116	0	2037.21052631579	-2037.21052631579
117	61022	48725.25	12296.75
118	63528	71539	-8011
119	34835	48725.25	-13890.25
120	37172	32277.0769230769	4894.92307692308
121	13	2037.21052631579	-2024.21052631579
122	62548	48725.25	13822.75
123	31334	32277.0769230769	-943.076923076922
124	20839	32277.0769230769	-11438.0769230769
125	5084	15162.0833333333	-10078.0833333333
126	9927	2037.21052631579	7889.78947368421
127	53229	48725.25	4503.75
128	29877	32277.0769230769	-2400.07692307692
129	37310	48725.25	-11415.25
130	0	2037.21052631579	-2037.21052631579
131	0	2037.21052631579	-2037.21052631579
132	50067	48725.25	1341.75
133	0	2037.21052631579	-2037.21052631579
134	47708	48725.25	-1017.25
135	0	2037.21052631579	-2037.21052631579
136	6012	2037.21052631579	3974.78947368421
137	0	2037.21052631579	-2037.21052631579
138	27749	32277.0769230769	-4528.07692307692
139	47555	15162.0833333333	32392.9166666667
140	0	2037.21052631579	-2037.21052631579
141	1336	2037.21052631579	-701.210526315789
142	11017	15162.0833333333	-4145.08333333333
143	55184	43458.1333333333	11725.8666666667
144	43485	32277.0769230769	11207.9230769231

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 23975 & 48725.25 & -24750.25 \tabularnewline
2 & 85634 & 48725.25 & 36908.75 \tabularnewline
3 & 1929 & 2037.21052631579 & -108.210526315789 \tabularnewline
4 & 36294 & 32277.0769230769 & 4016.92307692308 \tabularnewline
5 & 72255 & 100920 & -28665 \tabularnewline
6 & 189748 & 100920 & 88828 \tabularnewline
7 & 61834 & 48725.25 & 13108.75 \tabularnewline
8 & 68167 & 48725.25 & 19441.75 \tabularnewline
9 & 38462 & 48725.25 & -10263.25 \tabularnewline
10 & 101219 & 100920 & 299 \tabularnewline
11 & 43270 & 32277.0769230769 & 10992.9230769231 \tabularnewline
12 & 76183 & 71539 & 4644 \tabularnewline
13 & 31476 & 32277.0769230769 & -801.076923076922 \tabularnewline
14 & 62157 & 71539 & -9382 \tabularnewline
15 & 46261 & 48725.25 & -2464.25 \tabularnewline
16 & 50063 & 48725.25 & 1337.75 \tabularnewline
17 & 64483 & 43458.1333333333 & 21024.8666666667 \tabularnewline
18 & 2341 & 15162.0833333333 & -12821.0833333333 \tabularnewline
19 & 48149 & 69185 & -21036 \tabularnewline
20 & 12743 & 15162.0833333333 & -2419.08333333333 \tabularnewline
21 & 18743 & 15162.0833333333 & 3580.91666666667 \tabularnewline
22 & 97057 & 100920 & -3863 \tabularnewline
23 & 17675 & 48725.25 & -31050.25 \tabularnewline
24 & 33106 & 32277.0769230769 & 828.923076923078 \tabularnewline
25 & 53311 & 48725.25 & 4585.75 \tabularnewline
26 & 42754 & 48725.25 & -5971.25 \tabularnewline
27 & 59056 & 71539 & -12483 \tabularnewline
28 & 101621 & 71539 & 30082 \tabularnewline
29 & 118120 & 100920 & 17200 \tabularnewline
30 & 79572 & 71539 & 8033 \tabularnewline
31 & 42744 & 48725.25 & -5981.25 \tabularnewline
32 & 65931 & 48725.25 & 17205.75 \tabularnewline
33 & 38575 & 32277.0769230769 & 6297.92307692308 \tabularnewline
34 & 28795 & 48725.25 & -19930.25 \tabularnewline
35 & 94440 & 100920 & -6480 \tabularnewline
36 & 0 & 2037.21052631579 & -2037.21052631579 \tabularnewline
37 & 38229 & 48725.25 & -10496.25 \tabularnewline
38 & 31972 & 32277.0769230769 & -305.076923076922 \tabularnewline
39 & 40071 & 48725.25 & -8654.25 \tabularnewline
40 & 132480 & 100920 & 31560 \tabularnewline
41 & 62797 & 100920 & -38123 \tabularnewline
42 & 40429 & 69185 & -28756 \tabularnewline
43 & 45545 & 43458.1333333333 & 2086.86666666667 \tabularnewline
44 & 57568 & 71539 & -13971 \tabularnewline
45 & 39019 & 43458.1333333333 & -4439.13333333333 \tabularnewline
46 & 53866 & 69185 & -15319 \tabularnewline
47 & 38345 & 32277.0769230769 & 6067.92307692308 \tabularnewline
48 & 50210 & 48725.25 & 1484.75 \tabularnewline
49 & 80947 & 69185 & 11762 \tabularnewline
50 & 43461 & 48725.25 & -5264.25 \tabularnewline
51 & 14812 & 15162.0833333333 & -350.083333333334 \tabularnewline
52 & 37819 & 48725.25 & -10906.25 \tabularnewline
53 & 102738 & 100920 & 1818 \tabularnewline
54 & 54509 & 48725.25 & 5783.75 \tabularnewline
55 & 62956 & 48725.25 & 14230.75 \tabularnewline
56 & 55411 & 48725.25 & 6685.75 \tabularnewline
57 & 50611 & 48725.25 & 1885.75 \tabularnewline
58 & 26692 & 32277.0769230769 & -5585.07692307692 \tabularnewline
59 & 60056 & 69185 & -9129 \tabularnewline
60 & 25155 & 48725.25 & -23570.25 \tabularnewline
61 & 42840 & 48725.25 & -5885.25 \tabularnewline
62 & 39358 & 32277.0769230769 & 7080.92307692308 \tabularnewline
63 & 47241 & 43458.1333333333 & 3782.86666666667 \tabularnewline
64 & 49611 & 32277.0769230769 & 17333.9230769231 \tabularnewline
65 & 41833 & 48725.25 & -6892.25 \tabularnewline
66 & 48930 & 43458.1333333333 & 5471.86666666667 \tabularnewline
67 & 110600 & 69185 & 41415 \tabularnewline
68 & 52235 & 43458.1333333333 & 8776.86666666667 \tabularnewline
69 & 53986 & 43458.1333333333 & 10527.8666666667 \tabularnewline
70 & 4105 & 2037.21052631579 & 2067.78947368421 \tabularnewline
71 & 59331 & 100920 & -41589 \tabularnewline
72 & 47796 & 48725.25 & -929.25 \tabularnewline
73 & 38302 & 43458.1333333333 & -5156.13333333333 \tabularnewline
74 & 14063 & 15162.0833333333 & -1099.08333333333 \tabularnewline
75 & 54414 & 48725.25 & 5688.75 \tabularnewline
76 & 9903 & 15162.0833333333 & -5259.08333333333 \tabularnewline
77 & 53987 & 48725.25 & 5261.75 \tabularnewline
78 & 88937 & 71539 & 17398 \tabularnewline
79 & 21928 & 32277.0769230769 & -10349.0769230769 \tabularnewline
80 & 29487 & 43458.1333333333 & -13971.1333333333 \tabularnewline
81 & 35334 & 43458.1333333333 & -8124.13333333333 \tabularnewline
82 & 57596 & 48725.25 & 8870.75 \tabularnewline
83 & 29750 & 32277.0769230769 & -2527.07692307692 \tabularnewline
84 & 41029 & 43458.1333333333 & -2429.13333333333 \tabularnewline
85 & 12416 & 15162.0833333333 & -2746.08333333333 \tabularnewline
86 & 51158 & 48725.25 & 2432.75 \tabularnewline
87 & 79935 & 100920 & -20985 \tabularnewline
88 & 26552 & 32277.0769230769 & -5725.07692307692 \tabularnewline
89 & 25807 & 32277.0769230769 & -6470.07692307692 \tabularnewline
90 & 50620 & 48725.25 & 1894.75 \tabularnewline
91 & 61467 & 48725.25 & 12741.75 \tabularnewline
92 & 65292 & 71539 & -6247 \tabularnewline
93 & 55516 & 48725.25 & 6790.75 \tabularnewline
94 & 42006 & 32277.0769230769 & 9728.92307692308 \tabularnewline
95 & 26273 & 43458.1333333333 & -17185.1333333333 \tabularnewline
96 & 90248 & 69185 & 21063 \tabularnewline
97 & 61476 & 71539 & -10063 \tabularnewline
98 & 9604 & 2037.21052631579 & 7566.78947368421 \tabularnewline
99 & 45108 & 48725.25 & -3617.25 \tabularnewline
100 & 47232 & 48725.25 & -1493.25 \tabularnewline
101 & 3439 & 2037.21052631579 & 1401.78947368421 \tabularnewline
102 & 30553 & 32277.0769230769 & -1724.07692307692 \tabularnewline
103 & 24751 & 32277.0769230769 & -7526.07692307692 \tabularnewline
104 & 34458 & 32277.0769230769 & 2180.92307692308 \tabularnewline
105 & 24649 & 32277.0769230769 & -7628.07692307692 \tabularnewline
106 & 2342 & 2037.21052631579 & 304.789473684211 \tabularnewline
107 & 52739 & 48725.25 & 4013.75 \tabularnewline
108 & 6245 & 15162.0833333333 & -8917.08333333333 \tabularnewline
109 & 0 & 2037.21052631579 & -2037.21052631579 \tabularnewline
110 & 35381 & 43458.1333333333 & -8077.13333333333 \tabularnewline
111 & 19595 & 32277.0769230769 & -12682.0769230769 \tabularnewline
112 & 50848 & 48725.25 & 2122.75 \tabularnewline
113 & 39443 & 43458.1333333333 & -4015.13333333333 \tabularnewline
114 & 27023 & 15162.0833333333 & 11860.9166666667 \tabularnewline
115 & 0 & 2037.21052631579 & -2037.21052631579 \tabularnewline
116 & 0 & 2037.21052631579 & -2037.21052631579 \tabularnewline
117 & 61022 & 48725.25 & 12296.75 \tabularnewline
118 & 63528 & 71539 & -8011 \tabularnewline
119 & 34835 & 48725.25 & -13890.25 \tabularnewline
120 & 37172 & 32277.0769230769 & 4894.92307692308 \tabularnewline
121 & 13 & 2037.21052631579 & -2024.21052631579 \tabularnewline
122 & 62548 & 48725.25 & 13822.75 \tabularnewline
123 & 31334 & 32277.0769230769 & -943.076923076922 \tabularnewline
124 & 20839 & 32277.0769230769 & -11438.0769230769 \tabularnewline
125 & 5084 & 15162.0833333333 & -10078.0833333333 \tabularnewline
126 & 9927 & 2037.21052631579 & 7889.78947368421 \tabularnewline
127 & 53229 & 48725.25 & 4503.75 \tabularnewline
128 & 29877 & 32277.0769230769 & -2400.07692307692 \tabularnewline
129 & 37310 & 48725.25 & -11415.25 \tabularnewline
130 & 0 & 2037.21052631579 & -2037.21052631579 \tabularnewline
131 & 0 & 2037.21052631579 & -2037.21052631579 \tabularnewline
132 & 50067 & 48725.25 & 1341.75 \tabularnewline
133 & 0 & 2037.21052631579 & -2037.21052631579 \tabularnewline
134 & 47708 & 48725.25 & -1017.25 \tabularnewline
135 & 0 & 2037.21052631579 & -2037.21052631579 \tabularnewline
136 & 6012 & 2037.21052631579 & 3974.78947368421 \tabularnewline
137 & 0 & 2037.21052631579 & -2037.21052631579 \tabularnewline
138 & 27749 & 32277.0769230769 & -4528.07692307692 \tabularnewline
139 & 47555 & 15162.0833333333 & 32392.9166666667 \tabularnewline
140 & 0 & 2037.21052631579 & -2037.21052631579 \tabularnewline
141 & 1336 & 2037.21052631579 & -701.210526315789 \tabularnewline
142 & 11017 & 15162.0833333333 & -4145.08333333333 \tabularnewline
143 & 55184 & 43458.1333333333 & 11725.8666666667 \tabularnewline
144 & 43485 & 32277.0769230769 & 11207.9230769231 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159800&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]23975[/C][C]48725.25[/C][C]-24750.25[/C][/ROW]
[ROW][C]2[/C][C]85634[/C][C]48725.25[/C][C]36908.75[/C][/ROW]
[ROW][C]3[/C][C]1929[/C][C]2037.21052631579[/C][C]-108.210526315789[/C][/ROW]
[ROW][C]4[/C][C]36294[/C][C]32277.0769230769[/C][C]4016.92307692308[/C][/ROW]
[ROW][C]5[/C][C]72255[/C][C]100920[/C][C]-28665[/C][/ROW]
[ROW][C]6[/C][C]189748[/C][C]100920[/C][C]88828[/C][/ROW]
[ROW][C]7[/C][C]61834[/C][C]48725.25[/C][C]13108.75[/C][/ROW]
[ROW][C]8[/C][C]68167[/C][C]48725.25[/C][C]19441.75[/C][/ROW]
[ROW][C]9[/C][C]38462[/C][C]48725.25[/C][C]-10263.25[/C][/ROW]
[ROW][C]10[/C][C]101219[/C][C]100920[/C][C]299[/C][/ROW]
[ROW][C]11[/C][C]43270[/C][C]32277.0769230769[/C][C]10992.9230769231[/C][/ROW]
[ROW][C]12[/C][C]76183[/C][C]71539[/C][C]4644[/C][/ROW]
[ROW][C]13[/C][C]31476[/C][C]32277.0769230769[/C][C]-801.076923076922[/C][/ROW]
[ROW][C]14[/C][C]62157[/C][C]71539[/C][C]-9382[/C][/ROW]
[ROW][C]15[/C][C]46261[/C][C]48725.25[/C][C]-2464.25[/C][/ROW]
[ROW][C]16[/C][C]50063[/C][C]48725.25[/C][C]1337.75[/C][/ROW]
[ROW][C]17[/C][C]64483[/C][C]43458.1333333333[/C][C]21024.8666666667[/C][/ROW]
[ROW][C]18[/C][C]2341[/C][C]15162.0833333333[/C][C]-12821.0833333333[/C][/ROW]
[ROW][C]19[/C][C]48149[/C][C]69185[/C][C]-21036[/C][/ROW]
[ROW][C]20[/C][C]12743[/C][C]15162.0833333333[/C][C]-2419.08333333333[/C][/ROW]
[ROW][C]21[/C][C]18743[/C][C]15162.0833333333[/C][C]3580.91666666667[/C][/ROW]
[ROW][C]22[/C][C]97057[/C][C]100920[/C][C]-3863[/C][/ROW]
[ROW][C]23[/C][C]17675[/C][C]48725.25[/C][C]-31050.25[/C][/ROW]
[ROW][C]24[/C][C]33106[/C][C]32277.0769230769[/C][C]828.923076923078[/C][/ROW]
[ROW][C]25[/C][C]53311[/C][C]48725.25[/C][C]4585.75[/C][/ROW]
[ROW][C]26[/C][C]42754[/C][C]48725.25[/C][C]-5971.25[/C][/ROW]
[ROW][C]27[/C][C]59056[/C][C]71539[/C][C]-12483[/C][/ROW]
[ROW][C]28[/C][C]101621[/C][C]71539[/C][C]30082[/C][/ROW]
[ROW][C]29[/C][C]118120[/C][C]100920[/C][C]17200[/C][/ROW]
[ROW][C]30[/C][C]79572[/C][C]71539[/C][C]8033[/C][/ROW]
[ROW][C]31[/C][C]42744[/C][C]48725.25[/C][C]-5981.25[/C][/ROW]
[ROW][C]32[/C][C]65931[/C][C]48725.25[/C][C]17205.75[/C][/ROW]
[ROW][C]33[/C][C]38575[/C][C]32277.0769230769[/C][C]6297.92307692308[/C][/ROW]
[ROW][C]34[/C][C]28795[/C][C]48725.25[/C][C]-19930.25[/C][/ROW]
[ROW][C]35[/C][C]94440[/C][C]100920[/C][C]-6480[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]2037.21052631579[/C][C]-2037.21052631579[/C][/ROW]
[ROW][C]37[/C][C]38229[/C][C]48725.25[/C][C]-10496.25[/C][/ROW]
[ROW][C]38[/C][C]31972[/C][C]32277.0769230769[/C][C]-305.076923076922[/C][/ROW]
[ROW][C]39[/C][C]40071[/C][C]48725.25[/C][C]-8654.25[/C][/ROW]
[ROW][C]40[/C][C]132480[/C][C]100920[/C][C]31560[/C][/ROW]
[ROW][C]41[/C][C]62797[/C][C]100920[/C][C]-38123[/C][/ROW]
[ROW][C]42[/C][C]40429[/C][C]69185[/C][C]-28756[/C][/ROW]
[ROW][C]43[/C][C]45545[/C][C]43458.1333333333[/C][C]2086.86666666667[/C][/ROW]
[ROW][C]44[/C][C]57568[/C][C]71539[/C][C]-13971[/C][/ROW]
[ROW][C]45[/C][C]39019[/C][C]43458.1333333333[/C][C]-4439.13333333333[/C][/ROW]
[ROW][C]46[/C][C]53866[/C][C]69185[/C][C]-15319[/C][/ROW]
[ROW][C]47[/C][C]38345[/C][C]32277.0769230769[/C][C]6067.92307692308[/C][/ROW]
[ROW][C]48[/C][C]50210[/C][C]48725.25[/C][C]1484.75[/C][/ROW]
[ROW][C]49[/C][C]80947[/C][C]69185[/C][C]11762[/C][/ROW]
[ROW][C]50[/C][C]43461[/C][C]48725.25[/C][C]-5264.25[/C][/ROW]
[ROW][C]51[/C][C]14812[/C][C]15162.0833333333[/C][C]-350.083333333334[/C][/ROW]
[ROW][C]52[/C][C]37819[/C][C]48725.25[/C][C]-10906.25[/C][/ROW]
[ROW][C]53[/C][C]102738[/C][C]100920[/C][C]1818[/C][/ROW]
[ROW][C]54[/C][C]54509[/C][C]48725.25[/C][C]5783.75[/C][/ROW]
[ROW][C]55[/C][C]62956[/C][C]48725.25[/C][C]14230.75[/C][/ROW]
[ROW][C]56[/C][C]55411[/C][C]48725.25[/C][C]6685.75[/C][/ROW]
[ROW][C]57[/C][C]50611[/C][C]48725.25[/C][C]1885.75[/C][/ROW]
[ROW][C]58[/C][C]26692[/C][C]32277.0769230769[/C][C]-5585.07692307692[/C][/ROW]
[ROW][C]59[/C][C]60056[/C][C]69185[/C][C]-9129[/C][/ROW]
[ROW][C]60[/C][C]25155[/C][C]48725.25[/C][C]-23570.25[/C][/ROW]
[ROW][C]61[/C][C]42840[/C][C]48725.25[/C][C]-5885.25[/C][/ROW]
[ROW][C]62[/C][C]39358[/C][C]32277.0769230769[/C][C]7080.92307692308[/C][/ROW]
[ROW][C]63[/C][C]47241[/C][C]43458.1333333333[/C][C]3782.86666666667[/C][/ROW]
[ROW][C]64[/C][C]49611[/C][C]32277.0769230769[/C][C]17333.9230769231[/C][/ROW]
[ROW][C]65[/C][C]41833[/C][C]48725.25[/C][C]-6892.25[/C][/ROW]
[ROW][C]66[/C][C]48930[/C][C]43458.1333333333[/C][C]5471.86666666667[/C][/ROW]
[ROW][C]67[/C][C]110600[/C][C]69185[/C][C]41415[/C][/ROW]
[ROW][C]68[/C][C]52235[/C][C]43458.1333333333[/C][C]8776.86666666667[/C][/ROW]
[ROW][C]69[/C][C]53986[/C][C]43458.1333333333[/C][C]10527.8666666667[/C][/ROW]
[ROW][C]70[/C][C]4105[/C][C]2037.21052631579[/C][C]2067.78947368421[/C][/ROW]
[ROW][C]71[/C][C]59331[/C][C]100920[/C][C]-41589[/C][/ROW]
[ROW][C]72[/C][C]47796[/C][C]48725.25[/C][C]-929.25[/C][/ROW]
[ROW][C]73[/C][C]38302[/C][C]43458.1333333333[/C][C]-5156.13333333333[/C][/ROW]
[ROW][C]74[/C][C]14063[/C][C]15162.0833333333[/C][C]-1099.08333333333[/C][/ROW]
[ROW][C]75[/C][C]54414[/C][C]48725.25[/C][C]5688.75[/C][/ROW]
[ROW][C]76[/C][C]9903[/C][C]15162.0833333333[/C][C]-5259.08333333333[/C][/ROW]
[ROW][C]77[/C][C]53987[/C][C]48725.25[/C][C]5261.75[/C][/ROW]
[ROW][C]78[/C][C]88937[/C][C]71539[/C][C]17398[/C][/ROW]
[ROW][C]79[/C][C]21928[/C][C]32277.0769230769[/C][C]-10349.0769230769[/C][/ROW]
[ROW][C]80[/C][C]29487[/C][C]43458.1333333333[/C][C]-13971.1333333333[/C][/ROW]
[ROW][C]81[/C][C]35334[/C][C]43458.1333333333[/C][C]-8124.13333333333[/C][/ROW]
[ROW][C]82[/C][C]57596[/C][C]48725.25[/C][C]8870.75[/C][/ROW]
[ROW][C]83[/C][C]29750[/C][C]32277.0769230769[/C][C]-2527.07692307692[/C][/ROW]
[ROW][C]84[/C][C]41029[/C][C]43458.1333333333[/C][C]-2429.13333333333[/C][/ROW]
[ROW][C]85[/C][C]12416[/C][C]15162.0833333333[/C][C]-2746.08333333333[/C][/ROW]
[ROW][C]86[/C][C]51158[/C][C]48725.25[/C][C]2432.75[/C][/ROW]
[ROW][C]87[/C][C]79935[/C][C]100920[/C][C]-20985[/C][/ROW]
[ROW][C]88[/C][C]26552[/C][C]32277.0769230769[/C][C]-5725.07692307692[/C][/ROW]
[ROW][C]89[/C][C]25807[/C][C]32277.0769230769[/C][C]-6470.07692307692[/C][/ROW]
[ROW][C]90[/C][C]50620[/C][C]48725.25[/C][C]1894.75[/C][/ROW]
[ROW][C]91[/C][C]61467[/C][C]48725.25[/C][C]12741.75[/C][/ROW]
[ROW][C]92[/C][C]65292[/C][C]71539[/C][C]-6247[/C][/ROW]
[ROW][C]93[/C][C]55516[/C][C]48725.25[/C][C]6790.75[/C][/ROW]
[ROW][C]94[/C][C]42006[/C][C]32277.0769230769[/C][C]9728.92307692308[/C][/ROW]
[ROW][C]95[/C][C]26273[/C][C]43458.1333333333[/C][C]-17185.1333333333[/C][/ROW]
[ROW][C]96[/C][C]90248[/C][C]69185[/C][C]21063[/C][/ROW]
[ROW][C]97[/C][C]61476[/C][C]71539[/C][C]-10063[/C][/ROW]
[ROW][C]98[/C][C]9604[/C][C]2037.21052631579[/C][C]7566.78947368421[/C][/ROW]
[ROW][C]99[/C][C]45108[/C][C]48725.25[/C][C]-3617.25[/C][/ROW]
[ROW][C]100[/C][C]47232[/C][C]48725.25[/C][C]-1493.25[/C][/ROW]
[ROW][C]101[/C][C]3439[/C][C]2037.21052631579[/C][C]1401.78947368421[/C][/ROW]
[ROW][C]102[/C][C]30553[/C][C]32277.0769230769[/C][C]-1724.07692307692[/C][/ROW]
[ROW][C]103[/C][C]24751[/C][C]32277.0769230769[/C][C]-7526.07692307692[/C][/ROW]
[ROW][C]104[/C][C]34458[/C][C]32277.0769230769[/C][C]2180.92307692308[/C][/ROW]
[ROW][C]105[/C][C]24649[/C][C]32277.0769230769[/C][C]-7628.07692307692[/C][/ROW]
[ROW][C]106[/C][C]2342[/C][C]2037.21052631579[/C][C]304.789473684211[/C][/ROW]
[ROW][C]107[/C][C]52739[/C][C]48725.25[/C][C]4013.75[/C][/ROW]
[ROW][C]108[/C][C]6245[/C][C]15162.0833333333[/C][C]-8917.08333333333[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]2037.21052631579[/C][C]-2037.21052631579[/C][/ROW]
[ROW][C]110[/C][C]35381[/C][C]43458.1333333333[/C][C]-8077.13333333333[/C][/ROW]
[ROW][C]111[/C][C]19595[/C][C]32277.0769230769[/C][C]-12682.0769230769[/C][/ROW]
[ROW][C]112[/C][C]50848[/C][C]48725.25[/C][C]2122.75[/C][/ROW]
[ROW][C]113[/C][C]39443[/C][C]43458.1333333333[/C][C]-4015.13333333333[/C][/ROW]
[ROW][C]114[/C][C]27023[/C][C]15162.0833333333[/C][C]11860.9166666667[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]2037.21052631579[/C][C]-2037.21052631579[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]2037.21052631579[/C][C]-2037.21052631579[/C][/ROW]
[ROW][C]117[/C][C]61022[/C][C]48725.25[/C][C]12296.75[/C][/ROW]
[ROW][C]118[/C][C]63528[/C][C]71539[/C][C]-8011[/C][/ROW]
[ROW][C]119[/C][C]34835[/C][C]48725.25[/C][C]-13890.25[/C][/ROW]
[ROW][C]120[/C][C]37172[/C][C]32277.0769230769[/C][C]4894.92307692308[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]2037.21052631579[/C][C]-2024.21052631579[/C][/ROW]
[ROW][C]122[/C][C]62548[/C][C]48725.25[/C][C]13822.75[/C][/ROW]
[ROW][C]123[/C][C]31334[/C][C]32277.0769230769[/C][C]-943.076923076922[/C][/ROW]
[ROW][C]124[/C][C]20839[/C][C]32277.0769230769[/C][C]-11438.0769230769[/C][/ROW]
[ROW][C]125[/C][C]5084[/C][C]15162.0833333333[/C][C]-10078.0833333333[/C][/ROW]
[ROW][C]126[/C][C]9927[/C][C]2037.21052631579[/C][C]7889.78947368421[/C][/ROW]
[ROW][C]127[/C][C]53229[/C][C]48725.25[/C][C]4503.75[/C][/ROW]
[ROW][C]128[/C][C]29877[/C][C]32277.0769230769[/C][C]-2400.07692307692[/C][/ROW]
[ROW][C]129[/C][C]37310[/C][C]48725.25[/C][C]-11415.25[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]2037.21052631579[/C][C]-2037.21052631579[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]2037.21052631579[/C][C]-2037.21052631579[/C][/ROW]
[ROW][C]132[/C][C]50067[/C][C]48725.25[/C][C]1341.75[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]2037.21052631579[/C][C]-2037.21052631579[/C][/ROW]
[ROW][C]134[/C][C]47708[/C][C]48725.25[/C][C]-1017.25[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]2037.21052631579[/C][C]-2037.21052631579[/C][/ROW]
[ROW][C]136[/C][C]6012[/C][C]2037.21052631579[/C][C]3974.78947368421[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]2037.21052631579[/C][C]-2037.21052631579[/C][/ROW]
[ROW][C]138[/C][C]27749[/C][C]32277.0769230769[/C][C]-4528.07692307692[/C][/ROW]
[ROW][C]139[/C][C]47555[/C][C]15162.0833333333[/C][C]32392.9166666667[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]2037.21052631579[/C][C]-2037.21052631579[/C][/ROW]
[ROW][C]141[/C][C]1336[/C][C]2037.21052631579[/C][C]-701.210526315789[/C][/ROW]
[ROW][C]142[/C][C]11017[/C][C]15162.0833333333[/C][C]-4145.08333333333[/C][/ROW]
[ROW][C]143[/C][C]55184[/C][C]43458.1333333333[/C][C]11725.8666666667[/C][/ROW]
[ROW][C]144[/C][C]43485[/C][C]32277.0769230769[/C][C]11207.9230769231[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159800&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159800&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	23975	48725.25	-24750.25
2	85634	48725.25	36908.75
3	1929	2037.21052631579	-108.210526315789
4	36294	32277.0769230769	4016.92307692308
5	72255	100920	-28665
6	189748	100920	88828
7	61834	48725.25	13108.75
8	68167	48725.25	19441.75
9	38462	48725.25	-10263.25
10	101219	100920	299
11	43270	32277.0769230769	10992.9230769231
12	76183	71539	4644
13	31476	32277.0769230769	-801.076923076922
14	62157	71539	-9382
15	46261	48725.25	-2464.25
16	50063	48725.25	1337.75
17	64483	43458.1333333333	21024.8666666667
18	2341	15162.0833333333	-12821.0833333333
19	48149	69185	-21036
20	12743	15162.0833333333	-2419.08333333333
21	18743	15162.0833333333	3580.91666666667
22	97057	100920	-3863
23	17675	48725.25	-31050.25
24	33106	32277.0769230769	828.923076923078
25	53311	48725.25	4585.75
26	42754	48725.25	-5971.25
27	59056	71539	-12483
28	101621	71539	30082
29	118120	100920	17200
30	79572	71539	8033
31	42744	48725.25	-5981.25
32	65931	48725.25	17205.75
33	38575	32277.0769230769	6297.92307692308
34	28795	48725.25	-19930.25
35	94440	100920	-6480
36	0	2037.21052631579	-2037.21052631579
37	38229	48725.25	-10496.25
38	31972	32277.0769230769	-305.076923076922
39	40071	48725.25	-8654.25
40	132480	100920	31560
41	62797	100920	-38123
42	40429	69185	-28756
43	45545	43458.1333333333	2086.86666666667
44	57568	71539	-13971
45	39019	43458.1333333333	-4439.13333333333
46	53866	69185	-15319
47	38345	32277.0769230769	6067.92307692308
48	50210	48725.25	1484.75
49	80947	69185	11762
50	43461	48725.25	-5264.25
51	14812	15162.0833333333	-350.083333333334
52	37819	48725.25	-10906.25
53	102738	100920	1818
54	54509	48725.25	5783.75
55	62956	48725.25	14230.75
56	55411	48725.25	6685.75
57	50611	48725.25	1885.75
58	26692	32277.0769230769	-5585.07692307692
59	60056	69185	-9129
60	25155	48725.25	-23570.25
61	42840	48725.25	-5885.25
62	39358	32277.0769230769	7080.92307692308
63	47241	43458.1333333333	3782.86666666667
64	49611	32277.0769230769	17333.9230769231
65	41833	48725.25	-6892.25
66	48930	43458.1333333333	5471.86666666667
67	110600	69185	41415
68	52235	43458.1333333333	8776.86666666667
69	53986	43458.1333333333	10527.8666666667
70	4105	2037.21052631579	2067.78947368421
71	59331	100920	-41589
72	47796	48725.25	-929.25
73	38302	43458.1333333333	-5156.13333333333
74	14063	15162.0833333333	-1099.08333333333
75	54414	48725.25	5688.75
76	9903	15162.0833333333	-5259.08333333333
77	53987	48725.25	5261.75
78	88937	71539	17398
79	21928	32277.0769230769	-10349.0769230769
80	29487	43458.1333333333	-13971.1333333333
81	35334	43458.1333333333	-8124.13333333333
82	57596	48725.25	8870.75
83	29750	32277.0769230769	-2527.07692307692
84	41029	43458.1333333333	-2429.13333333333
85	12416	15162.0833333333	-2746.08333333333
86	51158	48725.25	2432.75
87	79935	100920	-20985
88	26552	32277.0769230769	-5725.07692307692
89	25807	32277.0769230769	-6470.07692307692
90	50620	48725.25	1894.75
91	61467	48725.25	12741.75
92	65292	71539	-6247
93	55516	48725.25	6790.75
94	42006	32277.0769230769	9728.92307692308
95	26273	43458.1333333333	-17185.1333333333
96	90248	69185	21063
97	61476	71539	-10063
98	9604	2037.21052631579	7566.78947368421
99	45108	48725.25	-3617.25
100	47232	48725.25	-1493.25
101	3439	2037.21052631579	1401.78947368421
102	30553	32277.0769230769	-1724.07692307692
103	24751	32277.0769230769	-7526.07692307692
104	34458	32277.0769230769	2180.92307692308
105	24649	32277.0769230769	-7628.07692307692
106	2342	2037.21052631579	304.789473684211
107	52739	48725.25	4013.75
108	6245	15162.0833333333	-8917.08333333333
109	0	2037.21052631579	-2037.21052631579
110	35381	43458.1333333333	-8077.13333333333
111	19595	32277.0769230769	-12682.0769230769
112	50848	48725.25	2122.75
113	39443	43458.1333333333	-4015.13333333333
114	27023	15162.0833333333	11860.9166666667
115	0	2037.21052631579	-2037.21052631579
116	0	2037.21052631579	-2037.21052631579
117	61022	48725.25	12296.75
118	63528	71539	-8011
119	34835	48725.25	-13890.25
120	37172	32277.0769230769	4894.92307692308
121	13	2037.21052631579	-2024.21052631579
122	62548	48725.25	13822.75
123	31334	32277.0769230769	-943.076923076922
124	20839	32277.0769230769	-11438.0769230769
125	5084	15162.0833333333	-10078.0833333333
126	9927	2037.21052631579	7889.78947368421
127	53229	48725.25	4503.75
128	29877	32277.0769230769	-2400.07692307692
129	37310	48725.25	-11415.25
130	0	2037.21052631579	-2037.21052631579
131	0	2037.21052631579	-2037.21052631579
132	50067	48725.25	1341.75
133	0	2037.21052631579	-2037.21052631579
134	47708	48725.25	-1017.25
135	0	2037.21052631579	-2037.21052631579
136	6012	2037.21052631579	3974.78947368421
137	0	2037.21052631579	-2037.21052631579
138	27749	32277.0769230769	-4528.07692307692
139	47555	15162.0833333333	32392.9166666667
140	0	2037.21052631579	-2037.21052631579
141	1336	2037.21052631579	-701.210526315789
142	11017	15162.0833333333	-4145.08333333333
143	55184	43458.1333333333	11725.8666666667
144	43485	32277.0769230769	11207.9230769231

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 = 13 ; 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