Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationMon, 17 Dec 2012 09:30:52 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/17/t1355754688evi9mhdmwskgypx.htm/, Retrieved Thu, 28 Mar 2024 20:32:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200966, Retrieved Thu, 28 Mar 2024 20:32:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact187
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2012-12-02 10:36:10] [b98453cac15ba1066b407e146608df68]
- RM      [Recursive Partitioning (Regression Trees)] [] [2012-12-17 14:30:52] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
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Dataseries X:
102	122	88	1	9	8976	102	918	88	792	9
99	114	106	1	3	10494	99	297	106	318	3
97	140	70	1	2	6790	97	194	70	140	2
82	143	70	1	21	5740	82	1722	70	1470	21
77	122	56	1	10	4312	77	770	56	560	10
65	127	50	1	2	3250	65	130	50	100	2
64	113	48	1	4	3072	64	256	48	192	4
62	118	71	1	4	4402	62	248	71	284	4
62	161	61	1	4	3782	62	248	61	244	4
62	134	66	1	5	4092	62	310	66	330	5
61	96	80	1	2	4880	61	122	80	160	2
59	104	37	1	3	2183	59	177	37	111	3
57	135	53	1	3	3021	57	171	53	159	3
56	110	39	1	6	2184	56	336	39	234	6
54	128	40	1	3	2160	54	162	40	120	3
54	142	59	1	4	3186	54	216	59	236	4
53	117	42	1	3	2226	53	159	42	126	3
52	94	33	1	13	1716	52	676	33	429	13
51	135	36	1	3	1836	51	153	36	108	3
51	121	57	1	4	2907	51	204	57	228	4
51	103	38	1	8	1938	51	408	38	304	8
50	118	98	1	8	4900	50	400	98	784	8
50	127	43	1	3	2150	50	150	43	129	3
50	116	73	1	4	3650	50	200	73	292	4
49	129	52	1	2	2548	49	98	52	104	2
49	115	53	1	5	2597	49	245	53	265	5
49	135	51	1	4	2499	49	196	51	204	4
48	133	32	1	3	1536	48	144	32	96	3
48	113	43	1	3	2064	48	144	43	129	3
47	111	53	1	2	2491	47	94	53	106	2
47	92	50	1	3	2350	47	141	50	150	3
46	118	50	1	3	2300	46	138	50	150	3
46	134	56	1	3	2576	46	138	56	168	3
45	106	53	1	5	2385	45	225	53	265	5
45	137	47	1	3	2115	45	135	47	141	3
45	100	42	1	4	1890	45	180	42	168	4
44	102	29	1	3	1276	44	132	29	87	3
43	134	54	1	4	2322	43	172	54	216	4
42	130	40	1	8	1680	42	336	40	320	8
42	144	41	1	3	1722	42	126	41	123	3
42	120	37	1	4	1554	42	168	37	148	4
42	91	25	1	2	1050	42	84	25	50	2
42	100	27	1	5	1134	42	210	27	135	5
42	134	61	1	4	2562	42	168	61	244	4
41	161	54	1	7	2214	41	287	54	378	7
41	128	35	1	3	1435	41	123	35	105	3
41	124	55	1	4	2255	41	164	55	220	4
41	115	47	1	6	1927	41	246	47	282	6
41	123	49	1	7	2009	41	287	49	343	7
41	117	38	1	20	1558	41	820	38	760	20
41	111	52	1	49	2132	41	2009	52	2548	49
40	146	35	1	3	1400	40	120	35	105	3
40	101	52	1	3	2080	40	120	52	156	3
40	131	54	1	6	2160	40	240	54	324	6
40	122	40	1	6	1600	40	240	40	240	6
40	78	52	1	4	2080	40	160	52	208	4
39	120	34	1	5	1326	39	195	34	170	5
39	115	51	1	4	1989	39	156	51	204	4
38	142	43	1	4	1634	38	152	43	172	4
38	94	40	1	31	1520	38	1178	40	1240	31
36	114	38	1	3	1368	36	108	38	114	3
36	108	33	1	3	1188	36	108	33	99	3
35	119	27	1	4	945	35	140	27	108	4
35	117	34	1	6	1190	35	210	34	204	6
35	86	44	1	5	1540	35	175	44	220	5
35	138	46	1	3	1610	35	105	46	138	3
34	119	50	1	3	1700	34	102	50	150	3
34	117	31	1	2	1054	34	68	31	62	2
34	117	33	1	3	1122	34	102	33	99	3
33	76	37	1	3	1221	33	99	37	111	3
33	119	48	1	16	1584	33	528	48	768	16
33	119	33	1	3	1089	33	99	33	99	3
32	124	40	1	3	1280	32	96	40	120	3
32	116	21	1	3	672	32	96	21	63	3
32	118	33	1	2	1056	32	64	33	66	2
31	102	41	1	5	1271	31	155	41	205	5
31	116	35	1	3	1085	31	93	35	105	3
30	103	60	1	4	1800	30	120	60	240	4
30	117	30	1	5	900	30	150	30	150	5
30	108	45	1	2	1350	30	60	45	90	2
30	122	26	1	3	780	30	90	26	78	3
29	90	41	1	3	1189	29	87	41	123	3
28	133	48	1	14	1344	28	392	48	672	14
28	116	10	1	8	280	28	224	10	80	8
27	110	35	1	4	945	27	108	35	140	4
27	90	23	1	4	621	27	108	23	92	4
27	74	29	1	3	783	27	81	29	87	3
26	75	17	1	4	442	26	104	17	68	4
25	107	35	1	3	875	25	75	35	105	3
25	90	50	1	5	1250	25	125	50	250	5
25	96	33	1	3	825	25	75	33	99	3
24	115	23	1	3	552	24	72	23	69	3
24	91	22	1	2	528	24	48	22	44	2
23	77	52	1	4	1196	23	92	52	208	4
23	108	38	1	31	874	23	713	38	1178	31
23	83	32	1	2	736	23	46	32	64	2
23	77	28	1	5	644	23	115	28	140	5
23	99	43	1	5	989	23	115	43	215	5
22	115	32	1	2	704	22	44	32	64	2
22	99	35	1	2	770	22	44	35	70	2
22	106	25	1	3	550	22	66	25	75	3
22	77	14	1	8	308	22	176	14	112	8
20	115	17	1	6	340	20	120	17	102	6
19	67	18	1	3	342	19	57	18	54	3
19	8	12	1	2	228	19	38	12	24	2
17	69	27	1	5	459	17	85	27	135	5
17	88	28	1	3	476	17	51	28	84	3
16	107	12	1	5	192	16	80	12	60	5
16	120	21	1	5	336	16	80	21	105	5
5	3	9	1	4	45	5	20	9	36	4
4	1	11	1	2	44	4	8	11	22	2
3	0	3	1	4	9	3	12	3	12	4
156	111	111	0	4	17316	0	624	0	444	0
109	69	137	0	8	14933	0	872	0	1096	0
104	116	112	0	3	11648	0	312	0	336	0
98	103	73	0	4	7154	0	392	0	292	0
78	139	99	0	3	7722	0	234	0	297	0
77	135	115	0	3	8855	0	231	0	345	0
73	113	95	0	3	6935	0	219	0	285	0
71	99	60	0	4	4260	0	284	0	240	0
67	76	94	0	4	6298	0	268	0	376	0
64	110	70	0	5	4480	0	320	0	350	0
62	121	87	0	2	5394	0	124	0	174	0
61	95	102	0	3	6222	0	183	0	306	0
58	66	69	0	4	4002	0	232	0	276	0
58	111	111	0	7	6438	0	406	0	777	0
56	77	55	0	3	3080	0	168	0	165	0
56	101	118	0	4	6608	0	224	0	472	0
52	108	90	0	3	4680	0	156	0	270	0
51	135	81	0	4	4131	0	204	0	324	0
51	70	88	0	4	4488	0	204	0	352	0
50	124	63	0	3	3150	0	150	0	189	0
49	92	84	0	6	4116	0	294	0	504	0
49	104	87	0	4	4263	0	196	0	348	0
48	113	78	0	4	3744	0	192	0	312	0
47	95	93	0	4	4371	0	188	0	372	0
47	89	69	0	4	3243	0	188	0	276	0
46	83	67	0	3	3082	0	138	0	201	0
45	96	61	0	6	2745	0	270	0	366	0
45	95	123	0	4	5535	0	180	0	492	0
45	110	91	0	6	4095	0	270	0	546	0
45	106	98	0	6	4410	0	270	0	588	0
44	78	38	0	2	1672	0	88	0	76	0
44	115	72	0	3	3168	0	132	0	216	0
44	74	59	0	3	2596	0	132	0	177	0
43	93	78	0	2	3354	0	86	0	156	0
43	88	58	0	4	2494	0	172	0	232	0
42	104	97	0	5	4074	0	210	0	485	0
41	86	69	0	3	2829	0	123	0	207	0
41	104	50	0	7	2050	0	287	0	350	0
40	99	66	0	4	2640	0	160	0	264	0
39	101	70	0	3	2730	0	117	0	210	0
39	53	65	0	4	2535	0	156	0	260	0
39	96	69	0	4	2691	0	156	0	276	0
39	58	49	0	3	1911	0	117	0	147	0
39	117	72	0	3	2808	0	117	0	216	0
39	82	74	0	4	2886	0	156	0	296	0
39	57	82	0	5	3198	0	195	0	410	0
38	71	61	0	3	2318	0	114	0	183	0
38	105	72	0	4	2736	0	152	0	288	0
38	60	77	0	6	2926	0	228	0	462	0
38	77	64	0	5	2432	0	190	0	320	0
37	73	23	0	7	851	0	259	0	161	0
37	78	39	0	5	1443	0	185	0	195	0
37	81	87	0	5	3219	0	185	0	435	0
36	101	46	0	3	1656	0	108	0	138	0
36	118	66	0	5	2376	0	180	0	330	0
36	59	57	0	4	2052	0	144	0	228	0
36	101	48	0	9	1728	0	324	0	432	0
36	22	75	0	3	2700	0	108	0	225	0
36	77	35	0	3	1260	0	108	0	105	0
35	100	53	0	3	1855	0	105	0	159	0
35	39	60	0	3	2100	0	105	0	180	0
34	42	20	0	15	680	0	510	0	300	0
34	80	66	0	4	2244	0	136	0	264	0
34	48	34	0	6	1156	0	204	0	204	0
34	131	80	0	3	2720	0	102	0	240	0
34	46	63	0	3	2142	0	102	0	189	0
33	89	46	0	3	1518	0	99	0	138	0
33	51	20	0	5	660	0	165	0	100	0
33	108	73	0	3	2409	0	99	0	219	0
33	86	57	0	4	1881	0	132	0	228	0
33	105	65	0	7	2145	0	231	0	455	0
33	85	70	0	4	2310	0	132	0	280	0
32	103	53	0	5	1696	0	160	0	265	0
32	83	60	0	7	1920	0	224	0	420	0
32	77	34	0	14	1088	0	448	0	476	0
32	26	18	0	25	576	0	800	0	450	0
32	73	49	0	6	1568	0	192	0	294	0
31	42	27	0	4	837	0	124	0	108	0
31	71	45	0	3	1395	0	93	0	135	0
31	105	9	0	62	279	0	1922	0	558	0
30	73	23	0	5	690	0	150	0	115	0
30	98	61	0	10	1830	0	300	0	610	0
29	108	67	0	4	1943	0	116	0	268	0
29	57	72	0	5	2088	0	145	0	360	0
29	37	58	0	5	1682	0	145	0	290	0
28	70	55	0	4	1540	0	112	0	220	0
28	73	33	0	10	924	0	280	0	330	0
28	47	40	0	5	1120	0	140	0	200	0
28	73	57	0	3	1596	0	84	0	171	0
28	91	61	0	3	1708	0	84	0	183	0
28	110	87	0	17	2436	0	476	0	1479	0
27	78	65	0	4	1755	0	108	0	260	0
27	92	85	0	6	2295	0	162	0	510	0
27	52	85	0	3	2295	0	81	0	255	0
26	88	54	0	4	1404	0	104	0	216	0
26	100	24	0	8	624	0	208	0	192	0
26	33	31	0	3	806	0	78	0	93	0
26	42	64	0	4	1664	0	104	0	256	0
25	81	70	0	4	1750	0	100	0	280	0
25	67	2	0	47	50	0	1175	0	94	0
24	8	27	0	8	648	0	192	0	216	0
24	46	29	0	3	696	0	72	0	87	0
24	83	68	0	5	1632	0	120	0	340	0
24	87	42	0	3	1008	0	72	0	126	0
24	82	78	0	4	1872	0	96	0	312	0
24	63	13	0	30	312	0	720	0	390	0
24	27	52	0	4	1248	0	96	0	208	0
23	14	25	0	12	575	0	276	0	300	0
23	83	38	0	8	874	0	184	0	304	0
23	168	40	0	3	920	0	69	0	120	0
23	67	42	0	8	966	0	184	0	336	0
23	21	40	0	4	920	0	92	0	160	0
23	55	74	0	3	1702	0	69	0	222	0
23	54	73	0	4	1679	0	92	0	292	0
22	118	56	0	4	1232	0	88	0	224	0
22	69	3	0	21	66	0	462	0	63	0
21	77	9	0	12	189	0	252	0	108	0
21	72	68	0	3	1428	0	63	0	204	0
21	53	28	0	3	588	0	63	0	84	0
21	40	36	0	4	756	0	84	0	144	0
20	102	38	0	6	760	0	120	0	228	0
20	25	55	0	17	1100	0	340	0	935	0
20	31	36	0	3	720	0	60	0	108	0
20	77	17	0	18	340	0	360	0	306	0
20	38	54	0	5	1080	0	100	0	270	0
19	23	57	0	5	1083	0	95	0	285	0
19	91	30	0	16	570	0	304	0	480	0
19	58	40	0	10	760	0	190	0	400	0
18	42	37	0	5	666	0	90	0	185	0
18	44	46	0	4	828	0	72	0	184	0
18	58	32	0	3	576	0	54	0	96	0
18	35	34	0	5	612	0	90	0	170	0
18	88	22	0	4	396	0	72	0	88	0
17	25	59	0	5	1003	0	85	0	295	0
17	39	32	0	10	544	0	170	0	320	0
16	48	18	0	12	288	0	192	0	216	0
16	64	28	0	4	448	0	64	0	112	0
15	65	34	0	9	510	0	135	0	306	0
15	95	29	0	12	435	0	180	0	348	0
15	29	24	0	10	360	0	150	0	240	0
15	2	24	0	9	360	0	135	0	216	0
14	83	23	0	17	322	0	238	0	391	0
13	11	43	0	6	559	0	78	0	258	0
13	16	28	0	3	364	0	39	0	84	0
12	9	19	0	4	228	0	48	0	76	0
11	46	16	0	19	176	0	209	0	304	0
11	41	40	0	3	440	0	33	0	120	0
10	14	14	0	9	140	0	90	0	126	0
10	63	19	0	7	190	0	70	0	133	0
10	9	22	0	4	220	0	40	0	88	0
10	0	8	0	3	80	0	30	0	24	0
9	58	31	0	46	279	0	414	0	1426	0
8	18	9	0	31	72	0	248	0	279	0
8	42	18	0	21	144	0	168	0	378	0
7	26	9	0	7	63	0	49	0	63	0
7	38	5	0	29	35	0	203	0	145	0
4	1	11	0	5	44	0	20	0	55	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200966&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200966&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200966&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Goodness of Fit
Correlation0.7746
R-squared0.6
RMSE22.6301

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.7746 \tabularnewline
R-squared & 0.6 \tabularnewline
RMSE & 22.6301 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200966&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.7746[/C][/ROW]
[ROW][C]R-squared[/C][C]0.6[/C][/ROW]
[ROW][C]RMSE[/C][C]22.6301[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200966&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200966&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
Correlation0.7746
R-squared0.6
RMSE22.6301







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1122118.5714285714293.42857142857143
2114118.571428571429-4.57142857142857
3140118.57142857142921.4285714285714
4143118.57142857142924.4285714285714
5122118.5714285714293.42857142857143
6127118.5714285714298.42857142857143
7113118.571428571429-5.57142857142857
8118118.571428571429-0.571428571428569
9161118.57142857142942.4285714285714
10134118.57142857142915.4285714285714
1196118.571428571429-22.5714285714286
12104118.571428571429-14.5714285714286
13135118.57142857142916.4285714285714
14110118.571428571429-8.57142857142857
15128118.5714285714299.42857142857143
16142118.57142857142923.4285714285714
17117118.571428571429-1.57142857142857
1894118.571428571429-24.5714285714286
19135118.57142857142916.4285714285714
20121118.5714285714292.42857142857143
21103118.571428571429-15.5714285714286
22118118.571428571429-0.571428571428569
23127118.5714285714298.42857142857143
24116118.571428571429-2.57142857142857
25129118.57142857142910.4285714285714
26115118.571428571429-3.57142857142857
27135118.57142857142916.4285714285714
28133118.57142857142914.4285714285714
29113118.571428571429-5.57142857142857
30111118.571428571429-7.57142857142857
3192118.571428571429-26.5714285714286
32118118.571428571429-0.571428571428569
33134118.57142857142915.4285714285714
34106118.571428571429-12.5714285714286
35137118.57142857142918.4285714285714
36100118.571428571429-18.5714285714286
37102118.571428571429-16.5714285714286
38134118.57142857142915.4285714285714
39130118.57142857142911.4285714285714
40144118.57142857142925.4285714285714
41120118.5714285714291.42857142857143
4291118.571428571429-27.5714285714286
43100118.571428571429-18.5714285714286
44134118.57142857142915.4285714285714
45161118.57142857142942.4285714285714
46128118.5714285714299.42857142857143
47124118.5714285714295.42857142857143
48115118.571428571429-3.57142857142857
49123118.5714285714294.42857142857143
50117118.571428571429-1.57142857142857
51111118.571428571429-7.57142857142857
52146118.57142857142927.4285714285714
53101118.571428571429-17.5714285714286
54131118.57142857142912.4285714285714
55122118.5714285714293.42857142857143
5678118.571428571429-40.5714285714286
57120118.5714285714291.42857142857143
58115118.571428571429-3.57142857142857
59142118.57142857142923.4285714285714
6094118.571428571429-24.5714285714286
61114118.571428571429-4.57142857142857
62108118.571428571429-10.5714285714286
63119118.5714285714290.428571428571431
64117118.571428571429-1.57142857142857
6586118.571428571429-32.5714285714286
66138118.57142857142919.4285714285714
67119118.5714285714290.428571428571431
68117118.571428571429-1.57142857142857
69117118.571428571429-1.57142857142857
7076118.571428571429-42.5714285714286
71119118.5714285714290.428571428571431
72119118.5714285714290.428571428571431
73124118.5714285714295.42857142857143
74116118.571428571429-2.57142857142857
75118118.571428571429-0.571428571428569
76102118.571428571429-16.5714285714286
77116118.571428571429-2.57142857142857
78103118.571428571429-15.5714285714286
79117118.571428571429-1.57142857142857
80108118.571428571429-10.5714285714286
81122118.5714285714293.42857142857143
8290118.571428571429-28.5714285714286
83133118.57142857142914.4285714285714
84116118.571428571429-2.57142857142857
8511094.947368421052615.0526315789474
869094.9473684210526-4.94736842105263
877494.9473684210526-20.9473684210526
887594.9473684210526-19.9473684210526
8910794.947368421052612.0526315789474
909094.9473684210526-4.94736842105263
919694.94736842105261.05263157894737
9211594.947368421052620.0526315789474
939194.9473684210526-3.94736842105263
947794.9473684210526-17.9473684210526
9510894.947368421052613.0526315789474
968394.9473684210526-11.9473684210526
977794.9473684210526-17.9473684210526
989994.94736842105264.05263157894737
9911594.947368421052620.0526315789474
1009994.94736842105264.05263157894737
10110694.947368421052611.0526315789474
1027794.9473684210526-17.9473684210526
10311594.947368421052620.0526315789474
1046765.74074074074071.25925925925925
105865.7407407407407-57.7407407407407
1066965.74074074074073.25925925925925
1078865.740740740740722.2592592592593
10810765.740740740740741.2592592592593
10912065.740740740740754.2592592592593
110322-19
111122-21
112022-22
113111102.5151515151528.48484848484848
11469102.515151515152-33.5151515151515
115116102.51515151515213.4848484848485
116103102.5151515151520.484848484848484
117139102.51515151515236.4848484848485
118135102.51515151515232.4848484848485
119113102.51515151515210.4848484848485
1209965.740740740740733.2592592592593
12176102.515151515152-26.5151515151515
122110102.5151515151527.48484848484848
123121102.51515151515218.4848484848485
12495102.515151515152-7.51515151515152
12566102.515151515152-36.5151515151515
126111102.5151515151528.48484848484848
1277765.740740740740711.2592592592593
128101102.515151515152-1.51515151515152
129108102.5151515151525.48484848484848
130135102.51515151515232.4848484848485
13170102.515151515152-32.5151515151515
132124102.51515151515221.4848484848485
13392102.515151515152-10.5151515151515
134104102.5151515151521.48484848484848
135113102.51515151515210.4848484848485
13695102.515151515152-7.51515151515152
13789102.515151515152-13.5151515151515
13883102.515151515152-19.5151515151515
13996102.515151515152-6.51515151515152
14095102.515151515152-7.51515151515152
141110102.5151515151527.48484848484848
142106102.5151515151523.48484848484848
1437865.740740740740712.2592592592593
144115102.51515151515212.4848484848485
1457465.74074074074078.25925925925925
14693102.515151515152-9.51515151515152
1478865.740740740740722.2592592592593
148104102.5151515151521.48484848484848
14986102.515151515152-16.5151515151515
15010465.740740740740738.2592592592593
15199102.515151515152-3.51515151515152
15210180.882352941176520.1176470588235
1535380.8823529411765-27.8823529411765
1549680.882352941176515.1176470588235
1555865.7407407407407-7.74074074074075
15611780.882352941176536.1176470588235
1578280.88235294117651.11764705882354
1585780.8823529411765-23.8823529411765
1597180.8823529411765-9.88235294117646
16010580.882352941176524.1176470588235
1616080.8823529411765-20.8823529411765
1627780.8823529411765-3.88235294117646
1637365.74074074074077.25925925925925
1647865.740740740740712.2592592592593
1658180.88235294117650.117647058823536
16610165.740740740740735.2592592592593
16711880.882352941176537.1176470588235
1685965.7407407407407-6.74074074074075
16910165.740740740740735.2592592592593
1702280.8823529411765-58.8823529411765
1717765.740740740740711.2592592592593
17210065.740740740740734.2592592592593
1733965.7407407407407-26.7407407407407
1744265.7407407407407-23.7407407407407
1758080.8823529411765-0.882352941176464
1764865.7407407407407-17.7407407407407
17713180.882352941176550.1176470588235
1784680.8823529411765-34.8823529411765
1798965.740740740740723.2592592592593
1805165.7407407407407-14.7407407407407
18110880.882352941176527.1176470588235
1828665.740740740740720.2592592592593
18310580.882352941176524.1176470588235
1848580.88235294117654.11764705882354
18510365.740740740740737.2592592592593
1868365.740740740740717.2592592592593
1877765.740740740740711.2592592592593
1882665.7407407407407-39.7407407407407
1897365.74074074074077.25925925925925
1904265.7407407407407-23.7407407407407
1917165.74074074074075.25925925925925
19210565.740740740740739.2592592592593
1937365.74074074074077.25925925925925
1949880.882352941176517.1176470588235
19510880.882352941176527.1176470588235
1965780.8823529411765-23.8823529411765
1973765.7407407407407-28.7407407407407
1987065.74074074074074.25925925925925
1997365.74074074074077.25925925925925
2004765.7407407407407-18.7407407407407
2017365.74074074074077.25925925925925
2029180.882352941176510.1176470588235
20311080.882352941176529.1176470588235
2047880.8823529411765-2.88235294117646
2059280.882352941176511.1176470588235
2065280.8823529411765-28.8823529411765
2078865.740740740740722.2592592592593
20810065.740740740740734.2592592592593
2093365.7407407407407-32.7407407407407
2104280.8823529411765-38.8823529411765
2118180.88235294117650.117647058823536
2126765.74074074074071.25925925925925
213865.7407407407407-57.7407407407407
2144665.7407407407407-19.7407407407407
2158380.88235294117652.11764705882354
2168765.740740740740721.2592592592593
2178280.88235294117651.11764705882354
2186365.7407407407407-2.74074074074075
2192765.7407407407407-38.7407407407407
2201465.7407407407407-51.7407407407407
2218365.740740740740717.2592592592593
22216865.7407407407407102.259259259259
2236765.74074074074071.25925925925925
2242165.7407407407407-44.7407407407407
2255580.8823529411765-25.8823529411765
2265480.8823529411765-26.8823529411765
22711865.740740740740752.2592592592593
2286965.74074074074073.25925925925925
2297765.740740740740711.2592592592593
2307280.8823529411765-8.88235294117646
2315365.7407407407407-12.7407407407407
2324065.7407407407407-25.7407407407407
23310265.740740740740736.2592592592593
2342565.7407407407407-40.7407407407407
2353165.7407407407407-34.7407407407407
2367765.740740740740711.2592592592593
2373865.7407407407407-27.7407407407407
2382365.7407407407407-42.7407407407407
2399165.740740740740725.2592592592593
2405865.7407407407407-7.74074074074075
2414265.7407407407407-23.7407407407407
2424465.7407407407407-21.7407407407407
2435865.7407407407407-7.74074074074075
2443565.7407407407407-30.7407407407407
2458865.740740740740722.2592592592593
2462565.7407407407407-40.7407407407407
2473965.7407407407407-26.7407407407407
2484865.7407407407407-17.7407407407407
2496465.7407407407407-1.74074074074075
2506565.7407407407407-0.740740740740748
2519565.740740740740729.2592592592593
2522965.7407407407407-36.7407407407407
253265.7407407407407-63.7407407407407
2548365.740740740740717.2592592592593
2551122-11
2561622-6
257922-13
258462224
259412219
2601422-8
261632241
262922-13
263022-22
264582236
2651822-4
266422220
26726224
268382216
269122-21

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 122 & 118.571428571429 & 3.42857142857143 \tabularnewline
2 & 114 & 118.571428571429 & -4.57142857142857 \tabularnewline
3 & 140 & 118.571428571429 & 21.4285714285714 \tabularnewline
4 & 143 & 118.571428571429 & 24.4285714285714 \tabularnewline
5 & 122 & 118.571428571429 & 3.42857142857143 \tabularnewline
6 & 127 & 118.571428571429 & 8.42857142857143 \tabularnewline
7 & 113 & 118.571428571429 & -5.57142857142857 \tabularnewline
8 & 118 & 118.571428571429 & -0.571428571428569 \tabularnewline
9 & 161 & 118.571428571429 & 42.4285714285714 \tabularnewline
10 & 134 & 118.571428571429 & 15.4285714285714 \tabularnewline
11 & 96 & 118.571428571429 & -22.5714285714286 \tabularnewline
12 & 104 & 118.571428571429 & -14.5714285714286 \tabularnewline
13 & 135 & 118.571428571429 & 16.4285714285714 \tabularnewline
14 & 110 & 118.571428571429 & -8.57142857142857 \tabularnewline
15 & 128 & 118.571428571429 & 9.42857142857143 \tabularnewline
16 & 142 & 118.571428571429 & 23.4285714285714 \tabularnewline
17 & 117 & 118.571428571429 & -1.57142857142857 \tabularnewline
18 & 94 & 118.571428571429 & -24.5714285714286 \tabularnewline
19 & 135 & 118.571428571429 & 16.4285714285714 \tabularnewline
20 & 121 & 118.571428571429 & 2.42857142857143 \tabularnewline
21 & 103 & 118.571428571429 & -15.5714285714286 \tabularnewline
22 & 118 & 118.571428571429 & -0.571428571428569 \tabularnewline
23 & 127 & 118.571428571429 & 8.42857142857143 \tabularnewline
24 & 116 & 118.571428571429 & -2.57142857142857 \tabularnewline
25 & 129 & 118.571428571429 & 10.4285714285714 \tabularnewline
26 & 115 & 118.571428571429 & -3.57142857142857 \tabularnewline
27 & 135 & 118.571428571429 & 16.4285714285714 \tabularnewline
28 & 133 & 118.571428571429 & 14.4285714285714 \tabularnewline
29 & 113 & 118.571428571429 & -5.57142857142857 \tabularnewline
30 & 111 & 118.571428571429 & -7.57142857142857 \tabularnewline
31 & 92 & 118.571428571429 & -26.5714285714286 \tabularnewline
32 & 118 & 118.571428571429 & -0.571428571428569 \tabularnewline
33 & 134 & 118.571428571429 & 15.4285714285714 \tabularnewline
34 & 106 & 118.571428571429 & -12.5714285714286 \tabularnewline
35 & 137 & 118.571428571429 & 18.4285714285714 \tabularnewline
36 & 100 & 118.571428571429 & -18.5714285714286 \tabularnewline
37 & 102 & 118.571428571429 & -16.5714285714286 \tabularnewline
38 & 134 & 118.571428571429 & 15.4285714285714 \tabularnewline
39 & 130 & 118.571428571429 & 11.4285714285714 \tabularnewline
40 & 144 & 118.571428571429 & 25.4285714285714 \tabularnewline
41 & 120 & 118.571428571429 & 1.42857142857143 \tabularnewline
42 & 91 & 118.571428571429 & -27.5714285714286 \tabularnewline
43 & 100 & 118.571428571429 & -18.5714285714286 \tabularnewline
44 & 134 & 118.571428571429 & 15.4285714285714 \tabularnewline
45 & 161 & 118.571428571429 & 42.4285714285714 \tabularnewline
46 & 128 & 118.571428571429 & 9.42857142857143 \tabularnewline
47 & 124 & 118.571428571429 & 5.42857142857143 \tabularnewline
48 & 115 & 118.571428571429 & -3.57142857142857 \tabularnewline
49 & 123 & 118.571428571429 & 4.42857142857143 \tabularnewline
50 & 117 & 118.571428571429 & -1.57142857142857 \tabularnewline
51 & 111 & 118.571428571429 & -7.57142857142857 \tabularnewline
52 & 146 & 118.571428571429 & 27.4285714285714 \tabularnewline
53 & 101 & 118.571428571429 & -17.5714285714286 \tabularnewline
54 & 131 & 118.571428571429 & 12.4285714285714 \tabularnewline
55 & 122 & 118.571428571429 & 3.42857142857143 \tabularnewline
56 & 78 & 118.571428571429 & -40.5714285714286 \tabularnewline
57 & 120 & 118.571428571429 & 1.42857142857143 \tabularnewline
58 & 115 & 118.571428571429 & -3.57142857142857 \tabularnewline
59 & 142 & 118.571428571429 & 23.4285714285714 \tabularnewline
60 & 94 & 118.571428571429 & -24.5714285714286 \tabularnewline
61 & 114 & 118.571428571429 & -4.57142857142857 \tabularnewline
62 & 108 & 118.571428571429 & -10.5714285714286 \tabularnewline
63 & 119 & 118.571428571429 & 0.428571428571431 \tabularnewline
64 & 117 & 118.571428571429 & -1.57142857142857 \tabularnewline
65 & 86 & 118.571428571429 & -32.5714285714286 \tabularnewline
66 & 138 & 118.571428571429 & 19.4285714285714 \tabularnewline
67 & 119 & 118.571428571429 & 0.428571428571431 \tabularnewline
68 & 117 & 118.571428571429 & -1.57142857142857 \tabularnewline
69 & 117 & 118.571428571429 & -1.57142857142857 \tabularnewline
70 & 76 & 118.571428571429 & -42.5714285714286 \tabularnewline
71 & 119 & 118.571428571429 & 0.428571428571431 \tabularnewline
72 & 119 & 118.571428571429 & 0.428571428571431 \tabularnewline
73 & 124 & 118.571428571429 & 5.42857142857143 \tabularnewline
74 & 116 & 118.571428571429 & -2.57142857142857 \tabularnewline
75 & 118 & 118.571428571429 & -0.571428571428569 \tabularnewline
76 & 102 & 118.571428571429 & -16.5714285714286 \tabularnewline
77 & 116 & 118.571428571429 & -2.57142857142857 \tabularnewline
78 & 103 & 118.571428571429 & -15.5714285714286 \tabularnewline
79 & 117 & 118.571428571429 & -1.57142857142857 \tabularnewline
80 & 108 & 118.571428571429 & -10.5714285714286 \tabularnewline
81 & 122 & 118.571428571429 & 3.42857142857143 \tabularnewline
82 & 90 & 118.571428571429 & -28.5714285714286 \tabularnewline
83 & 133 & 118.571428571429 & 14.4285714285714 \tabularnewline
84 & 116 & 118.571428571429 & -2.57142857142857 \tabularnewline
85 & 110 & 94.9473684210526 & 15.0526315789474 \tabularnewline
86 & 90 & 94.9473684210526 & -4.94736842105263 \tabularnewline
87 & 74 & 94.9473684210526 & -20.9473684210526 \tabularnewline
88 & 75 & 94.9473684210526 & -19.9473684210526 \tabularnewline
89 & 107 & 94.9473684210526 & 12.0526315789474 \tabularnewline
90 & 90 & 94.9473684210526 & -4.94736842105263 \tabularnewline
91 & 96 & 94.9473684210526 & 1.05263157894737 \tabularnewline
92 & 115 & 94.9473684210526 & 20.0526315789474 \tabularnewline
93 & 91 & 94.9473684210526 & -3.94736842105263 \tabularnewline
94 & 77 & 94.9473684210526 & -17.9473684210526 \tabularnewline
95 & 108 & 94.9473684210526 & 13.0526315789474 \tabularnewline
96 & 83 & 94.9473684210526 & -11.9473684210526 \tabularnewline
97 & 77 & 94.9473684210526 & -17.9473684210526 \tabularnewline
98 & 99 & 94.9473684210526 & 4.05263157894737 \tabularnewline
99 & 115 & 94.9473684210526 & 20.0526315789474 \tabularnewline
100 & 99 & 94.9473684210526 & 4.05263157894737 \tabularnewline
101 & 106 & 94.9473684210526 & 11.0526315789474 \tabularnewline
102 & 77 & 94.9473684210526 & -17.9473684210526 \tabularnewline
103 & 115 & 94.9473684210526 & 20.0526315789474 \tabularnewline
104 & 67 & 65.7407407407407 & 1.25925925925925 \tabularnewline
105 & 8 & 65.7407407407407 & -57.7407407407407 \tabularnewline
106 & 69 & 65.7407407407407 & 3.25925925925925 \tabularnewline
107 & 88 & 65.7407407407407 & 22.2592592592593 \tabularnewline
108 & 107 & 65.7407407407407 & 41.2592592592593 \tabularnewline
109 & 120 & 65.7407407407407 & 54.2592592592593 \tabularnewline
110 & 3 & 22 & -19 \tabularnewline
111 & 1 & 22 & -21 \tabularnewline
112 & 0 & 22 & -22 \tabularnewline
113 & 111 & 102.515151515152 & 8.48484848484848 \tabularnewline
114 & 69 & 102.515151515152 & -33.5151515151515 \tabularnewline
115 & 116 & 102.515151515152 & 13.4848484848485 \tabularnewline
116 & 103 & 102.515151515152 & 0.484848484848484 \tabularnewline
117 & 139 & 102.515151515152 & 36.4848484848485 \tabularnewline
118 & 135 & 102.515151515152 & 32.4848484848485 \tabularnewline
119 & 113 & 102.515151515152 & 10.4848484848485 \tabularnewline
120 & 99 & 65.7407407407407 & 33.2592592592593 \tabularnewline
121 & 76 & 102.515151515152 & -26.5151515151515 \tabularnewline
122 & 110 & 102.515151515152 & 7.48484848484848 \tabularnewline
123 & 121 & 102.515151515152 & 18.4848484848485 \tabularnewline
124 & 95 & 102.515151515152 & -7.51515151515152 \tabularnewline
125 & 66 & 102.515151515152 & -36.5151515151515 \tabularnewline
126 & 111 & 102.515151515152 & 8.48484848484848 \tabularnewline
127 & 77 & 65.7407407407407 & 11.2592592592593 \tabularnewline
128 & 101 & 102.515151515152 & -1.51515151515152 \tabularnewline
129 & 108 & 102.515151515152 & 5.48484848484848 \tabularnewline
130 & 135 & 102.515151515152 & 32.4848484848485 \tabularnewline
131 & 70 & 102.515151515152 & -32.5151515151515 \tabularnewline
132 & 124 & 102.515151515152 & 21.4848484848485 \tabularnewline
133 & 92 & 102.515151515152 & -10.5151515151515 \tabularnewline
134 & 104 & 102.515151515152 & 1.48484848484848 \tabularnewline
135 & 113 & 102.515151515152 & 10.4848484848485 \tabularnewline
136 & 95 & 102.515151515152 & -7.51515151515152 \tabularnewline
137 & 89 & 102.515151515152 & -13.5151515151515 \tabularnewline
138 & 83 & 102.515151515152 & -19.5151515151515 \tabularnewline
139 & 96 & 102.515151515152 & -6.51515151515152 \tabularnewline
140 & 95 & 102.515151515152 & -7.51515151515152 \tabularnewline
141 & 110 & 102.515151515152 & 7.48484848484848 \tabularnewline
142 & 106 & 102.515151515152 & 3.48484848484848 \tabularnewline
143 & 78 & 65.7407407407407 & 12.2592592592593 \tabularnewline
144 & 115 & 102.515151515152 & 12.4848484848485 \tabularnewline
145 & 74 & 65.7407407407407 & 8.25925925925925 \tabularnewline
146 & 93 & 102.515151515152 & -9.51515151515152 \tabularnewline
147 & 88 & 65.7407407407407 & 22.2592592592593 \tabularnewline
148 & 104 & 102.515151515152 & 1.48484848484848 \tabularnewline
149 & 86 & 102.515151515152 & -16.5151515151515 \tabularnewline
150 & 104 & 65.7407407407407 & 38.2592592592593 \tabularnewline
151 & 99 & 102.515151515152 & -3.51515151515152 \tabularnewline
152 & 101 & 80.8823529411765 & 20.1176470588235 \tabularnewline
153 & 53 & 80.8823529411765 & -27.8823529411765 \tabularnewline
154 & 96 & 80.8823529411765 & 15.1176470588235 \tabularnewline
155 & 58 & 65.7407407407407 & -7.74074074074075 \tabularnewline
156 & 117 & 80.8823529411765 & 36.1176470588235 \tabularnewline
157 & 82 & 80.8823529411765 & 1.11764705882354 \tabularnewline
158 & 57 & 80.8823529411765 & -23.8823529411765 \tabularnewline
159 & 71 & 80.8823529411765 & -9.88235294117646 \tabularnewline
160 & 105 & 80.8823529411765 & 24.1176470588235 \tabularnewline
161 & 60 & 80.8823529411765 & -20.8823529411765 \tabularnewline
162 & 77 & 80.8823529411765 & -3.88235294117646 \tabularnewline
163 & 73 & 65.7407407407407 & 7.25925925925925 \tabularnewline
164 & 78 & 65.7407407407407 & 12.2592592592593 \tabularnewline
165 & 81 & 80.8823529411765 & 0.117647058823536 \tabularnewline
166 & 101 & 65.7407407407407 & 35.2592592592593 \tabularnewline
167 & 118 & 80.8823529411765 & 37.1176470588235 \tabularnewline
168 & 59 & 65.7407407407407 & -6.74074074074075 \tabularnewline
169 & 101 & 65.7407407407407 & 35.2592592592593 \tabularnewline
170 & 22 & 80.8823529411765 & -58.8823529411765 \tabularnewline
171 & 77 & 65.7407407407407 & 11.2592592592593 \tabularnewline
172 & 100 & 65.7407407407407 & 34.2592592592593 \tabularnewline
173 & 39 & 65.7407407407407 & -26.7407407407407 \tabularnewline
174 & 42 & 65.7407407407407 & -23.7407407407407 \tabularnewline
175 & 80 & 80.8823529411765 & -0.882352941176464 \tabularnewline
176 & 48 & 65.7407407407407 & -17.7407407407407 \tabularnewline
177 & 131 & 80.8823529411765 & 50.1176470588235 \tabularnewline
178 & 46 & 80.8823529411765 & -34.8823529411765 \tabularnewline
179 & 89 & 65.7407407407407 & 23.2592592592593 \tabularnewline
180 & 51 & 65.7407407407407 & -14.7407407407407 \tabularnewline
181 & 108 & 80.8823529411765 & 27.1176470588235 \tabularnewline
182 & 86 & 65.7407407407407 & 20.2592592592593 \tabularnewline
183 & 105 & 80.8823529411765 & 24.1176470588235 \tabularnewline
184 & 85 & 80.8823529411765 & 4.11764705882354 \tabularnewline
185 & 103 & 65.7407407407407 & 37.2592592592593 \tabularnewline
186 & 83 & 65.7407407407407 & 17.2592592592593 \tabularnewline
187 & 77 & 65.7407407407407 & 11.2592592592593 \tabularnewline
188 & 26 & 65.7407407407407 & -39.7407407407407 \tabularnewline
189 & 73 & 65.7407407407407 & 7.25925925925925 \tabularnewline
190 & 42 & 65.7407407407407 & -23.7407407407407 \tabularnewline
191 & 71 & 65.7407407407407 & 5.25925925925925 \tabularnewline
192 & 105 & 65.7407407407407 & 39.2592592592593 \tabularnewline
193 & 73 & 65.7407407407407 & 7.25925925925925 \tabularnewline
194 & 98 & 80.8823529411765 & 17.1176470588235 \tabularnewline
195 & 108 & 80.8823529411765 & 27.1176470588235 \tabularnewline
196 & 57 & 80.8823529411765 & -23.8823529411765 \tabularnewline
197 & 37 & 65.7407407407407 & -28.7407407407407 \tabularnewline
198 & 70 & 65.7407407407407 & 4.25925925925925 \tabularnewline
199 & 73 & 65.7407407407407 & 7.25925925925925 \tabularnewline
200 & 47 & 65.7407407407407 & -18.7407407407407 \tabularnewline
201 & 73 & 65.7407407407407 & 7.25925925925925 \tabularnewline
202 & 91 & 80.8823529411765 & 10.1176470588235 \tabularnewline
203 & 110 & 80.8823529411765 & 29.1176470588235 \tabularnewline
204 & 78 & 80.8823529411765 & -2.88235294117646 \tabularnewline
205 & 92 & 80.8823529411765 & 11.1176470588235 \tabularnewline
206 & 52 & 80.8823529411765 & -28.8823529411765 \tabularnewline
207 & 88 & 65.7407407407407 & 22.2592592592593 \tabularnewline
208 & 100 & 65.7407407407407 & 34.2592592592593 \tabularnewline
209 & 33 & 65.7407407407407 & -32.7407407407407 \tabularnewline
210 & 42 & 80.8823529411765 & -38.8823529411765 \tabularnewline
211 & 81 & 80.8823529411765 & 0.117647058823536 \tabularnewline
212 & 67 & 65.7407407407407 & 1.25925925925925 \tabularnewline
213 & 8 & 65.7407407407407 & -57.7407407407407 \tabularnewline
214 & 46 & 65.7407407407407 & -19.7407407407407 \tabularnewline
215 & 83 & 80.8823529411765 & 2.11764705882354 \tabularnewline
216 & 87 & 65.7407407407407 & 21.2592592592593 \tabularnewline
217 & 82 & 80.8823529411765 & 1.11764705882354 \tabularnewline
218 & 63 & 65.7407407407407 & -2.74074074074075 \tabularnewline
219 & 27 & 65.7407407407407 & -38.7407407407407 \tabularnewline
220 & 14 & 65.7407407407407 & -51.7407407407407 \tabularnewline
221 & 83 & 65.7407407407407 & 17.2592592592593 \tabularnewline
222 & 168 & 65.7407407407407 & 102.259259259259 \tabularnewline
223 & 67 & 65.7407407407407 & 1.25925925925925 \tabularnewline
224 & 21 & 65.7407407407407 & -44.7407407407407 \tabularnewline
225 & 55 & 80.8823529411765 & -25.8823529411765 \tabularnewline
226 & 54 & 80.8823529411765 & -26.8823529411765 \tabularnewline
227 & 118 & 65.7407407407407 & 52.2592592592593 \tabularnewline
228 & 69 & 65.7407407407407 & 3.25925925925925 \tabularnewline
229 & 77 & 65.7407407407407 & 11.2592592592593 \tabularnewline
230 & 72 & 80.8823529411765 & -8.88235294117646 \tabularnewline
231 & 53 & 65.7407407407407 & -12.7407407407407 \tabularnewline
232 & 40 & 65.7407407407407 & -25.7407407407407 \tabularnewline
233 & 102 & 65.7407407407407 & 36.2592592592593 \tabularnewline
234 & 25 & 65.7407407407407 & -40.7407407407407 \tabularnewline
235 & 31 & 65.7407407407407 & -34.7407407407407 \tabularnewline
236 & 77 & 65.7407407407407 & 11.2592592592593 \tabularnewline
237 & 38 & 65.7407407407407 & -27.7407407407407 \tabularnewline
238 & 23 & 65.7407407407407 & -42.7407407407407 \tabularnewline
239 & 91 & 65.7407407407407 & 25.2592592592593 \tabularnewline
240 & 58 & 65.7407407407407 & -7.74074074074075 \tabularnewline
241 & 42 & 65.7407407407407 & -23.7407407407407 \tabularnewline
242 & 44 & 65.7407407407407 & -21.7407407407407 \tabularnewline
243 & 58 & 65.7407407407407 & -7.74074074074075 \tabularnewline
244 & 35 & 65.7407407407407 & -30.7407407407407 \tabularnewline
245 & 88 & 65.7407407407407 & 22.2592592592593 \tabularnewline
246 & 25 & 65.7407407407407 & -40.7407407407407 \tabularnewline
247 & 39 & 65.7407407407407 & -26.7407407407407 \tabularnewline
248 & 48 & 65.7407407407407 & -17.7407407407407 \tabularnewline
249 & 64 & 65.7407407407407 & -1.74074074074075 \tabularnewline
250 & 65 & 65.7407407407407 & -0.740740740740748 \tabularnewline
251 & 95 & 65.7407407407407 & 29.2592592592593 \tabularnewline
252 & 29 & 65.7407407407407 & -36.7407407407407 \tabularnewline
253 & 2 & 65.7407407407407 & -63.7407407407407 \tabularnewline
254 & 83 & 65.7407407407407 & 17.2592592592593 \tabularnewline
255 & 11 & 22 & -11 \tabularnewline
256 & 16 & 22 & -6 \tabularnewline
257 & 9 & 22 & -13 \tabularnewline
258 & 46 & 22 & 24 \tabularnewline
259 & 41 & 22 & 19 \tabularnewline
260 & 14 & 22 & -8 \tabularnewline
261 & 63 & 22 & 41 \tabularnewline
262 & 9 & 22 & -13 \tabularnewline
263 & 0 & 22 & -22 \tabularnewline
264 & 58 & 22 & 36 \tabularnewline
265 & 18 & 22 & -4 \tabularnewline
266 & 42 & 22 & 20 \tabularnewline
267 & 26 & 22 & 4 \tabularnewline
268 & 38 & 22 & 16 \tabularnewline
269 & 1 & 22 & -21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200966&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]122[/C][C]118.571428571429[/C][C]3.42857142857143[/C][/ROW]
[ROW][C]2[/C][C]114[/C][C]118.571428571429[/C][C]-4.57142857142857[/C][/ROW]
[ROW][C]3[/C][C]140[/C][C]118.571428571429[/C][C]21.4285714285714[/C][/ROW]
[ROW][C]4[/C][C]143[/C][C]118.571428571429[/C][C]24.4285714285714[/C][/ROW]
[ROW][C]5[/C][C]122[/C][C]118.571428571429[/C][C]3.42857142857143[/C][/ROW]
[ROW][C]6[/C][C]127[/C][C]118.571428571429[/C][C]8.42857142857143[/C][/ROW]
[ROW][C]7[/C][C]113[/C][C]118.571428571429[/C][C]-5.57142857142857[/C][/ROW]
[ROW][C]8[/C][C]118[/C][C]118.571428571429[/C][C]-0.571428571428569[/C][/ROW]
[ROW][C]9[/C][C]161[/C][C]118.571428571429[/C][C]42.4285714285714[/C][/ROW]
[ROW][C]10[/C][C]134[/C][C]118.571428571429[/C][C]15.4285714285714[/C][/ROW]
[ROW][C]11[/C][C]96[/C][C]118.571428571429[/C][C]-22.5714285714286[/C][/ROW]
[ROW][C]12[/C][C]104[/C][C]118.571428571429[/C][C]-14.5714285714286[/C][/ROW]
[ROW][C]13[/C][C]135[/C][C]118.571428571429[/C][C]16.4285714285714[/C][/ROW]
[ROW][C]14[/C][C]110[/C][C]118.571428571429[/C][C]-8.57142857142857[/C][/ROW]
[ROW][C]15[/C][C]128[/C][C]118.571428571429[/C][C]9.42857142857143[/C][/ROW]
[ROW][C]16[/C][C]142[/C][C]118.571428571429[/C][C]23.4285714285714[/C][/ROW]
[ROW][C]17[/C][C]117[/C][C]118.571428571429[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]18[/C][C]94[/C][C]118.571428571429[/C][C]-24.5714285714286[/C][/ROW]
[ROW][C]19[/C][C]135[/C][C]118.571428571429[/C][C]16.4285714285714[/C][/ROW]
[ROW][C]20[/C][C]121[/C][C]118.571428571429[/C][C]2.42857142857143[/C][/ROW]
[ROW][C]21[/C][C]103[/C][C]118.571428571429[/C][C]-15.5714285714286[/C][/ROW]
[ROW][C]22[/C][C]118[/C][C]118.571428571429[/C][C]-0.571428571428569[/C][/ROW]
[ROW][C]23[/C][C]127[/C][C]118.571428571429[/C][C]8.42857142857143[/C][/ROW]
[ROW][C]24[/C][C]116[/C][C]118.571428571429[/C][C]-2.57142857142857[/C][/ROW]
[ROW][C]25[/C][C]129[/C][C]118.571428571429[/C][C]10.4285714285714[/C][/ROW]
[ROW][C]26[/C][C]115[/C][C]118.571428571429[/C][C]-3.57142857142857[/C][/ROW]
[ROW][C]27[/C][C]135[/C][C]118.571428571429[/C][C]16.4285714285714[/C][/ROW]
[ROW][C]28[/C][C]133[/C][C]118.571428571429[/C][C]14.4285714285714[/C][/ROW]
[ROW][C]29[/C][C]113[/C][C]118.571428571429[/C][C]-5.57142857142857[/C][/ROW]
[ROW][C]30[/C][C]111[/C][C]118.571428571429[/C][C]-7.57142857142857[/C][/ROW]
[ROW][C]31[/C][C]92[/C][C]118.571428571429[/C][C]-26.5714285714286[/C][/ROW]
[ROW][C]32[/C][C]118[/C][C]118.571428571429[/C][C]-0.571428571428569[/C][/ROW]
[ROW][C]33[/C][C]134[/C][C]118.571428571429[/C][C]15.4285714285714[/C][/ROW]
[ROW][C]34[/C][C]106[/C][C]118.571428571429[/C][C]-12.5714285714286[/C][/ROW]
[ROW][C]35[/C][C]137[/C][C]118.571428571429[/C][C]18.4285714285714[/C][/ROW]
[ROW][C]36[/C][C]100[/C][C]118.571428571429[/C][C]-18.5714285714286[/C][/ROW]
[ROW][C]37[/C][C]102[/C][C]118.571428571429[/C][C]-16.5714285714286[/C][/ROW]
[ROW][C]38[/C][C]134[/C][C]118.571428571429[/C][C]15.4285714285714[/C][/ROW]
[ROW][C]39[/C][C]130[/C][C]118.571428571429[/C][C]11.4285714285714[/C][/ROW]
[ROW][C]40[/C][C]144[/C][C]118.571428571429[/C][C]25.4285714285714[/C][/ROW]
[ROW][C]41[/C][C]120[/C][C]118.571428571429[/C][C]1.42857142857143[/C][/ROW]
[ROW][C]42[/C][C]91[/C][C]118.571428571429[/C][C]-27.5714285714286[/C][/ROW]
[ROW][C]43[/C][C]100[/C][C]118.571428571429[/C][C]-18.5714285714286[/C][/ROW]
[ROW][C]44[/C][C]134[/C][C]118.571428571429[/C][C]15.4285714285714[/C][/ROW]
[ROW][C]45[/C][C]161[/C][C]118.571428571429[/C][C]42.4285714285714[/C][/ROW]
[ROW][C]46[/C][C]128[/C][C]118.571428571429[/C][C]9.42857142857143[/C][/ROW]
[ROW][C]47[/C][C]124[/C][C]118.571428571429[/C][C]5.42857142857143[/C][/ROW]
[ROW][C]48[/C][C]115[/C][C]118.571428571429[/C][C]-3.57142857142857[/C][/ROW]
[ROW][C]49[/C][C]123[/C][C]118.571428571429[/C][C]4.42857142857143[/C][/ROW]
[ROW][C]50[/C][C]117[/C][C]118.571428571429[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]51[/C][C]111[/C][C]118.571428571429[/C][C]-7.57142857142857[/C][/ROW]
[ROW][C]52[/C][C]146[/C][C]118.571428571429[/C][C]27.4285714285714[/C][/ROW]
[ROW][C]53[/C][C]101[/C][C]118.571428571429[/C][C]-17.5714285714286[/C][/ROW]
[ROW][C]54[/C][C]131[/C][C]118.571428571429[/C][C]12.4285714285714[/C][/ROW]
[ROW][C]55[/C][C]122[/C][C]118.571428571429[/C][C]3.42857142857143[/C][/ROW]
[ROW][C]56[/C][C]78[/C][C]118.571428571429[/C][C]-40.5714285714286[/C][/ROW]
[ROW][C]57[/C][C]120[/C][C]118.571428571429[/C][C]1.42857142857143[/C][/ROW]
[ROW][C]58[/C][C]115[/C][C]118.571428571429[/C][C]-3.57142857142857[/C][/ROW]
[ROW][C]59[/C][C]142[/C][C]118.571428571429[/C][C]23.4285714285714[/C][/ROW]
[ROW][C]60[/C][C]94[/C][C]118.571428571429[/C][C]-24.5714285714286[/C][/ROW]
[ROW][C]61[/C][C]114[/C][C]118.571428571429[/C][C]-4.57142857142857[/C][/ROW]
[ROW][C]62[/C][C]108[/C][C]118.571428571429[/C][C]-10.5714285714286[/C][/ROW]
[ROW][C]63[/C][C]119[/C][C]118.571428571429[/C][C]0.428571428571431[/C][/ROW]
[ROW][C]64[/C][C]117[/C][C]118.571428571429[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]65[/C][C]86[/C][C]118.571428571429[/C][C]-32.5714285714286[/C][/ROW]
[ROW][C]66[/C][C]138[/C][C]118.571428571429[/C][C]19.4285714285714[/C][/ROW]
[ROW][C]67[/C][C]119[/C][C]118.571428571429[/C][C]0.428571428571431[/C][/ROW]
[ROW][C]68[/C][C]117[/C][C]118.571428571429[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]69[/C][C]117[/C][C]118.571428571429[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]70[/C][C]76[/C][C]118.571428571429[/C][C]-42.5714285714286[/C][/ROW]
[ROW][C]71[/C][C]119[/C][C]118.571428571429[/C][C]0.428571428571431[/C][/ROW]
[ROW][C]72[/C][C]119[/C][C]118.571428571429[/C][C]0.428571428571431[/C][/ROW]
[ROW][C]73[/C][C]124[/C][C]118.571428571429[/C][C]5.42857142857143[/C][/ROW]
[ROW][C]74[/C][C]116[/C][C]118.571428571429[/C][C]-2.57142857142857[/C][/ROW]
[ROW][C]75[/C][C]118[/C][C]118.571428571429[/C][C]-0.571428571428569[/C][/ROW]
[ROW][C]76[/C][C]102[/C][C]118.571428571429[/C][C]-16.5714285714286[/C][/ROW]
[ROW][C]77[/C][C]116[/C][C]118.571428571429[/C][C]-2.57142857142857[/C][/ROW]
[ROW][C]78[/C][C]103[/C][C]118.571428571429[/C][C]-15.5714285714286[/C][/ROW]
[ROW][C]79[/C][C]117[/C][C]118.571428571429[/C][C]-1.57142857142857[/C][/ROW]
[ROW][C]80[/C][C]108[/C][C]118.571428571429[/C][C]-10.5714285714286[/C][/ROW]
[ROW][C]81[/C][C]122[/C][C]118.571428571429[/C][C]3.42857142857143[/C][/ROW]
[ROW][C]82[/C][C]90[/C][C]118.571428571429[/C][C]-28.5714285714286[/C][/ROW]
[ROW][C]83[/C][C]133[/C][C]118.571428571429[/C][C]14.4285714285714[/C][/ROW]
[ROW][C]84[/C][C]116[/C][C]118.571428571429[/C][C]-2.57142857142857[/C][/ROW]
[ROW][C]85[/C][C]110[/C][C]94.9473684210526[/C][C]15.0526315789474[/C][/ROW]
[ROW][C]86[/C][C]90[/C][C]94.9473684210526[/C][C]-4.94736842105263[/C][/ROW]
[ROW][C]87[/C][C]74[/C][C]94.9473684210526[/C][C]-20.9473684210526[/C][/ROW]
[ROW][C]88[/C][C]75[/C][C]94.9473684210526[/C][C]-19.9473684210526[/C][/ROW]
[ROW][C]89[/C][C]107[/C][C]94.9473684210526[/C][C]12.0526315789474[/C][/ROW]
[ROW][C]90[/C][C]90[/C][C]94.9473684210526[/C][C]-4.94736842105263[/C][/ROW]
[ROW][C]91[/C][C]96[/C][C]94.9473684210526[/C][C]1.05263157894737[/C][/ROW]
[ROW][C]92[/C][C]115[/C][C]94.9473684210526[/C][C]20.0526315789474[/C][/ROW]
[ROW][C]93[/C][C]91[/C][C]94.9473684210526[/C][C]-3.94736842105263[/C][/ROW]
[ROW][C]94[/C][C]77[/C][C]94.9473684210526[/C][C]-17.9473684210526[/C][/ROW]
[ROW][C]95[/C][C]108[/C][C]94.9473684210526[/C][C]13.0526315789474[/C][/ROW]
[ROW][C]96[/C][C]83[/C][C]94.9473684210526[/C][C]-11.9473684210526[/C][/ROW]
[ROW][C]97[/C][C]77[/C][C]94.9473684210526[/C][C]-17.9473684210526[/C][/ROW]
[ROW][C]98[/C][C]99[/C][C]94.9473684210526[/C][C]4.05263157894737[/C][/ROW]
[ROW][C]99[/C][C]115[/C][C]94.9473684210526[/C][C]20.0526315789474[/C][/ROW]
[ROW][C]100[/C][C]99[/C][C]94.9473684210526[/C][C]4.05263157894737[/C][/ROW]
[ROW][C]101[/C][C]106[/C][C]94.9473684210526[/C][C]11.0526315789474[/C][/ROW]
[ROW][C]102[/C][C]77[/C][C]94.9473684210526[/C][C]-17.9473684210526[/C][/ROW]
[ROW][C]103[/C][C]115[/C][C]94.9473684210526[/C][C]20.0526315789474[/C][/ROW]
[ROW][C]104[/C][C]67[/C][C]65.7407407407407[/C][C]1.25925925925925[/C][/ROW]
[ROW][C]105[/C][C]8[/C][C]65.7407407407407[/C][C]-57.7407407407407[/C][/ROW]
[ROW][C]106[/C][C]69[/C][C]65.7407407407407[/C][C]3.25925925925925[/C][/ROW]
[ROW][C]107[/C][C]88[/C][C]65.7407407407407[/C][C]22.2592592592593[/C][/ROW]
[ROW][C]108[/C][C]107[/C][C]65.7407407407407[/C][C]41.2592592592593[/C][/ROW]
[ROW][C]109[/C][C]120[/C][C]65.7407407407407[/C][C]54.2592592592593[/C][/ROW]
[ROW][C]110[/C][C]3[/C][C]22[/C][C]-19[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]22[/C][C]-21[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]22[/C][C]-22[/C][/ROW]
[ROW][C]113[/C][C]111[/C][C]102.515151515152[/C][C]8.48484848484848[/C][/ROW]
[ROW][C]114[/C][C]69[/C][C]102.515151515152[/C][C]-33.5151515151515[/C][/ROW]
[ROW][C]115[/C][C]116[/C][C]102.515151515152[/C][C]13.4848484848485[/C][/ROW]
[ROW][C]116[/C][C]103[/C][C]102.515151515152[/C][C]0.484848484848484[/C][/ROW]
[ROW][C]117[/C][C]139[/C][C]102.515151515152[/C][C]36.4848484848485[/C][/ROW]
[ROW][C]118[/C][C]135[/C][C]102.515151515152[/C][C]32.4848484848485[/C][/ROW]
[ROW][C]119[/C][C]113[/C][C]102.515151515152[/C][C]10.4848484848485[/C][/ROW]
[ROW][C]120[/C][C]99[/C][C]65.7407407407407[/C][C]33.2592592592593[/C][/ROW]
[ROW][C]121[/C][C]76[/C][C]102.515151515152[/C][C]-26.5151515151515[/C][/ROW]
[ROW][C]122[/C][C]110[/C][C]102.515151515152[/C][C]7.48484848484848[/C][/ROW]
[ROW][C]123[/C][C]121[/C][C]102.515151515152[/C][C]18.4848484848485[/C][/ROW]
[ROW][C]124[/C][C]95[/C][C]102.515151515152[/C][C]-7.51515151515152[/C][/ROW]
[ROW][C]125[/C][C]66[/C][C]102.515151515152[/C][C]-36.5151515151515[/C][/ROW]
[ROW][C]126[/C][C]111[/C][C]102.515151515152[/C][C]8.48484848484848[/C][/ROW]
[ROW][C]127[/C][C]77[/C][C]65.7407407407407[/C][C]11.2592592592593[/C][/ROW]
[ROW][C]128[/C][C]101[/C][C]102.515151515152[/C][C]-1.51515151515152[/C][/ROW]
[ROW][C]129[/C][C]108[/C][C]102.515151515152[/C][C]5.48484848484848[/C][/ROW]
[ROW][C]130[/C][C]135[/C][C]102.515151515152[/C][C]32.4848484848485[/C][/ROW]
[ROW][C]131[/C][C]70[/C][C]102.515151515152[/C][C]-32.5151515151515[/C][/ROW]
[ROW][C]132[/C][C]124[/C][C]102.515151515152[/C][C]21.4848484848485[/C][/ROW]
[ROW][C]133[/C][C]92[/C][C]102.515151515152[/C][C]-10.5151515151515[/C][/ROW]
[ROW][C]134[/C][C]104[/C][C]102.515151515152[/C][C]1.48484848484848[/C][/ROW]
[ROW][C]135[/C][C]113[/C][C]102.515151515152[/C][C]10.4848484848485[/C][/ROW]
[ROW][C]136[/C][C]95[/C][C]102.515151515152[/C][C]-7.51515151515152[/C][/ROW]
[ROW][C]137[/C][C]89[/C][C]102.515151515152[/C][C]-13.5151515151515[/C][/ROW]
[ROW][C]138[/C][C]83[/C][C]102.515151515152[/C][C]-19.5151515151515[/C][/ROW]
[ROW][C]139[/C][C]96[/C][C]102.515151515152[/C][C]-6.51515151515152[/C][/ROW]
[ROW][C]140[/C][C]95[/C][C]102.515151515152[/C][C]-7.51515151515152[/C][/ROW]
[ROW][C]141[/C][C]110[/C][C]102.515151515152[/C][C]7.48484848484848[/C][/ROW]
[ROW][C]142[/C][C]106[/C][C]102.515151515152[/C][C]3.48484848484848[/C][/ROW]
[ROW][C]143[/C][C]78[/C][C]65.7407407407407[/C][C]12.2592592592593[/C][/ROW]
[ROW][C]144[/C][C]115[/C][C]102.515151515152[/C][C]12.4848484848485[/C][/ROW]
[ROW][C]145[/C][C]74[/C][C]65.7407407407407[/C][C]8.25925925925925[/C][/ROW]
[ROW][C]146[/C][C]93[/C][C]102.515151515152[/C][C]-9.51515151515152[/C][/ROW]
[ROW][C]147[/C][C]88[/C][C]65.7407407407407[/C][C]22.2592592592593[/C][/ROW]
[ROW][C]148[/C][C]104[/C][C]102.515151515152[/C][C]1.48484848484848[/C][/ROW]
[ROW][C]149[/C][C]86[/C][C]102.515151515152[/C][C]-16.5151515151515[/C][/ROW]
[ROW][C]150[/C][C]104[/C][C]65.7407407407407[/C][C]38.2592592592593[/C][/ROW]
[ROW][C]151[/C][C]99[/C][C]102.515151515152[/C][C]-3.51515151515152[/C][/ROW]
[ROW][C]152[/C][C]101[/C][C]80.8823529411765[/C][C]20.1176470588235[/C][/ROW]
[ROW][C]153[/C][C]53[/C][C]80.8823529411765[/C][C]-27.8823529411765[/C][/ROW]
[ROW][C]154[/C][C]96[/C][C]80.8823529411765[/C][C]15.1176470588235[/C][/ROW]
[ROW][C]155[/C][C]58[/C][C]65.7407407407407[/C][C]-7.74074074074075[/C][/ROW]
[ROW][C]156[/C][C]117[/C][C]80.8823529411765[/C][C]36.1176470588235[/C][/ROW]
[ROW][C]157[/C][C]82[/C][C]80.8823529411765[/C][C]1.11764705882354[/C][/ROW]
[ROW][C]158[/C][C]57[/C][C]80.8823529411765[/C][C]-23.8823529411765[/C][/ROW]
[ROW][C]159[/C][C]71[/C][C]80.8823529411765[/C][C]-9.88235294117646[/C][/ROW]
[ROW][C]160[/C][C]105[/C][C]80.8823529411765[/C][C]24.1176470588235[/C][/ROW]
[ROW][C]161[/C][C]60[/C][C]80.8823529411765[/C][C]-20.8823529411765[/C][/ROW]
[ROW][C]162[/C][C]77[/C][C]80.8823529411765[/C][C]-3.88235294117646[/C][/ROW]
[ROW][C]163[/C][C]73[/C][C]65.7407407407407[/C][C]7.25925925925925[/C][/ROW]
[ROW][C]164[/C][C]78[/C][C]65.7407407407407[/C][C]12.2592592592593[/C][/ROW]
[ROW][C]165[/C][C]81[/C][C]80.8823529411765[/C][C]0.117647058823536[/C][/ROW]
[ROW][C]166[/C][C]101[/C][C]65.7407407407407[/C][C]35.2592592592593[/C][/ROW]
[ROW][C]167[/C][C]118[/C][C]80.8823529411765[/C][C]37.1176470588235[/C][/ROW]
[ROW][C]168[/C][C]59[/C][C]65.7407407407407[/C][C]-6.74074074074075[/C][/ROW]
[ROW][C]169[/C][C]101[/C][C]65.7407407407407[/C][C]35.2592592592593[/C][/ROW]
[ROW][C]170[/C][C]22[/C][C]80.8823529411765[/C][C]-58.8823529411765[/C][/ROW]
[ROW][C]171[/C][C]77[/C][C]65.7407407407407[/C][C]11.2592592592593[/C][/ROW]
[ROW][C]172[/C][C]100[/C][C]65.7407407407407[/C][C]34.2592592592593[/C][/ROW]
[ROW][C]173[/C][C]39[/C][C]65.7407407407407[/C][C]-26.7407407407407[/C][/ROW]
[ROW][C]174[/C][C]42[/C][C]65.7407407407407[/C][C]-23.7407407407407[/C][/ROW]
[ROW][C]175[/C][C]80[/C][C]80.8823529411765[/C][C]-0.882352941176464[/C][/ROW]
[ROW][C]176[/C][C]48[/C][C]65.7407407407407[/C][C]-17.7407407407407[/C][/ROW]
[ROW][C]177[/C][C]131[/C][C]80.8823529411765[/C][C]50.1176470588235[/C][/ROW]
[ROW][C]178[/C][C]46[/C][C]80.8823529411765[/C][C]-34.8823529411765[/C][/ROW]
[ROW][C]179[/C][C]89[/C][C]65.7407407407407[/C][C]23.2592592592593[/C][/ROW]
[ROW][C]180[/C][C]51[/C][C]65.7407407407407[/C][C]-14.7407407407407[/C][/ROW]
[ROW][C]181[/C][C]108[/C][C]80.8823529411765[/C][C]27.1176470588235[/C][/ROW]
[ROW][C]182[/C][C]86[/C][C]65.7407407407407[/C][C]20.2592592592593[/C][/ROW]
[ROW][C]183[/C][C]105[/C][C]80.8823529411765[/C][C]24.1176470588235[/C][/ROW]
[ROW][C]184[/C][C]85[/C][C]80.8823529411765[/C][C]4.11764705882354[/C][/ROW]
[ROW][C]185[/C][C]103[/C][C]65.7407407407407[/C][C]37.2592592592593[/C][/ROW]
[ROW][C]186[/C][C]83[/C][C]65.7407407407407[/C][C]17.2592592592593[/C][/ROW]
[ROW][C]187[/C][C]77[/C][C]65.7407407407407[/C][C]11.2592592592593[/C][/ROW]
[ROW][C]188[/C][C]26[/C][C]65.7407407407407[/C][C]-39.7407407407407[/C][/ROW]
[ROW][C]189[/C][C]73[/C][C]65.7407407407407[/C][C]7.25925925925925[/C][/ROW]
[ROW][C]190[/C][C]42[/C][C]65.7407407407407[/C][C]-23.7407407407407[/C][/ROW]
[ROW][C]191[/C][C]71[/C][C]65.7407407407407[/C][C]5.25925925925925[/C][/ROW]
[ROW][C]192[/C][C]105[/C][C]65.7407407407407[/C][C]39.2592592592593[/C][/ROW]
[ROW][C]193[/C][C]73[/C][C]65.7407407407407[/C][C]7.25925925925925[/C][/ROW]
[ROW][C]194[/C][C]98[/C][C]80.8823529411765[/C][C]17.1176470588235[/C][/ROW]
[ROW][C]195[/C][C]108[/C][C]80.8823529411765[/C][C]27.1176470588235[/C][/ROW]
[ROW][C]196[/C][C]57[/C][C]80.8823529411765[/C][C]-23.8823529411765[/C][/ROW]
[ROW][C]197[/C][C]37[/C][C]65.7407407407407[/C][C]-28.7407407407407[/C][/ROW]
[ROW][C]198[/C][C]70[/C][C]65.7407407407407[/C][C]4.25925925925925[/C][/ROW]
[ROW][C]199[/C][C]73[/C][C]65.7407407407407[/C][C]7.25925925925925[/C][/ROW]
[ROW][C]200[/C][C]47[/C][C]65.7407407407407[/C][C]-18.7407407407407[/C][/ROW]
[ROW][C]201[/C][C]73[/C][C]65.7407407407407[/C][C]7.25925925925925[/C][/ROW]
[ROW][C]202[/C][C]91[/C][C]80.8823529411765[/C][C]10.1176470588235[/C][/ROW]
[ROW][C]203[/C][C]110[/C][C]80.8823529411765[/C][C]29.1176470588235[/C][/ROW]
[ROW][C]204[/C][C]78[/C][C]80.8823529411765[/C][C]-2.88235294117646[/C][/ROW]
[ROW][C]205[/C][C]92[/C][C]80.8823529411765[/C][C]11.1176470588235[/C][/ROW]
[ROW][C]206[/C][C]52[/C][C]80.8823529411765[/C][C]-28.8823529411765[/C][/ROW]
[ROW][C]207[/C][C]88[/C][C]65.7407407407407[/C][C]22.2592592592593[/C][/ROW]
[ROW][C]208[/C][C]100[/C][C]65.7407407407407[/C][C]34.2592592592593[/C][/ROW]
[ROW][C]209[/C][C]33[/C][C]65.7407407407407[/C][C]-32.7407407407407[/C][/ROW]
[ROW][C]210[/C][C]42[/C][C]80.8823529411765[/C][C]-38.8823529411765[/C][/ROW]
[ROW][C]211[/C][C]81[/C][C]80.8823529411765[/C][C]0.117647058823536[/C][/ROW]
[ROW][C]212[/C][C]67[/C][C]65.7407407407407[/C][C]1.25925925925925[/C][/ROW]
[ROW][C]213[/C][C]8[/C][C]65.7407407407407[/C][C]-57.7407407407407[/C][/ROW]
[ROW][C]214[/C][C]46[/C][C]65.7407407407407[/C][C]-19.7407407407407[/C][/ROW]
[ROW][C]215[/C][C]83[/C][C]80.8823529411765[/C][C]2.11764705882354[/C][/ROW]
[ROW][C]216[/C][C]87[/C][C]65.7407407407407[/C][C]21.2592592592593[/C][/ROW]
[ROW][C]217[/C][C]82[/C][C]80.8823529411765[/C][C]1.11764705882354[/C][/ROW]
[ROW][C]218[/C][C]63[/C][C]65.7407407407407[/C][C]-2.74074074074075[/C][/ROW]
[ROW][C]219[/C][C]27[/C][C]65.7407407407407[/C][C]-38.7407407407407[/C][/ROW]
[ROW][C]220[/C][C]14[/C][C]65.7407407407407[/C][C]-51.7407407407407[/C][/ROW]
[ROW][C]221[/C][C]83[/C][C]65.7407407407407[/C][C]17.2592592592593[/C][/ROW]
[ROW][C]222[/C][C]168[/C][C]65.7407407407407[/C][C]102.259259259259[/C][/ROW]
[ROW][C]223[/C][C]67[/C][C]65.7407407407407[/C][C]1.25925925925925[/C][/ROW]
[ROW][C]224[/C][C]21[/C][C]65.7407407407407[/C][C]-44.7407407407407[/C][/ROW]
[ROW][C]225[/C][C]55[/C][C]80.8823529411765[/C][C]-25.8823529411765[/C][/ROW]
[ROW][C]226[/C][C]54[/C][C]80.8823529411765[/C][C]-26.8823529411765[/C][/ROW]
[ROW][C]227[/C][C]118[/C][C]65.7407407407407[/C][C]52.2592592592593[/C][/ROW]
[ROW][C]228[/C][C]69[/C][C]65.7407407407407[/C][C]3.25925925925925[/C][/ROW]
[ROW][C]229[/C][C]77[/C][C]65.7407407407407[/C][C]11.2592592592593[/C][/ROW]
[ROW][C]230[/C][C]72[/C][C]80.8823529411765[/C][C]-8.88235294117646[/C][/ROW]
[ROW][C]231[/C][C]53[/C][C]65.7407407407407[/C][C]-12.7407407407407[/C][/ROW]
[ROW][C]232[/C][C]40[/C][C]65.7407407407407[/C][C]-25.7407407407407[/C][/ROW]
[ROW][C]233[/C][C]102[/C][C]65.7407407407407[/C][C]36.2592592592593[/C][/ROW]
[ROW][C]234[/C][C]25[/C][C]65.7407407407407[/C][C]-40.7407407407407[/C][/ROW]
[ROW][C]235[/C][C]31[/C][C]65.7407407407407[/C][C]-34.7407407407407[/C][/ROW]
[ROW][C]236[/C][C]77[/C][C]65.7407407407407[/C][C]11.2592592592593[/C][/ROW]
[ROW][C]237[/C][C]38[/C][C]65.7407407407407[/C][C]-27.7407407407407[/C][/ROW]
[ROW][C]238[/C][C]23[/C][C]65.7407407407407[/C][C]-42.7407407407407[/C][/ROW]
[ROW][C]239[/C][C]91[/C][C]65.7407407407407[/C][C]25.2592592592593[/C][/ROW]
[ROW][C]240[/C][C]58[/C][C]65.7407407407407[/C][C]-7.74074074074075[/C][/ROW]
[ROW][C]241[/C][C]42[/C][C]65.7407407407407[/C][C]-23.7407407407407[/C][/ROW]
[ROW][C]242[/C][C]44[/C][C]65.7407407407407[/C][C]-21.7407407407407[/C][/ROW]
[ROW][C]243[/C][C]58[/C][C]65.7407407407407[/C][C]-7.74074074074075[/C][/ROW]
[ROW][C]244[/C][C]35[/C][C]65.7407407407407[/C][C]-30.7407407407407[/C][/ROW]
[ROW][C]245[/C][C]88[/C][C]65.7407407407407[/C][C]22.2592592592593[/C][/ROW]
[ROW][C]246[/C][C]25[/C][C]65.7407407407407[/C][C]-40.7407407407407[/C][/ROW]
[ROW][C]247[/C][C]39[/C][C]65.7407407407407[/C][C]-26.7407407407407[/C][/ROW]
[ROW][C]248[/C][C]48[/C][C]65.7407407407407[/C][C]-17.7407407407407[/C][/ROW]
[ROW][C]249[/C][C]64[/C][C]65.7407407407407[/C][C]-1.74074074074075[/C][/ROW]
[ROW][C]250[/C][C]65[/C][C]65.7407407407407[/C][C]-0.740740740740748[/C][/ROW]
[ROW][C]251[/C][C]95[/C][C]65.7407407407407[/C][C]29.2592592592593[/C][/ROW]
[ROW][C]252[/C][C]29[/C][C]65.7407407407407[/C][C]-36.7407407407407[/C][/ROW]
[ROW][C]253[/C][C]2[/C][C]65.7407407407407[/C][C]-63.7407407407407[/C][/ROW]
[ROW][C]254[/C][C]83[/C][C]65.7407407407407[/C][C]17.2592592592593[/C][/ROW]
[ROW][C]255[/C][C]11[/C][C]22[/C][C]-11[/C][/ROW]
[ROW][C]256[/C][C]16[/C][C]22[/C][C]-6[/C][/ROW]
[ROW][C]257[/C][C]9[/C][C]22[/C][C]-13[/C][/ROW]
[ROW][C]258[/C][C]46[/C][C]22[/C][C]24[/C][/ROW]
[ROW][C]259[/C][C]41[/C][C]22[/C][C]19[/C][/ROW]
[ROW][C]260[/C][C]14[/C][C]22[/C][C]-8[/C][/ROW]
[ROW][C]261[/C][C]63[/C][C]22[/C][C]41[/C][/ROW]
[ROW][C]262[/C][C]9[/C][C]22[/C][C]-13[/C][/ROW]
[ROW][C]263[/C][C]0[/C][C]22[/C][C]-22[/C][/ROW]
[ROW][C]264[/C][C]58[/C][C]22[/C][C]36[/C][/ROW]
[ROW][C]265[/C][C]18[/C][C]22[/C][C]-4[/C][/ROW]
[ROW][C]266[/C][C]42[/C][C]22[/C][C]20[/C][/ROW]
[ROW][C]267[/C][C]26[/C][C]22[/C][C]4[/C][/ROW]
[ROW][C]268[/C][C]38[/C][C]22[/C][C]16[/C][/ROW]
[ROW][C]269[/C][C]1[/C][C]22[/C][C]-21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200966&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200966&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
#ActualsForecastsResiduals
1122118.5714285714293.42857142857143
2114118.571428571429-4.57142857142857
3140118.57142857142921.4285714285714
4143118.57142857142924.4285714285714
5122118.5714285714293.42857142857143
6127118.5714285714298.42857142857143
7113118.571428571429-5.57142857142857
8118118.571428571429-0.571428571428569
9161118.57142857142942.4285714285714
10134118.57142857142915.4285714285714
1196118.571428571429-22.5714285714286
12104118.571428571429-14.5714285714286
13135118.57142857142916.4285714285714
14110118.571428571429-8.57142857142857
15128118.5714285714299.42857142857143
16142118.57142857142923.4285714285714
17117118.571428571429-1.57142857142857
1894118.571428571429-24.5714285714286
19135118.57142857142916.4285714285714
20121118.5714285714292.42857142857143
21103118.571428571429-15.5714285714286
22118118.571428571429-0.571428571428569
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269122-21



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
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')
}