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Author's title

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

Wed, 14 Dec 2011 09:33:40 -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/14/t13238734467l8lratdztze1vg.htm/, Retrieved Wed, 01 May 2024 19:27:28 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155007, Retrieved Wed, 01 May 2024 19:27:28 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

140

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

-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD    [Recursive Partitioning (Regression Trees)] [Ws 10, Recursive ...] [2011-12-14 14:33:40] [242bbde8f74d68805b56d9ecebfdbe63] [Current]
- RMPD      [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Paper blog 3] [2011-12-19 17:14:15] [2628f630b839c9d14ba9c3627ab0414a] 
- RMPD      [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Paper blog 3] [2011-12-19 17:14:15] [2628f630b839c9d14ba9c3627ab0414a] 

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Dataseries X:

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Histogram

Boxplots

1536	127476	78	490	107	0	20	17	66	59	18158	5636	22622	30	28
1134	130358	46	329	68	1	38	17	68	50	30461	9079	73570	42	39
192	7215	18	72	1	0	0	0	0	0	1423	603	1929	0	0
2032	112861	84	584	146	0	49	22	68	51	25629	8874	36294	54	54
3231	210171	125	1077	124	0	74	30	120	112	48758	17988	62378	86	80
5777	393802	215	1578	267	1	104	31	120	118	129230	21325	167760	157	144
1322	117604	50	442	83	1	37	19	72	59	27376	8325	52443	36	36
1181	126029	48	319	48	0	53	25	96	90	26706	7117	57283	48	48
1462	99729	37	406	87	0	42	30	109	50	26505	7996	36614	45	42
2568	256310	86	818	129	1	62	26	104	79	49801	14218	93268	77	71
1810	113066	69	568	146	2	50	20	54	49	46580	6321	35439	49	49
1789	156212	59	551	94	0	65	25	98	74	48352	19690	72405	77	74
1334	69952	85	494	57	0	28	15	49	32	13899	5659	24044	28	27
2415	152673	84	818	240	4	48	22	88	82	39342	11370	55909	84	83
1156	125841	44	331	40	4	42	12	45	43	27465	4778	44689	31	31
1374	125769	67	419	81	3	47	19	74	65	55211	5954	49319	28	28
1504	123467	50	364	85	0	71	28	112	111	74098	22924	62075	99	98
999	56232	47	284	62	5	0	12	45	36	13497	70	2341	2	2
2190	108244	77	667	126	0	50	28	110	89	38338	14369	40551	41	43
633	22762	20	188	44	0	12	13	39	28	52505	3706	11621	25	24
838	48554	49	286	37	0	16	14	55	35	10663	3147	18741	16	16
2167	178697	81	633	94	0	76	27	102	78	74484	16801	84202	96	95
1452	139115	58	514	127	0	29	25	96	67	28895	2162	15334	23	22
1790	93773	45	532	159	1	38	30	86	61	32827	4721	28024	33	33
1718	132796	76	540	41	1	50	20	74	55	36188	5290	53306	46	45
1179	113933	22	428	153	0	33	17	64	49	28173	6446	37918	59	59
1688	144781	138	539	86	0	45	22	82	77	54926	14711	54819	72	66
1101	140711	75	266	55	0	59	28	100	71	38900	13311	89058	72	70
2259	283337	102	745	73	0	49	25	95	82	88530	13577	103354	62	56
1768	158146	36	733	79	0	40	16	63	53	35482	14634	70239	55	55
1300	123344	39	394	71	0	40	23	87	71	26730	6931	33045	27	27
1432	157640	38	482	111	2	51	20	65	58	29806	9992	63852	41	37
1791	91279	88	567	71	4	41	11	43	25	41799	6185	30905	51	48
2475	189374	102	746	243	0	73	20	80	59	54289	3445	24242	26	26
1930	167915	42	626	66	1	43	21	84	77	36805	12327	78907	65	64
1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
1782	175403	54	835	58	0	46	27	105	75	33146	9898	36005	28	21
1505	92342	46	464	131	3	44	14	51	39	23333	8022	31972	44	44
1820	100023	41	418	258	9	31	29	98	83	47686	10765	35853	36	36
1648	178277	49	607	56	0	71	31	124	123	77783	22717	115301	100	89
1668	145062	56	539	90	2	61	19	75	67	36042	10090	47689	104	101
1366	110980	47	519	57	0	28	30	120	105	34541	12385	34223	35	31
864	86039	25	309	35	2	21	23	84	76	75620	8513	43431	69	65
1602	119514	62	609	50	1	42	20	78	54	60610	5508	52220	73	71
1023	95535	41	321	46	2	44	22	87	82	55041	9628	33863	106	102
963	109894	73	246	32	2	34	19	70	57	32087	11872	46879	53	53
629	61554	26	180	45	1	15	32	97	57	16356	4186	23228	43	41
1568	156520	77	544	96	0	46	18	72	72	40161	10877	42827	49	46
1715	159121	75	544	104	1	43	26	104	94	55459	17066	65765	38	37
2093	129362	51	758	150	4	47	25	93	72	36679	9175	38167	51	51
658	48188	28	205	37	0	12	22	82	39	22346	2102	14812	14	14
1199	91198	54	309	49	0	42	19	73	60	27377	10807	32615	40	40
2059	229864	64	709	83	0	56	24	87	84	50273	13662	82188	79	77
1592	180317	67	542	67	0	41	26	95	69	32104	9224	51763	52	51
1447	150640	48	526	39	1	48	27	105	102	27016	9001	59325	44	43
1342	104416	44	418	68	5	30	10	37	28	19715	7204	48976	34	33
1527	159645	55	409	58	0	44	26	96	65	33629	6572	43384	47	47
670	60368	17	189	59	0	25	21	80	59	27084	7509	26692	32	31
859	100056	55	293	30	0	42	21	83	80	32352	12920	53279	31	31
2329	137214	73	781	54	10	28	34	124	79	51845	5438	20652	40	40
1326	99630	47	383	65	6	33	29	116	107	26591	11489	38338	42	42
1567	84557	62	572	81	0	32	18	72	57	29677	6661	36735	34	35
1081	91199	45	308	81	11	28	16	55	44	54237	7941	42764	40	40
897	83419	29	288	45	3	31	23	86	59	20284	6173	44331	35	30
855	101723	25	285	52	0	13	22	85	80	22741	5562	41354	11	11
1229	94982	37	391	36	0	38	29	107	89	34178	9492	47879	43	41
1939	129700	60	446	80	8	39	31	124	115	69551	17456	103793	53	53
2293	110708	57	690	137	2	68	21	78	59	29653	9422	52235	82	82
820	81518	32	208	45	0	32	21	83	66	38071	10913	49825	41	41
340	31970	15	101	40	0	5	21	78	42	4157	1283	4105	6	6
2443	192268	102	858	126	3	53	15	59	35	28321	6198	58687	82	81
993	87611	52	293	74	1	33	9	33	3	40195	4501	40745	47	47
1038	77890	53	349	48	2	48	21	84	68	48158	9560	33187	108	100
1380	83261	58	411	82	1	36	18	52	38	13310	3394	14063	46	46
2186	116290	51	561	86	0	52	31	121	107	78474	9871	37407	38	38
1069	55254	31	289	60	2	0	24	88	69	6386	2419	7190	0	0
1763	116173	50	492	99	1	52	24	99	80	31588	10630	49562	45	45
1995	111488	78	669	63	0	45	22	86	69	61254	8536	76324	57	56
816	60138	23	253	76	0	16	21	75	46	21152	4911	21928	20	18
1121	73422	66	366	92	0	33	26	96	52	41272	9775	27860	56	54
808	67751	56	192	45	0	48	22	81	58	34165	11227	28078	38	37
1690	213351	51	616	57	0	33	26	104	85	37054	6916	49577	42	40
751	51185	24	221	44	0	24	20	76	13	12368	3424	28145	37	37
1309	97181	32	438	132	0	37	25	90	61	23168	8637	36241	36	36
685	42311	37	229	43	0	16	19	75	49	16380	3189	10824	34	34
1326	115801	42	388	67	0	32	22	86	47	41242	8178	46892	53	49
2224	183637	182	536	82	0	55	25	100	93	48450	16739	61264	85	82
923	68161	84	220	71	0	36	22	88	65	20790	6094	22933	36	36
967	76441	46	313	44	4	29	21	80	64	34585	7237	20787	33	33
1099	103613	40	422	68	0	26	20	73	64	35672	7355	43978	57	55
1300	98707	33	452	54	3	37	23	88	57	52168	9734	51305	50	50
1872	126527	66	556	86	1	58	22	79	61	53933	11225	55593	71	71
1091	136781	52	366	59	0	35	21	81	71	34474	6213	51648	32	31
1106	105863	51	406	74	0	24	12	48	43	43753	4875	30552	45	42
632	38775	30	254	18	0	18	9	33	18	36456	8159	23470	33	31
1901	179984	89	606	156	0	37	32	120	103	51183	11893	77530	53	51
1580	164808	49	479	87	0	86	24	90	76	52742	10754	57299	64	64
223	19349	12	67	15	0	13	1	2	0	3895	786	9604	14	14
1698	143902	83	578	104	1	20	24	96	83	37076	9706	34684	38	37
1420	108660	52	581	49	0	32	22	79	70	24079	7796	41094	39	37
552	43803	24	240	11	0	8	4	15	4	2325	593	3439	8	8
708	47062	19	219	37	0	38	15	48	41	29354	5600	25171	38	38
1079	110845	44	349	80	0	45	21	81	57	30341	7245	23437	24	23
957	92517	52	241	66	1	24	23	84	52	18992	7360	34086	22	22
584	58660	35	136	27	0	23	12	46	24	15292	4574	24649	18	18
596	27676	22	194	59	0	2	16	59	17	5842	522	2342	3	1
980	98550	32	222	113	0	52	24	96	89	28918	10905	45571	49	48
576	43284	22	151	24	0	5	9	29	20	3738	999	3255	5	5
0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
880	66016	26	239	54	0	43	22	79	45	95352	9016	30002	47	46
750	57359	48	240	43	0	18	17	63	63	37478	5134	19360	33	33
999	96933	35	323	45	0	41	18	68	48	26839	6608	43320	44	41
931	70369	47	302	55	0	45	21	84	70	26783	8577	35513	56	57
782	65494	55	267	66	0	29	17	54	32	33392	1543	23536	49	49
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
874	143931	37	287	67	0	32	20	75	72	25446	9803	54438	45	45
1262	109894	65	442	67	0	58	26	87	56	59847	12140	56812	78	78
1711	122973	81	490	115	1	17	26	104	64	28162	6678	33838	51	46
749	84336	32	243	51	0	24	20	80	77	33298	6420	32366	25	25
778	43410	19	292	63	0	7	1	3	3	2781	4	13	1	1
1373	136250	58	410	84	1	62	24	93	73	37121	7979	55082	62	59
806	79015	33	217	35	0	30	14	55	37	22698	5141	31334	29	29
1448	92937	42	422	57	8	49	26	96	54	27615	1311	16612	26	26
684	57586	37	160	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
1336	105757	42	412	51	2	42	16	60	55	23164	8396	47413	43	43
841	96410	23	293	51	0	18	22	84	81	20304	5462	27389	36	36
1283	113402	35	417	96	0	40	28	112	90	34409	7271	30425	43	41
256	11796	9	79	22	0	1	2	8	1	0	0	0	0	0
81	7627	9	25	7	0	0	0	0	0	0	0	0	0	0
1214	121085	49	431	34	0	29	17	52	38	92538	4423	33510	33	32
41	6836	3	11	5	0	0	1	4	0	0	0	0	0	0
1629	139563	41	564	43	4	46	17	57	36	46037	5331	40389	53	53
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
889	95079	41	295	49	0	21	25	91	75	23924	6676	22205	19	18
1197	80750	31	228	44	0	21	26	89	52	52230	1489	17231	26	26
81	7131	4	27	0	1	0	0	0	0	0	0	0	0	0
61	4194	11	14	4	0	0	0	0	0	0	0	0	0	0
849	60378	20	240	40	1	15	15	54	45	8019	3080	11017	16	16
970	96971	40	233	49	0	40	18	69	60	34542	11409	46741	84	84
964	83484	16	347	47	0	17	19	76	48	21157	6769	39869	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=155007&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=155007&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155007&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	0.8759
R-squared	0.7671
RMSE	28418.1625

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8759 \tabularnewline
R-squared & 0.7671 \tabularnewline
RMSE & 28418.1625 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155007&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8759[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7671[/C][/ROW]
[ROW][C]RMSE[/C][C]28418.1625[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155007&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155007&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	0.8759
R-squared	0.7671
RMSE	28418.1625

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	127476	106450.3125	21025.6875
2	130358	148556.392857143	-18198.3928571429
3	7215	6617.57142857143	597.428571428572
4	112861	124982.230769231	-12121.2307692308
5	210171	210949.4	-778.399999999994
6	393802	210949.4	182852.6
7	117604	148556.392857143	-30952.3928571429
8	126029	148556.392857143	-22527.3928571429
9	99729	106450.3125	-6721.3125
10	256310	210949.4	45360.6
11	113066	124982.230769231	-11916.2307692308
12	156212	148556.392857143	7655.60714285713
13	69952	106450.3125	-36498.3125
14	152673	210949.4	-58276.4
15	125841	106450.3125	19390.6875
16	125769	106450.3125	19318.6875
17	123467	148556.392857143	-25089.3928571429
18	56232	44404	11828
19	108244	124982.230769231	-16738.2307692308
20	22762	44404	-21642
21	48554	44404	4150
22	178697	148556.392857143	30140.6071428571
23	139115	106450.3125	32664.6875
24	93773	106450.3125	-12677.3125
25	132796	148556.392857143	-15760.3928571429
26	113933	106450.3125	7482.6875
27	144781	148556.392857143	-3775.39285714287
28	140711	148556.392857143	-7845.39285714287
29	283337	210949.4	72387.6
30	158146	148556.392857143	9589.60714285713
31	123344	106450.3125	16893.6875
32	157640	148556.392857143	9083.60714285713
33	91279	124982.230769231	-33703.2307692308
34	189374	210949.4	-21575.4
35	167915	148556.392857143	19358.6071428571
36	0	6617.57142857143	-6617.57142857143
37	175403	124982.230769231	50420.7692307692
38	92342	106450.3125	-14108.3125
39	100023	106450.3125	-6427.3125
40	178277	148556.392857143	29720.6071428571
41	145062	124982.230769231	20079.7692307692
42	110980	106450.3125	4529.6875
43	86039	93575.0588235294	-7536.05882352941
44	119514	148556.392857143	-29042.3928571429
45	95535	93575.0588235294	1959.94117647059
46	109894	93575.0588235294	16318.9411764706
47	61554	65591.75	-4037.75
48	156520	124982.230769231	31537.7692307692
49	159121	148556.392857143	10564.6071428571
50	129362	124982.230769231	4379.76923076923
51	48188	44404	3784
52	91198	106450.3125	-15252.3125
53	229864	148556.392857143	81307.6071428571
54	180317	148556.392857143	31760.6071428571
55	150640	148556.392857143	2083.60714285713
56	104416	106450.3125	-2034.3125
57	159645	106450.3125	53194.6875
58	60368	65591.75	-5223.75
59	100056	93575.0588235294	6480.94117647059
60	137214	210949.4	-73735.4
61	99630	106450.3125	-6820.3125
62	84557	124982.230769231	-40425.2307692308
63	91199	106450.3125	-15251.3125
64	83419	93575.0588235294	-10156.0588235294
65	101723	93575.0588235294	8147.94117647059
66	94982	106450.3125	-11468.3125
67	129700	148556.392857143	-18856.3928571429
68	110708	210949.4	-100241.4
69	81518	93575.0588235294	-12057.0588235294
70	31970	44404	-12434
71	192268	210949.4	-18681.4
72	87611	93575.0588235294	-5964.05882352941
73	77890	93575.0588235294	-15685.0588235294
74	83261	106450.3125	-23189.3125
75	116290	124982.230769231	-8692.23076923077
76	55254	44404	10850
77	116173	106450.3125	9722.6875
78	111488	148556.392857143	-37068.3928571429
79	60138	65591.75	-5453.75
80	73422	106450.3125	-33028.3125
81	67751	65591.75	2159.25
82	213351	148556.392857143	64794.6071428571
83	51185	65591.75	-14406.75
84	97181	106450.3125	-9269.3125
85	42311	44404	-2093
86	115801	106450.3125	9350.6875
87	183637	210949.4	-27312.4
88	68161	65591.75	2569.25
89	76441	65591.75	10849.25
90	103613	106450.3125	-2837.3125
91	98707	148556.392857143	-49849.3928571429
92	126527	148556.392857143	-22029.3928571429
93	136781	148556.392857143	-11775.3928571429
94	105863	106450.3125	-587.3125
95	38775	65591.75	-26816.75
96	179984	148556.392857143	31427.6071428571
97	164808	148556.392857143	16251.6071428571
98	19349	6617.57142857143	12731.4285714286
99	143902	124982.230769231	18919.7692307692
100	108660	124982.230769231	-16322.2307692308
101	43803	44404	-601
102	47062	65591.75	-18529.75
103	110845	106450.3125	4394.6875
104	92517	93575.0588235294	-1058.05882352941
105	58660	65591.75	-6931.75
106	27676	44404	-16728
107	98550	93575.0588235294	4974.94117647059
108	43284	44404	-1120
109	0	6617.57142857143	-6617.57142857143
110	66016	65591.75	424.25
111	57359	65591.75	-8232.75
112	96933	93575.0588235294	3357.94117647059
113	70369	93575.0588235294	-23206.0588235294
114	65494	65591.75	-97.75
115	3616	6617.57142857143	-3001.57142857143
116	0	6617.57142857143	-6617.57142857143
117	143931	93575.0588235294	50355.9411764706
118	109894	148556.392857143	-38662.3928571429
119	122973	106450.3125	16522.6875
120	84336	93575.0588235294	-9239.05882352941
121	43410	44404	-994
122	136250	148556.392857143	-12306.3928571429
123	79015	65591.75	13423.25
124	92937	106450.3125	-13513.3125
125	57586	44404	13182
126	19764	6617.57142857143	13146.4285714286
127	105757	106450.3125	-693.3125
128	96410	65591.75	30818.25
129	113402	106450.3125	6951.6875
130	11796	6617.57142857143	5178.42857142857
131	7627	6617.57142857143	1009.42857142857
132	121085	106450.3125	14634.6875
133	6836	6617.57142857143	218.428571428572
134	139563	124982.230769231	14580.7692307692
135	5118	6617.57142857143	-1499.57142857143
136	40248	44404	-4156
137	0	6617.57142857143	-6617.57142857143
138	95079	65591.75	29487.25
139	80750	106450.3125	-25700.3125
140	7131	6617.57142857143	513.428571428572
141	4194	6617.57142857143	-2423.57142857143
142	60378	44404	15974
143	96971	93575.0588235294	3395.94117647059
144	83484	93575.0588235294	-10091.0588235294

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 127476 & 106450.3125 & 21025.6875 \tabularnewline
2 & 130358 & 148556.392857143 & -18198.3928571429 \tabularnewline
3 & 7215 & 6617.57142857143 & 597.428571428572 \tabularnewline
4 & 112861 & 124982.230769231 & -12121.2307692308 \tabularnewline
5 & 210171 & 210949.4 & -778.399999999994 \tabularnewline
6 & 393802 & 210949.4 & 182852.6 \tabularnewline
7 & 117604 & 148556.392857143 & -30952.3928571429 \tabularnewline
8 & 126029 & 148556.392857143 & -22527.3928571429 \tabularnewline
9 & 99729 & 106450.3125 & -6721.3125 \tabularnewline
10 & 256310 & 210949.4 & 45360.6 \tabularnewline
11 & 113066 & 124982.230769231 & -11916.2307692308 \tabularnewline
12 & 156212 & 148556.392857143 & 7655.60714285713 \tabularnewline
13 & 69952 & 106450.3125 & -36498.3125 \tabularnewline
14 & 152673 & 210949.4 & -58276.4 \tabularnewline
15 & 125841 & 106450.3125 & 19390.6875 \tabularnewline
16 & 125769 & 106450.3125 & 19318.6875 \tabularnewline
17 & 123467 & 148556.392857143 & -25089.3928571429 \tabularnewline
18 & 56232 & 44404 & 11828 \tabularnewline
19 & 108244 & 124982.230769231 & -16738.2307692308 \tabularnewline
20 & 22762 & 44404 & -21642 \tabularnewline
21 & 48554 & 44404 & 4150 \tabularnewline
22 & 178697 & 148556.392857143 & 30140.6071428571 \tabularnewline
23 & 139115 & 106450.3125 & 32664.6875 \tabularnewline
24 & 93773 & 106450.3125 & -12677.3125 \tabularnewline
25 & 132796 & 148556.392857143 & -15760.3928571429 \tabularnewline
26 & 113933 & 106450.3125 & 7482.6875 \tabularnewline
27 & 144781 & 148556.392857143 & -3775.39285714287 \tabularnewline
28 & 140711 & 148556.392857143 & -7845.39285714287 \tabularnewline
29 & 283337 & 210949.4 & 72387.6 \tabularnewline
30 & 158146 & 148556.392857143 & 9589.60714285713 \tabularnewline
31 & 123344 & 106450.3125 & 16893.6875 \tabularnewline
32 & 157640 & 148556.392857143 & 9083.60714285713 \tabularnewline
33 & 91279 & 124982.230769231 & -33703.2307692308 \tabularnewline
34 & 189374 & 210949.4 & -21575.4 \tabularnewline
35 & 167915 & 148556.392857143 & 19358.6071428571 \tabularnewline
36 & 0 & 6617.57142857143 & -6617.57142857143 \tabularnewline
37 & 175403 & 124982.230769231 & 50420.7692307692 \tabularnewline
38 & 92342 & 106450.3125 & -14108.3125 \tabularnewline
39 & 100023 & 106450.3125 & -6427.3125 \tabularnewline
40 & 178277 & 148556.392857143 & 29720.6071428571 \tabularnewline
41 & 145062 & 124982.230769231 & 20079.7692307692 \tabularnewline
42 & 110980 & 106450.3125 & 4529.6875 \tabularnewline
43 & 86039 & 93575.0588235294 & -7536.05882352941 \tabularnewline
44 & 119514 & 148556.392857143 & -29042.3928571429 \tabularnewline
45 & 95535 & 93575.0588235294 & 1959.94117647059 \tabularnewline
46 & 109894 & 93575.0588235294 & 16318.9411764706 \tabularnewline
47 & 61554 & 65591.75 & -4037.75 \tabularnewline
48 & 156520 & 124982.230769231 & 31537.7692307692 \tabularnewline
49 & 159121 & 148556.392857143 & 10564.6071428571 \tabularnewline
50 & 129362 & 124982.230769231 & 4379.76923076923 \tabularnewline
51 & 48188 & 44404 & 3784 \tabularnewline
52 & 91198 & 106450.3125 & -15252.3125 \tabularnewline
53 & 229864 & 148556.392857143 & 81307.6071428571 \tabularnewline
54 & 180317 & 148556.392857143 & 31760.6071428571 \tabularnewline
55 & 150640 & 148556.392857143 & 2083.60714285713 \tabularnewline
56 & 104416 & 106450.3125 & -2034.3125 \tabularnewline
57 & 159645 & 106450.3125 & 53194.6875 \tabularnewline
58 & 60368 & 65591.75 & -5223.75 \tabularnewline
59 & 100056 & 93575.0588235294 & 6480.94117647059 \tabularnewline
60 & 137214 & 210949.4 & -73735.4 \tabularnewline
61 & 99630 & 106450.3125 & -6820.3125 \tabularnewline
62 & 84557 & 124982.230769231 & -40425.2307692308 \tabularnewline
63 & 91199 & 106450.3125 & -15251.3125 \tabularnewline
64 & 83419 & 93575.0588235294 & -10156.0588235294 \tabularnewline
65 & 101723 & 93575.0588235294 & 8147.94117647059 \tabularnewline
66 & 94982 & 106450.3125 & -11468.3125 \tabularnewline
67 & 129700 & 148556.392857143 & -18856.3928571429 \tabularnewline
68 & 110708 & 210949.4 & -100241.4 \tabularnewline
69 & 81518 & 93575.0588235294 & -12057.0588235294 \tabularnewline
70 & 31970 & 44404 & -12434 \tabularnewline
71 & 192268 & 210949.4 & -18681.4 \tabularnewline
72 & 87611 & 93575.0588235294 & -5964.05882352941 \tabularnewline
73 & 77890 & 93575.0588235294 & -15685.0588235294 \tabularnewline
74 & 83261 & 106450.3125 & -23189.3125 \tabularnewline
75 & 116290 & 124982.230769231 & -8692.23076923077 \tabularnewline
76 & 55254 & 44404 & 10850 \tabularnewline
77 & 116173 & 106450.3125 & 9722.6875 \tabularnewline
78 & 111488 & 148556.392857143 & -37068.3928571429 \tabularnewline
79 & 60138 & 65591.75 & -5453.75 \tabularnewline
80 & 73422 & 106450.3125 & -33028.3125 \tabularnewline
81 & 67751 & 65591.75 & 2159.25 \tabularnewline
82 & 213351 & 148556.392857143 & 64794.6071428571 \tabularnewline
83 & 51185 & 65591.75 & -14406.75 \tabularnewline
84 & 97181 & 106450.3125 & -9269.3125 \tabularnewline
85 & 42311 & 44404 & -2093 \tabularnewline
86 & 115801 & 106450.3125 & 9350.6875 \tabularnewline
87 & 183637 & 210949.4 & -27312.4 \tabularnewline
88 & 68161 & 65591.75 & 2569.25 \tabularnewline
89 & 76441 & 65591.75 & 10849.25 \tabularnewline
90 & 103613 & 106450.3125 & -2837.3125 \tabularnewline
91 & 98707 & 148556.392857143 & -49849.3928571429 \tabularnewline
92 & 126527 & 148556.392857143 & -22029.3928571429 \tabularnewline
93 & 136781 & 148556.392857143 & -11775.3928571429 \tabularnewline
94 & 105863 & 106450.3125 & -587.3125 \tabularnewline
95 & 38775 & 65591.75 & -26816.75 \tabularnewline
96 & 179984 & 148556.392857143 & 31427.6071428571 \tabularnewline
97 & 164808 & 148556.392857143 & 16251.6071428571 \tabularnewline
98 & 19349 & 6617.57142857143 & 12731.4285714286 \tabularnewline
99 & 143902 & 124982.230769231 & 18919.7692307692 \tabularnewline
100 & 108660 & 124982.230769231 & -16322.2307692308 \tabularnewline
101 & 43803 & 44404 & -601 \tabularnewline
102 & 47062 & 65591.75 & -18529.75 \tabularnewline
103 & 110845 & 106450.3125 & 4394.6875 \tabularnewline
104 & 92517 & 93575.0588235294 & -1058.05882352941 \tabularnewline
105 & 58660 & 65591.75 & -6931.75 \tabularnewline
106 & 27676 & 44404 & -16728 \tabularnewline
107 & 98550 & 93575.0588235294 & 4974.94117647059 \tabularnewline
108 & 43284 & 44404 & -1120 \tabularnewline
109 & 0 & 6617.57142857143 & -6617.57142857143 \tabularnewline
110 & 66016 & 65591.75 & 424.25 \tabularnewline
111 & 57359 & 65591.75 & -8232.75 \tabularnewline
112 & 96933 & 93575.0588235294 & 3357.94117647059 \tabularnewline
113 & 70369 & 93575.0588235294 & -23206.0588235294 \tabularnewline
114 & 65494 & 65591.75 & -97.75 \tabularnewline
115 & 3616 & 6617.57142857143 & -3001.57142857143 \tabularnewline
116 & 0 & 6617.57142857143 & -6617.57142857143 \tabularnewline
117 & 143931 & 93575.0588235294 & 50355.9411764706 \tabularnewline
118 & 109894 & 148556.392857143 & -38662.3928571429 \tabularnewline
119 & 122973 & 106450.3125 & 16522.6875 \tabularnewline
120 & 84336 & 93575.0588235294 & -9239.05882352941 \tabularnewline
121 & 43410 & 44404 & -994 \tabularnewline
122 & 136250 & 148556.392857143 & -12306.3928571429 \tabularnewline
123 & 79015 & 65591.75 & 13423.25 \tabularnewline
124 & 92937 & 106450.3125 & -13513.3125 \tabularnewline
125 & 57586 & 44404 & 13182 \tabularnewline
126 & 19764 & 6617.57142857143 & 13146.4285714286 \tabularnewline
127 & 105757 & 106450.3125 & -693.3125 \tabularnewline
128 & 96410 & 65591.75 & 30818.25 \tabularnewline
129 & 113402 & 106450.3125 & 6951.6875 \tabularnewline
130 & 11796 & 6617.57142857143 & 5178.42857142857 \tabularnewline
131 & 7627 & 6617.57142857143 & 1009.42857142857 \tabularnewline
132 & 121085 & 106450.3125 & 14634.6875 \tabularnewline
133 & 6836 & 6617.57142857143 & 218.428571428572 \tabularnewline
134 & 139563 & 124982.230769231 & 14580.7692307692 \tabularnewline
135 & 5118 & 6617.57142857143 & -1499.57142857143 \tabularnewline
136 & 40248 & 44404 & -4156 \tabularnewline
137 & 0 & 6617.57142857143 & -6617.57142857143 \tabularnewline
138 & 95079 & 65591.75 & 29487.25 \tabularnewline
139 & 80750 & 106450.3125 & -25700.3125 \tabularnewline
140 & 7131 & 6617.57142857143 & 513.428571428572 \tabularnewline
141 & 4194 & 6617.57142857143 & -2423.57142857143 \tabularnewline
142 & 60378 & 44404 & 15974 \tabularnewline
143 & 96971 & 93575.0588235294 & 3395.94117647059 \tabularnewline
144 & 83484 & 93575.0588235294 & -10091.0588235294 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155007&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]127476[/C][C]106450.3125[/C][C]21025.6875[/C][/ROW]
[ROW][C]2[/C][C]130358[/C][C]148556.392857143[/C][C]-18198.3928571429[/C][/ROW]
[ROW][C]3[/C][C]7215[/C][C]6617.57142857143[/C][C]597.428571428572[/C][/ROW]
[ROW][C]4[/C][C]112861[/C][C]124982.230769231[/C][C]-12121.2307692308[/C][/ROW]
[ROW][C]5[/C][C]210171[/C][C]210949.4[/C][C]-778.399999999994[/C][/ROW]
[ROW][C]6[/C][C]393802[/C][C]210949.4[/C][C]182852.6[/C][/ROW]
[ROW][C]7[/C][C]117604[/C][C]148556.392857143[/C][C]-30952.3928571429[/C][/ROW]
[ROW][C]8[/C][C]126029[/C][C]148556.392857143[/C][C]-22527.3928571429[/C][/ROW]
[ROW][C]9[/C][C]99729[/C][C]106450.3125[/C][C]-6721.3125[/C][/ROW]
[ROW][C]10[/C][C]256310[/C][C]210949.4[/C][C]45360.6[/C][/ROW]
[ROW][C]11[/C][C]113066[/C][C]124982.230769231[/C][C]-11916.2307692308[/C][/ROW]
[ROW][C]12[/C][C]156212[/C][C]148556.392857143[/C][C]7655.60714285713[/C][/ROW]
[ROW][C]13[/C][C]69952[/C][C]106450.3125[/C][C]-36498.3125[/C][/ROW]
[ROW][C]14[/C][C]152673[/C][C]210949.4[/C][C]-58276.4[/C][/ROW]
[ROW][C]15[/C][C]125841[/C][C]106450.3125[/C][C]19390.6875[/C][/ROW]
[ROW][C]16[/C][C]125769[/C][C]106450.3125[/C][C]19318.6875[/C][/ROW]
[ROW][C]17[/C][C]123467[/C][C]148556.392857143[/C][C]-25089.3928571429[/C][/ROW]
[ROW][C]18[/C][C]56232[/C][C]44404[/C][C]11828[/C][/ROW]
[ROW][C]19[/C][C]108244[/C][C]124982.230769231[/C][C]-16738.2307692308[/C][/ROW]
[ROW][C]20[/C][C]22762[/C][C]44404[/C][C]-21642[/C][/ROW]
[ROW][C]21[/C][C]48554[/C][C]44404[/C][C]4150[/C][/ROW]
[ROW][C]22[/C][C]178697[/C][C]148556.392857143[/C][C]30140.6071428571[/C][/ROW]
[ROW][C]23[/C][C]139115[/C][C]106450.3125[/C][C]32664.6875[/C][/ROW]
[ROW][C]24[/C][C]93773[/C][C]106450.3125[/C][C]-12677.3125[/C][/ROW]
[ROW][C]25[/C][C]132796[/C][C]148556.392857143[/C][C]-15760.3928571429[/C][/ROW]
[ROW][C]26[/C][C]113933[/C][C]106450.3125[/C][C]7482.6875[/C][/ROW]
[ROW][C]27[/C][C]144781[/C][C]148556.392857143[/C][C]-3775.39285714287[/C][/ROW]
[ROW][C]28[/C][C]140711[/C][C]148556.392857143[/C][C]-7845.39285714287[/C][/ROW]
[ROW][C]29[/C][C]283337[/C][C]210949.4[/C][C]72387.6[/C][/ROW]
[ROW][C]30[/C][C]158146[/C][C]148556.392857143[/C][C]9589.60714285713[/C][/ROW]
[ROW][C]31[/C][C]123344[/C][C]106450.3125[/C][C]16893.6875[/C][/ROW]
[ROW][C]32[/C][C]157640[/C][C]148556.392857143[/C][C]9083.60714285713[/C][/ROW]
[ROW][C]33[/C][C]91279[/C][C]124982.230769231[/C][C]-33703.2307692308[/C][/ROW]
[ROW][C]34[/C][C]189374[/C][C]210949.4[/C][C]-21575.4[/C][/ROW]
[ROW][C]35[/C][C]167915[/C][C]148556.392857143[/C][C]19358.6071428571[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]6617.57142857143[/C][C]-6617.57142857143[/C][/ROW]
[ROW][C]37[/C][C]175403[/C][C]124982.230769231[/C][C]50420.7692307692[/C][/ROW]
[ROW][C]38[/C][C]92342[/C][C]106450.3125[/C][C]-14108.3125[/C][/ROW]
[ROW][C]39[/C][C]100023[/C][C]106450.3125[/C][C]-6427.3125[/C][/ROW]
[ROW][C]40[/C][C]178277[/C][C]148556.392857143[/C][C]29720.6071428571[/C][/ROW]
[ROW][C]41[/C][C]145062[/C][C]124982.230769231[/C][C]20079.7692307692[/C][/ROW]
[ROW][C]42[/C][C]110980[/C][C]106450.3125[/C][C]4529.6875[/C][/ROW]
[ROW][C]43[/C][C]86039[/C][C]93575.0588235294[/C][C]-7536.05882352941[/C][/ROW]
[ROW][C]44[/C][C]119514[/C][C]148556.392857143[/C][C]-29042.3928571429[/C][/ROW]
[ROW][C]45[/C][C]95535[/C][C]93575.0588235294[/C][C]1959.94117647059[/C][/ROW]
[ROW][C]46[/C][C]109894[/C][C]93575.0588235294[/C][C]16318.9411764706[/C][/ROW]
[ROW][C]47[/C][C]61554[/C][C]65591.75[/C][C]-4037.75[/C][/ROW]
[ROW][C]48[/C][C]156520[/C][C]124982.230769231[/C][C]31537.7692307692[/C][/ROW]
[ROW][C]49[/C][C]159121[/C][C]148556.392857143[/C][C]10564.6071428571[/C][/ROW]
[ROW][C]50[/C][C]129362[/C][C]124982.230769231[/C][C]4379.76923076923[/C][/ROW]
[ROW][C]51[/C][C]48188[/C][C]44404[/C][C]3784[/C][/ROW]
[ROW][C]52[/C][C]91198[/C][C]106450.3125[/C][C]-15252.3125[/C][/ROW]
[ROW][C]53[/C][C]229864[/C][C]148556.392857143[/C][C]81307.6071428571[/C][/ROW]
[ROW][C]54[/C][C]180317[/C][C]148556.392857143[/C][C]31760.6071428571[/C][/ROW]
[ROW][C]55[/C][C]150640[/C][C]148556.392857143[/C][C]2083.60714285713[/C][/ROW]
[ROW][C]56[/C][C]104416[/C][C]106450.3125[/C][C]-2034.3125[/C][/ROW]
[ROW][C]57[/C][C]159645[/C][C]106450.3125[/C][C]53194.6875[/C][/ROW]
[ROW][C]58[/C][C]60368[/C][C]65591.75[/C][C]-5223.75[/C][/ROW]
[ROW][C]59[/C][C]100056[/C][C]93575.0588235294[/C][C]6480.94117647059[/C][/ROW]
[ROW][C]60[/C][C]137214[/C][C]210949.4[/C][C]-73735.4[/C][/ROW]
[ROW][C]61[/C][C]99630[/C][C]106450.3125[/C][C]-6820.3125[/C][/ROW]
[ROW][C]62[/C][C]84557[/C][C]124982.230769231[/C][C]-40425.2307692308[/C][/ROW]
[ROW][C]63[/C][C]91199[/C][C]106450.3125[/C][C]-15251.3125[/C][/ROW]
[ROW][C]64[/C][C]83419[/C][C]93575.0588235294[/C][C]-10156.0588235294[/C][/ROW]
[ROW][C]65[/C][C]101723[/C][C]93575.0588235294[/C][C]8147.94117647059[/C][/ROW]
[ROW][C]66[/C][C]94982[/C][C]106450.3125[/C][C]-11468.3125[/C][/ROW]
[ROW][C]67[/C][C]129700[/C][C]148556.392857143[/C][C]-18856.3928571429[/C][/ROW]
[ROW][C]68[/C][C]110708[/C][C]210949.4[/C][C]-100241.4[/C][/ROW]
[ROW][C]69[/C][C]81518[/C][C]93575.0588235294[/C][C]-12057.0588235294[/C][/ROW]
[ROW][C]70[/C][C]31970[/C][C]44404[/C][C]-12434[/C][/ROW]
[ROW][C]71[/C][C]192268[/C][C]210949.4[/C][C]-18681.4[/C][/ROW]
[ROW][C]72[/C][C]87611[/C][C]93575.0588235294[/C][C]-5964.05882352941[/C][/ROW]
[ROW][C]73[/C][C]77890[/C][C]93575.0588235294[/C][C]-15685.0588235294[/C][/ROW]
[ROW][C]74[/C][C]83261[/C][C]106450.3125[/C][C]-23189.3125[/C][/ROW]
[ROW][C]75[/C][C]116290[/C][C]124982.230769231[/C][C]-8692.23076923077[/C][/ROW]
[ROW][C]76[/C][C]55254[/C][C]44404[/C][C]10850[/C][/ROW]
[ROW][C]77[/C][C]116173[/C][C]106450.3125[/C][C]9722.6875[/C][/ROW]
[ROW][C]78[/C][C]111488[/C][C]148556.392857143[/C][C]-37068.3928571429[/C][/ROW]
[ROW][C]79[/C][C]60138[/C][C]65591.75[/C][C]-5453.75[/C][/ROW]
[ROW][C]80[/C][C]73422[/C][C]106450.3125[/C][C]-33028.3125[/C][/ROW]
[ROW][C]81[/C][C]67751[/C][C]65591.75[/C][C]2159.25[/C][/ROW]
[ROW][C]82[/C][C]213351[/C][C]148556.392857143[/C][C]64794.6071428571[/C][/ROW]
[ROW][C]83[/C][C]51185[/C][C]65591.75[/C][C]-14406.75[/C][/ROW]
[ROW][C]84[/C][C]97181[/C][C]106450.3125[/C][C]-9269.3125[/C][/ROW]
[ROW][C]85[/C][C]42311[/C][C]44404[/C][C]-2093[/C][/ROW]
[ROW][C]86[/C][C]115801[/C][C]106450.3125[/C][C]9350.6875[/C][/ROW]
[ROW][C]87[/C][C]183637[/C][C]210949.4[/C][C]-27312.4[/C][/ROW]
[ROW][C]88[/C][C]68161[/C][C]65591.75[/C][C]2569.25[/C][/ROW]
[ROW][C]89[/C][C]76441[/C][C]65591.75[/C][C]10849.25[/C][/ROW]
[ROW][C]90[/C][C]103613[/C][C]106450.3125[/C][C]-2837.3125[/C][/ROW]
[ROW][C]91[/C][C]98707[/C][C]148556.392857143[/C][C]-49849.3928571429[/C][/ROW]
[ROW][C]92[/C][C]126527[/C][C]148556.392857143[/C][C]-22029.3928571429[/C][/ROW]
[ROW][C]93[/C][C]136781[/C][C]148556.392857143[/C][C]-11775.3928571429[/C][/ROW]
[ROW][C]94[/C][C]105863[/C][C]106450.3125[/C][C]-587.3125[/C][/ROW]
[ROW][C]95[/C][C]38775[/C][C]65591.75[/C][C]-26816.75[/C][/ROW]
[ROW][C]96[/C][C]179984[/C][C]148556.392857143[/C][C]31427.6071428571[/C][/ROW]
[ROW][C]97[/C][C]164808[/C][C]148556.392857143[/C][C]16251.6071428571[/C][/ROW]
[ROW][C]98[/C][C]19349[/C][C]6617.57142857143[/C][C]12731.4285714286[/C][/ROW]
[ROW][C]99[/C][C]143902[/C][C]124982.230769231[/C][C]18919.7692307692[/C][/ROW]
[ROW][C]100[/C][C]108660[/C][C]124982.230769231[/C][C]-16322.2307692308[/C][/ROW]
[ROW][C]101[/C][C]43803[/C][C]44404[/C][C]-601[/C][/ROW]
[ROW][C]102[/C][C]47062[/C][C]65591.75[/C][C]-18529.75[/C][/ROW]
[ROW][C]103[/C][C]110845[/C][C]106450.3125[/C][C]4394.6875[/C][/ROW]
[ROW][C]104[/C][C]92517[/C][C]93575.0588235294[/C][C]-1058.05882352941[/C][/ROW]
[ROW][C]105[/C][C]58660[/C][C]65591.75[/C][C]-6931.75[/C][/ROW]
[ROW][C]106[/C][C]27676[/C][C]44404[/C][C]-16728[/C][/ROW]
[ROW][C]107[/C][C]98550[/C][C]93575.0588235294[/C][C]4974.94117647059[/C][/ROW]
[ROW][C]108[/C][C]43284[/C][C]44404[/C][C]-1120[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]6617.57142857143[/C][C]-6617.57142857143[/C][/ROW]
[ROW][C]110[/C][C]66016[/C][C]65591.75[/C][C]424.25[/C][/ROW]
[ROW][C]111[/C][C]57359[/C][C]65591.75[/C][C]-8232.75[/C][/ROW]
[ROW][C]112[/C][C]96933[/C][C]93575.0588235294[/C][C]3357.94117647059[/C][/ROW]
[ROW][C]113[/C][C]70369[/C][C]93575.0588235294[/C][C]-23206.0588235294[/C][/ROW]
[ROW][C]114[/C][C]65494[/C][C]65591.75[/C][C]-97.75[/C][/ROW]
[ROW][C]115[/C][C]3616[/C][C]6617.57142857143[/C][C]-3001.57142857143[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]6617.57142857143[/C][C]-6617.57142857143[/C][/ROW]
[ROW][C]117[/C][C]143931[/C][C]93575.0588235294[/C][C]50355.9411764706[/C][/ROW]
[ROW][C]118[/C][C]109894[/C][C]148556.392857143[/C][C]-38662.3928571429[/C][/ROW]
[ROW][C]119[/C][C]122973[/C][C]106450.3125[/C][C]16522.6875[/C][/ROW]
[ROW][C]120[/C][C]84336[/C][C]93575.0588235294[/C][C]-9239.05882352941[/C][/ROW]
[ROW][C]121[/C][C]43410[/C][C]44404[/C][C]-994[/C][/ROW]
[ROW][C]122[/C][C]136250[/C][C]148556.392857143[/C][C]-12306.3928571429[/C][/ROW]
[ROW][C]123[/C][C]79015[/C][C]65591.75[/C][C]13423.25[/C][/ROW]
[ROW][C]124[/C][C]92937[/C][C]106450.3125[/C][C]-13513.3125[/C][/ROW]
[ROW][C]125[/C][C]57586[/C][C]44404[/C][C]13182[/C][/ROW]
[ROW][C]126[/C][C]19764[/C][C]6617.57142857143[/C][C]13146.4285714286[/C][/ROW]
[ROW][C]127[/C][C]105757[/C][C]106450.3125[/C][C]-693.3125[/C][/ROW]
[ROW][C]128[/C][C]96410[/C][C]65591.75[/C][C]30818.25[/C][/ROW]
[ROW][C]129[/C][C]113402[/C][C]106450.3125[/C][C]6951.6875[/C][/ROW]
[ROW][C]130[/C][C]11796[/C][C]6617.57142857143[/C][C]5178.42857142857[/C][/ROW]
[ROW][C]131[/C][C]7627[/C][C]6617.57142857143[/C][C]1009.42857142857[/C][/ROW]
[ROW][C]132[/C][C]121085[/C][C]106450.3125[/C][C]14634.6875[/C][/ROW]
[ROW][C]133[/C][C]6836[/C][C]6617.57142857143[/C][C]218.428571428572[/C][/ROW]
[ROW][C]134[/C][C]139563[/C][C]124982.230769231[/C][C]14580.7692307692[/C][/ROW]
[ROW][C]135[/C][C]5118[/C][C]6617.57142857143[/C][C]-1499.57142857143[/C][/ROW]
[ROW][C]136[/C][C]40248[/C][C]44404[/C][C]-4156[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]6617.57142857143[/C][C]-6617.57142857143[/C][/ROW]
[ROW][C]138[/C][C]95079[/C][C]65591.75[/C][C]29487.25[/C][/ROW]
[ROW][C]139[/C][C]80750[/C][C]106450.3125[/C][C]-25700.3125[/C][/ROW]
[ROW][C]140[/C][C]7131[/C][C]6617.57142857143[/C][C]513.428571428572[/C][/ROW]
[ROW][C]141[/C][C]4194[/C][C]6617.57142857143[/C][C]-2423.57142857143[/C][/ROW]
[ROW][C]142[/C][C]60378[/C][C]44404[/C][C]15974[/C][/ROW]
[ROW][C]143[/C][C]96971[/C][C]93575.0588235294[/C][C]3395.94117647059[/C][/ROW]
[ROW][C]144[/C][C]83484[/C][C]93575.0588235294[/C][C]-10091.0588235294[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155007&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155007&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	127476	106450.3125	21025.6875
2	130358	148556.392857143	-18198.3928571429
3	7215	6617.57142857143	597.428571428572
4	112861	124982.230769231	-12121.2307692308
5	210171	210949.4	-778.399999999994
6	393802	210949.4	182852.6
7	117604	148556.392857143	-30952.3928571429
8	126029	148556.392857143	-22527.3928571429
9	99729	106450.3125	-6721.3125
10	256310	210949.4	45360.6
11	113066	124982.230769231	-11916.2307692308
12	156212	148556.392857143	7655.60714285713
13	69952	106450.3125	-36498.3125
14	152673	210949.4	-58276.4
15	125841	106450.3125	19390.6875
16	125769	106450.3125	19318.6875
17	123467	148556.392857143	-25089.3928571429
18	56232	44404	11828
19	108244	124982.230769231	-16738.2307692308
20	22762	44404	-21642
21	48554	44404	4150
22	178697	148556.392857143	30140.6071428571
23	139115	106450.3125	32664.6875
24	93773	106450.3125	-12677.3125
25	132796	148556.392857143	-15760.3928571429
26	113933	106450.3125	7482.6875
27	144781	148556.392857143	-3775.39285714287
28	140711	148556.392857143	-7845.39285714287
29	283337	210949.4	72387.6
30	158146	148556.392857143	9589.60714285713
31	123344	106450.3125	16893.6875
32	157640	148556.392857143	9083.60714285713
33	91279	124982.230769231	-33703.2307692308
34	189374	210949.4	-21575.4
35	167915	148556.392857143	19358.6071428571
36	0	6617.57142857143	-6617.57142857143
37	175403	124982.230769231	50420.7692307692
38	92342	106450.3125	-14108.3125
39	100023	106450.3125	-6427.3125
40	178277	148556.392857143	29720.6071428571
41	145062	124982.230769231	20079.7692307692
42	110980	106450.3125	4529.6875
43	86039	93575.0588235294	-7536.05882352941
44	119514	148556.392857143	-29042.3928571429
45	95535	93575.0588235294	1959.94117647059
46	109894	93575.0588235294	16318.9411764706
47	61554	65591.75	-4037.75
48	156520	124982.230769231	31537.7692307692
49	159121	148556.392857143	10564.6071428571
50	129362	124982.230769231	4379.76923076923
51	48188	44404	3784
52	91198	106450.3125	-15252.3125
53	229864	148556.392857143	81307.6071428571
54	180317	148556.392857143	31760.6071428571
55	150640	148556.392857143	2083.60714285713
56	104416	106450.3125	-2034.3125
57	159645	106450.3125	53194.6875
58	60368	65591.75	-5223.75
59	100056	93575.0588235294	6480.94117647059
60	137214	210949.4	-73735.4
61	99630	106450.3125	-6820.3125
62	84557	124982.230769231	-40425.2307692308
63	91199	106450.3125	-15251.3125
64	83419	93575.0588235294	-10156.0588235294
65	101723	93575.0588235294	8147.94117647059
66	94982	106450.3125	-11468.3125
67	129700	148556.392857143	-18856.3928571429
68	110708	210949.4	-100241.4
69	81518	93575.0588235294	-12057.0588235294
70	31970	44404	-12434
71	192268	210949.4	-18681.4
72	87611	93575.0588235294	-5964.05882352941
73	77890	93575.0588235294	-15685.0588235294
74	83261	106450.3125	-23189.3125
75	116290	124982.230769231	-8692.23076923077
76	55254	44404	10850
77	116173	106450.3125	9722.6875
78	111488	148556.392857143	-37068.3928571429
79	60138	65591.75	-5453.75
80	73422	106450.3125	-33028.3125
81	67751	65591.75	2159.25
82	213351	148556.392857143	64794.6071428571
83	51185	65591.75	-14406.75
84	97181	106450.3125	-9269.3125
85	42311	44404	-2093
86	115801	106450.3125	9350.6875
87	183637	210949.4	-27312.4
88	68161	65591.75	2569.25
89	76441	65591.75	10849.25
90	103613	106450.3125	-2837.3125
91	98707	148556.392857143	-49849.3928571429
92	126527	148556.392857143	-22029.3928571429
93	136781	148556.392857143	-11775.3928571429
94	105863	106450.3125	-587.3125
95	38775	65591.75	-26816.75
96	179984	148556.392857143	31427.6071428571
97	164808	148556.392857143	16251.6071428571
98	19349	6617.57142857143	12731.4285714286
99	143902	124982.230769231	18919.7692307692
100	108660	124982.230769231	-16322.2307692308
101	43803	44404	-601
102	47062	65591.75	-18529.75
103	110845	106450.3125	4394.6875
104	92517	93575.0588235294	-1058.05882352941
105	58660	65591.75	-6931.75
106	27676	44404	-16728
107	98550	93575.0588235294	4974.94117647059
108	43284	44404	-1120
109	0	6617.57142857143	-6617.57142857143
110	66016	65591.75	424.25
111	57359	65591.75	-8232.75
112	96933	93575.0588235294	3357.94117647059
113	70369	93575.0588235294	-23206.0588235294
114	65494	65591.75	-97.75
115	3616	6617.57142857143	-3001.57142857143
116	0	6617.57142857143	-6617.57142857143
117	143931	93575.0588235294	50355.9411764706
118	109894	148556.392857143	-38662.3928571429
119	122973	106450.3125	16522.6875
120	84336	93575.0588235294	-9239.05882352941
121	43410	44404	-994
122	136250	148556.392857143	-12306.3928571429
123	79015	65591.75	13423.25
124	92937	106450.3125	-13513.3125
125	57586	44404	13182
126	19764	6617.57142857143	13146.4285714286
127	105757	106450.3125	-693.3125
128	96410	65591.75	30818.25
129	113402	106450.3125	6951.6875
130	11796	6617.57142857143	5178.42857142857
131	7627	6617.57142857143	1009.42857142857
132	121085	106450.3125	14634.6875
133	6836	6617.57142857143	218.428571428572
134	139563	124982.230769231	14580.7692307692
135	5118	6617.57142857143	-1499.57142857143
136	40248	44404	-4156
137	0	6617.57142857143	-6617.57142857143
138	95079	65591.75	29487.25
139	80750	106450.3125	-25700.3125
140	7131	6617.57142857143	513.428571428572
141	4194	6617.57142857143	-2423.57142857143
142	60378	44404	15974
143	96971	93575.0588235294	3395.94117647059
144	83484	93575.0588235294	-10091.0588235294

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):

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code