Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationFri, 21 Dec 2012 07:38:17 -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/21/t1356093508352kg04dtmdpeje.htm/, Retrieved Fri, 29 Mar 2024 06:03:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203559, Retrieved Fri, 29 Mar 2024 06:03:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [tree] [2011-12-09 10:01:58] [22f8bc702946f784836540059d0d9516]
- R     [Recursive Partitioning (Regression Trees)] [paper statistiek] [2011-12-23 14:33:36] [f0855b2dc4da686ca3c0ae4fedd71fda]
-   P       [Recursive Partitioning (Regression Trees)] [] [2012-12-21 12:38:17] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

Post a new message
Dataseries X:
293403	111	74	91256	123	119

277108	70	69	86997	64	64

264020	76	76	55709	101	100

260646	109	60	75741	104	104

246100	81	89	92046	135	135

244051	67	111	84607	130	124

241329	54	57	73586	93	93

234730	106	116	162365	159	155

234509	125	122	70817	125	120

233482	68	90	59635	81	78

233406	96	85	109104	117	117

228548	106	65	120087	205	198

223914	104	89	72631	115	110

223696	88	82	104911	115	114

223004	87	84	85224	147	137

213765	84	56	58233	150	150

210554	81	73	117986	126	124

202204	44	79	67271	61	56

199512	75	59	55071	82	82

195304	93	47	114425	152	145

191467	76	75	79194	109	104

191381	87	71	101653	210	212

191276	112	90	81493	151	141

190410	84	107	64664	96	94

188967	86	75	63717	98	94

188780	98	85	72369	98	98

185139	121	83	86281	128	126

185039	94	73	63958	100	98

184217	69	45	73795	74	74

181853	87	93	96750	92	91

181379	92	123	83038	101	96

181344	75	114	65196	109	108

179562	76	89	62932	116	116

178863	86	78	57637	88	87

178140	56	91	70111	83	78

176789	115	66	123328	149	149

176460	97	55	38885	122	122

175877	95	81	54628	96	95

175568	106	80	74482	105	102

174107	49	71	76168	95	91

173587	70	70	71170	97	95

173260	41	78	37238	16	15

172684	87	112	101773	103	102

167845	105	77	103646	145	145

167131	71	69	37048	56	56

167105	56	32	85903	75	71

166790	49	59	43460	46	46

164767	51	87	90257	81	80

162810	49	76	70027	83	80

162336	111	84	111436	153	151

161678	75	59	65911	87	83

158980	84	75	105965	123	122

157250	84	106	61704	104	104

156833	79	73	48204	85	85

155383	83	75	60029	99	99

154991	63	87	52295	99	98

154730	78	82	82204	98	98

151503	93	83	56316	99	98

146455	65	68	95556	127	128

143937	98	66	78792	140	139

142339	75	67	125410	144	142

142146	108	88	76013	152	139

142141	73	87	91939	61	61

142069	66	88	57231	83	82

141933	90	75	51370	100	99

139350	70	79	99518	89	88

139144	57	76	56530	75	75

137793	70	78	56699	77	77

136911	95	86	74349	117	103

136548	89	62	83042	158	157

135171	80	61	71181	82	82

134043	54	69	55901	57	54

131876	27	83	38417	36	36

131122	56	50	65724	89	89

130539	60	47	48821	66	66

130533	64	76	85168	78	79

130232	102	83	55027	107	105

129100	38	60	73713	87	87

128655	75	70	79774	111	108

128066	42	48	42564	80	80

127619	49	50	36311	52	50

127324	79	87	56733	104	101

126683	71	123	63262	72	71

126681	39	90	94137	67	66

125971	61	45	38439	71	71

125366	69	22	34497	68	68

122433	51	91	58425	66	66

121135	50	51	42051	69	68

119291	83	38	64102	123	120

118958	52	68	54506	61	58

118807	56	81	55827	70	70

118372	72	35	66477	142	145

116900	42	36	28340	58	57

116775	30	83	73087	124	112

115199	84	54	51360	87	87

114928	44	72	53009	96	91

114397	70	65	55064	87	85

113337	58	37	63016	68	68

111664	55	59	38650	98	98

108715	64	35	40671	80	78

107342	77	53	82043	116	111

107335	48	61	49319	65	64

106539	36	68	77411	63	63

105615	57	70	202316	51	48

105410	62	72	89041	88	86

105324	42	71	26982	46	46

103012	30	37	29467	28	26

102531	46	63	40001	64	63

101324	81	104	70780	103	100

100885	39	29	49288	49	48

100672	38	69	50466	55	55

99946	106	80	99501	125	119

99768	24	62	15430	27	27

99246	27	63	37361	52	51

98599	48	55	36252	46	44

98030	30	41	31701	35	35

94763	94	75	56979	100	99

93340	41	63	43448	60	60

93125	30	29	50838	37	36

91185	57	66	21067	67	67

90961	42	78	63785	49	49

90938	40	51	37137	43	42

89318	75	78	44970	82	81

88817	70	60	46765	56	56

84944	54	72	54565	90	89

84572	43	82	72571	84	84

84256	97	58	59155	76	75

80953	49	27	56622	59	58

78800	20	66	33032	21	21

78776	30	18	26998	34	34

75812	28	57	35606	30	30

75426	3	19	47261	36	33

74398	41	30	31258	51	51

74112	28	54	174949	52	52

73567	37	31	23238	18	18

69471	22	63	22618	26	25

68948	31	47	35838	45	43

67746	18	35	62832	58	56

67507	101	112	78956	49	49

65029	21	61	32551	21	21

64320	16	56	62147	24	23

61857	23	30	25162	31	28

61499	28	75	36990	15	15

50999	2	66	63989	8	8

46660	12	13	6179	13	13

43287	13	64	43750	49	49

38214	16	21	8773	16	16

35523	0	53	52491	33	33

32750	1	22	22807	5	5

31414	18	9	14116	39	39

24188	8	7	5950	7	7

22938	12	0	1168	11	11

21054	4	0	855	4	4

17547	0	4	3926	3	3

14688	4	0	6023	5	5

7199	7	0	1644	6	6

969	0	0	0	0	0

455	0	0	0	0	0

203	0	0	0	0	0

98	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	0	0	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 8 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=203559&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=203559&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203559&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 time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Goodness of Fit
Correlation0.8124
R-squared0.66
RMSE37063.2089

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.8124 \tabularnewline
R-squared & 0.66 \tabularnewline
RMSE & 37063.2089 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203559&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.8124[/C][/ROW]
[ROW][C]R-squared[/C][C]0.66[/C][/ROW]
[ROW][C]RMSE[/C][C]37063.2089[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203559&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203559&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
Correlation0.8124
R-squared0.66
RMSE37063.2089







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1293403176061.901639344117341.098360656
2277108129880.852459016147227.147540984
3264020176061.90163934487958.0983606557
4260646176061.90163934484584.0983606557
5246100176061.90163934470038.0983606557
6244051176061.90163934467989.0983606557
7241329176061.90163934465267.0983606557
8234730176061.90163934458668.0983606557
9234509176061.90163934458447.0983606557
10233482129880.852459016103601.147540984
11233406176061.90163934457344.0983606557
12228548176061.90163934452486.0983606557
13223914176061.90163934447852.0983606557
14223696176061.90163934447634.0983606557
15223004176061.90163934446942.0983606557
16213765176061.90163934437703.0983606557
17210554176061.90163934434492.0983606557
18202204129880.85245901672323.1475409836
19199512129880.85245901669631.1475409836
20195304176061.90163934419242.0983606557
21191467176061.90163934415405.0983606557
22191381176061.90163934415319.0983606557
23191276176061.90163934415214.0983606557
24190410176061.90163934414348.0983606557
25188967176061.90163934412905.0983606557
26188780176061.90163934412718.0983606557
27185139176061.9016393449077.09836065574
28185039176061.9016393448977.09836065574
29184217129880.85245901654336.1475409836
30181853176061.9016393445791.09836065574
31181379176061.9016393445317.09836065574
32181344176061.9016393445282.09836065574
33179562176061.9016393443500.09836065574
34178863129880.85245901648982.1475409836
35178140129880.85245901648259.1475409836
36176789176061.901639344727.098360655742
37176460176061.901639344398.098360655742
38175877176061.901639344-184.901639344258
39175568176061.901639344-493.901639344258
40174107176061.901639344-1954.90163934426
41173587176061.901639344-2474.90163934426
42173260129880.85245901643379.1475409836
43172684176061.901639344-3377.90163934426
44167845176061.901639344-8216.90163934426
45167131129880.85245901637250.1475409836
46167105129880.85245901637224.1475409836
47166790129880.85245901636909.1475409836
48164767129880.85245901634886.1475409836
49162810129880.85245901632929.1475409836
50162336176061.901639344-13725.9016393443
51161678129880.85245901631797.1475409836
52158980176061.901639344-17081.9016393443
53157250176061.901639344-18811.9016393443
54156833129880.85245901626952.1475409836
55155383176061.901639344-20678.9016393443
56154991176061.901639344-21070.9016393443
57154730176061.901639344-21331.9016393443
58151503176061.901639344-24558.9016393443
59146455176061.901639344-29606.9016393443
60143937176061.901639344-32124.9016393443
61142339176061.901639344-33722.9016393443
62142146176061.901639344-33915.9016393443
63142141129880.85245901612260.1475409836
64142069129880.85245901612188.1475409836
65141933176061.901639344-34128.9016393443
66139350129880.8524590169469.14754098361
67139144129880.8524590169263.14754098361
68137793129880.8524590167912.14754098361
69136911176061.901639344-39150.9016393443
70136548176061.901639344-39513.9016393443
71135171129880.8524590165290.14754098361
72134043129880.8524590164162.14754098361
7313187686457.888888888945418.1111111111
74131122129880.8524590161241.14754098361
75130539129880.852459016658.147540983613
76130533129880.852459016652.147540983613
77130232176061.901639344-45829.9016393443
78129100129880.852459016-780.852459016387
79128655176061.901639344-47406.9016393443
80128066129880.852459016-1814.85245901639
81127619129880.852459016-2261.85245901639
82127324176061.901639344-48737.9016393443
83126683129880.852459016-3197.85245901639
84126681129880.852459016-3199.85245901639
85125971129880.852459016-3909.85245901639
86125366129880.852459016-4514.85245901639
87122433129880.852459016-7447.85245901639
88121135129880.852459016-8745.85245901639
89119291176061.901639344-56770.9016393443
90118958129880.852459016-10922.8524590164
91118807129880.852459016-11073.8524590164
92118372176061.901639344-57689.9016393443
93116900129880.852459016-12980.8524590164
9411677586457.888888888930317.1111111111
95115199129880.852459016-14681.8524590164
96114928176061.901639344-61133.9016393443
97114397129880.852459016-15483.8524590164
98113337129880.852459016-16543.8524590164
99111664176061.901639344-64397.9016393443
100108715129880.852459016-21165.8524590164
101107342176061.901639344-68719.9016393443
102107335129880.852459016-22545.8524590164
10310653986457.888888888920081.1111111111
104105615129880.852459016-24265.8524590164
105105410129880.852459016-24470.8524590164
106105324129880.852459016-24556.8524590164
10710301286457.888888888916554.1111111111
108102531129880.852459016-27349.8524590164
109101324176061.901639344-74737.9016393443
110100885129880.852459016-28995.8524590164
111100672129880.852459016-29208.8524590164
11299946176061.901639344-76115.9016393443
1139976886457.888888888913310.1111111111
1149924686457.888888888912788.1111111111
11598599129880.852459016-31281.8524590164
1169803086457.888888888911572.1111111111
11794763176061.901639344-81298.9016393443
11893340129880.852459016-36540.8524590164
1199312586457.88888888896667.11111111111
12091185129880.852459016-38695.8524590164
12190961129880.852459016-38919.8524590164
12290938129880.852459016-38942.8524590164
12389318129880.852459016-40562.8524590164
12488817129880.852459016-41063.8524590164
12584944129880.852459016-44936.8524590164
12684572129880.852459016-45308.8524590164
12784256129880.852459016-45624.8524590164
12880953129880.852459016-48927.8524590164
1297880086457.8888888889-7657.88888888889
1307877686457.8888888889-7681.88888888889
1317581286457.8888888889-10645.8888888889
1327542648633.926792.1
13374398129880.852459016-55482.8524590164
1347411286457.8888888889-12345.8888888889
1357356786457.8888888889-12890.8888888889
1366947186457.8888888889-16986.8888888889
1376894886457.8888888889-17509.8888888889
1386774648633.919112.1
13967507129880.852459016-62373.8524590164
1406502986457.8888888889-21428.8888888889
1416432048633.915686.1
1426185786457.8888888889-24600.8888888889
1436149986457.8888888889-24958.8888888889
1445099948633.92365.1
1454666048633.9-1973.9
1464328748633.9-5346.9
1473821448633.9-10419.9
1483552348633.9-13110.9
1493275048633.9-15883.9
1503141448633.9-17219.9
151241887809.9285714285716378.0714285714
152229387809.9285714285715128.0714285714
153210547809.9285714285713244.0714285714
154175477809.928571428579737.07142857143
155146887809.928571428576878.07142857143
15671997809.92857142857-610.928571428572
1579697809.92857142857-6840.92857142857
1584557809.92857142857-7354.92857142857
1592037809.92857142857-7606.92857142857
160987809.92857142857-7711.92857142857
16107809.92857142857-7809.92857142857
16207809.92857142857-7809.92857142857
16307809.92857142857-7809.92857142857
16407809.92857142857-7809.92857142857

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 293403 & 176061.901639344 & 117341.098360656 \tabularnewline
2 & 277108 & 129880.852459016 & 147227.147540984 \tabularnewline
3 & 264020 & 176061.901639344 & 87958.0983606557 \tabularnewline
4 & 260646 & 176061.901639344 & 84584.0983606557 \tabularnewline
5 & 246100 & 176061.901639344 & 70038.0983606557 \tabularnewline
6 & 244051 & 176061.901639344 & 67989.0983606557 \tabularnewline
7 & 241329 & 176061.901639344 & 65267.0983606557 \tabularnewline
8 & 234730 & 176061.901639344 & 58668.0983606557 \tabularnewline
9 & 234509 & 176061.901639344 & 58447.0983606557 \tabularnewline
10 & 233482 & 129880.852459016 & 103601.147540984 \tabularnewline
11 & 233406 & 176061.901639344 & 57344.0983606557 \tabularnewline
12 & 228548 & 176061.901639344 & 52486.0983606557 \tabularnewline
13 & 223914 & 176061.901639344 & 47852.0983606557 \tabularnewline
14 & 223696 & 176061.901639344 & 47634.0983606557 \tabularnewline
15 & 223004 & 176061.901639344 & 46942.0983606557 \tabularnewline
16 & 213765 & 176061.901639344 & 37703.0983606557 \tabularnewline
17 & 210554 & 176061.901639344 & 34492.0983606557 \tabularnewline
18 & 202204 & 129880.852459016 & 72323.1475409836 \tabularnewline
19 & 199512 & 129880.852459016 & 69631.1475409836 \tabularnewline
20 & 195304 & 176061.901639344 & 19242.0983606557 \tabularnewline
21 & 191467 & 176061.901639344 & 15405.0983606557 \tabularnewline
22 & 191381 & 176061.901639344 & 15319.0983606557 \tabularnewline
23 & 191276 & 176061.901639344 & 15214.0983606557 \tabularnewline
24 & 190410 & 176061.901639344 & 14348.0983606557 \tabularnewline
25 & 188967 & 176061.901639344 & 12905.0983606557 \tabularnewline
26 & 188780 & 176061.901639344 & 12718.0983606557 \tabularnewline
27 & 185139 & 176061.901639344 & 9077.09836065574 \tabularnewline
28 & 185039 & 176061.901639344 & 8977.09836065574 \tabularnewline
29 & 184217 & 129880.852459016 & 54336.1475409836 \tabularnewline
30 & 181853 & 176061.901639344 & 5791.09836065574 \tabularnewline
31 & 181379 & 176061.901639344 & 5317.09836065574 \tabularnewline
32 & 181344 & 176061.901639344 & 5282.09836065574 \tabularnewline
33 & 179562 & 176061.901639344 & 3500.09836065574 \tabularnewline
34 & 178863 & 129880.852459016 & 48982.1475409836 \tabularnewline
35 & 178140 & 129880.852459016 & 48259.1475409836 \tabularnewline
36 & 176789 & 176061.901639344 & 727.098360655742 \tabularnewline
37 & 176460 & 176061.901639344 & 398.098360655742 \tabularnewline
38 & 175877 & 176061.901639344 & -184.901639344258 \tabularnewline
39 & 175568 & 176061.901639344 & -493.901639344258 \tabularnewline
40 & 174107 & 176061.901639344 & -1954.90163934426 \tabularnewline
41 & 173587 & 176061.901639344 & -2474.90163934426 \tabularnewline
42 & 173260 & 129880.852459016 & 43379.1475409836 \tabularnewline
43 & 172684 & 176061.901639344 & -3377.90163934426 \tabularnewline
44 & 167845 & 176061.901639344 & -8216.90163934426 \tabularnewline
45 & 167131 & 129880.852459016 & 37250.1475409836 \tabularnewline
46 & 167105 & 129880.852459016 & 37224.1475409836 \tabularnewline
47 & 166790 & 129880.852459016 & 36909.1475409836 \tabularnewline
48 & 164767 & 129880.852459016 & 34886.1475409836 \tabularnewline
49 & 162810 & 129880.852459016 & 32929.1475409836 \tabularnewline
50 & 162336 & 176061.901639344 & -13725.9016393443 \tabularnewline
51 & 161678 & 129880.852459016 & 31797.1475409836 \tabularnewline
52 & 158980 & 176061.901639344 & -17081.9016393443 \tabularnewline
53 & 157250 & 176061.901639344 & -18811.9016393443 \tabularnewline
54 & 156833 & 129880.852459016 & 26952.1475409836 \tabularnewline
55 & 155383 & 176061.901639344 & -20678.9016393443 \tabularnewline
56 & 154991 & 176061.901639344 & -21070.9016393443 \tabularnewline
57 & 154730 & 176061.901639344 & -21331.9016393443 \tabularnewline
58 & 151503 & 176061.901639344 & -24558.9016393443 \tabularnewline
59 & 146455 & 176061.901639344 & -29606.9016393443 \tabularnewline
60 & 143937 & 176061.901639344 & -32124.9016393443 \tabularnewline
61 & 142339 & 176061.901639344 & -33722.9016393443 \tabularnewline
62 & 142146 & 176061.901639344 & -33915.9016393443 \tabularnewline
63 & 142141 & 129880.852459016 & 12260.1475409836 \tabularnewline
64 & 142069 & 129880.852459016 & 12188.1475409836 \tabularnewline
65 & 141933 & 176061.901639344 & -34128.9016393443 \tabularnewline
66 & 139350 & 129880.852459016 & 9469.14754098361 \tabularnewline
67 & 139144 & 129880.852459016 & 9263.14754098361 \tabularnewline
68 & 137793 & 129880.852459016 & 7912.14754098361 \tabularnewline
69 & 136911 & 176061.901639344 & -39150.9016393443 \tabularnewline
70 & 136548 & 176061.901639344 & -39513.9016393443 \tabularnewline
71 & 135171 & 129880.852459016 & 5290.14754098361 \tabularnewline
72 & 134043 & 129880.852459016 & 4162.14754098361 \tabularnewline
73 & 131876 & 86457.8888888889 & 45418.1111111111 \tabularnewline
74 & 131122 & 129880.852459016 & 1241.14754098361 \tabularnewline
75 & 130539 & 129880.852459016 & 658.147540983613 \tabularnewline
76 & 130533 & 129880.852459016 & 652.147540983613 \tabularnewline
77 & 130232 & 176061.901639344 & -45829.9016393443 \tabularnewline
78 & 129100 & 129880.852459016 & -780.852459016387 \tabularnewline
79 & 128655 & 176061.901639344 & -47406.9016393443 \tabularnewline
80 & 128066 & 129880.852459016 & -1814.85245901639 \tabularnewline
81 & 127619 & 129880.852459016 & -2261.85245901639 \tabularnewline
82 & 127324 & 176061.901639344 & -48737.9016393443 \tabularnewline
83 & 126683 & 129880.852459016 & -3197.85245901639 \tabularnewline
84 & 126681 & 129880.852459016 & -3199.85245901639 \tabularnewline
85 & 125971 & 129880.852459016 & -3909.85245901639 \tabularnewline
86 & 125366 & 129880.852459016 & -4514.85245901639 \tabularnewline
87 & 122433 & 129880.852459016 & -7447.85245901639 \tabularnewline
88 & 121135 & 129880.852459016 & -8745.85245901639 \tabularnewline
89 & 119291 & 176061.901639344 & -56770.9016393443 \tabularnewline
90 & 118958 & 129880.852459016 & -10922.8524590164 \tabularnewline
91 & 118807 & 129880.852459016 & -11073.8524590164 \tabularnewline
92 & 118372 & 176061.901639344 & -57689.9016393443 \tabularnewline
93 & 116900 & 129880.852459016 & -12980.8524590164 \tabularnewline
94 & 116775 & 86457.8888888889 & 30317.1111111111 \tabularnewline
95 & 115199 & 129880.852459016 & -14681.8524590164 \tabularnewline
96 & 114928 & 176061.901639344 & -61133.9016393443 \tabularnewline
97 & 114397 & 129880.852459016 & -15483.8524590164 \tabularnewline
98 & 113337 & 129880.852459016 & -16543.8524590164 \tabularnewline
99 & 111664 & 176061.901639344 & -64397.9016393443 \tabularnewline
100 & 108715 & 129880.852459016 & -21165.8524590164 \tabularnewline
101 & 107342 & 176061.901639344 & -68719.9016393443 \tabularnewline
102 & 107335 & 129880.852459016 & -22545.8524590164 \tabularnewline
103 & 106539 & 86457.8888888889 & 20081.1111111111 \tabularnewline
104 & 105615 & 129880.852459016 & -24265.8524590164 \tabularnewline
105 & 105410 & 129880.852459016 & -24470.8524590164 \tabularnewline
106 & 105324 & 129880.852459016 & -24556.8524590164 \tabularnewline
107 & 103012 & 86457.8888888889 & 16554.1111111111 \tabularnewline
108 & 102531 & 129880.852459016 & -27349.8524590164 \tabularnewline
109 & 101324 & 176061.901639344 & -74737.9016393443 \tabularnewline
110 & 100885 & 129880.852459016 & -28995.8524590164 \tabularnewline
111 & 100672 & 129880.852459016 & -29208.8524590164 \tabularnewline
112 & 99946 & 176061.901639344 & -76115.9016393443 \tabularnewline
113 & 99768 & 86457.8888888889 & 13310.1111111111 \tabularnewline
114 & 99246 & 86457.8888888889 & 12788.1111111111 \tabularnewline
115 & 98599 & 129880.852459016 & -31281.8524590164 \tabularnewline
116 & 98030 & 86457.8888888889 & 11572.1111111111 \tabularnewline
117 & 94763 & 176061.901639344 & -81298.9016393443 \tabularnewline
118 & 93340 & 129880.852459016 & -36540.8524590164 \tabularnewline
119 & 93125 & 86457.8888888889 & 6667.11111111111 \tabularnewline
120 & 91185 & 129880.852459016 & -38695.8524590164 \tabularnewline
121 & 90961 & 129880.852459016 & -38919.8524590164 \tabularnewline
122 & 90938 & 129880.852459016 & -38942.8524590164 \tabularnewline
123 & 89318 & 129880.852459016 & -40562.8524590164 \tabularnewline
124 & 88817 & 129880.852459016 & -41063.8524590164 \tabularnewline
125 & 84944 & 129880.852459016 & -44936.8524590164 \tabularnewline
126 & 84572 & 129880.852459016 & -45308.8524590164 \tabularnewline
127 & 84256 & 129880.852459016 & -45624.8524590164 \tabularnewline
128 & 80953 & 129880.852459016 & -48927.8524590164 \tabularnewline
129 & 78800 & 86457.8888888889 & -7657.88888888889 \tabularnewline
130 & 78776 & 86457.8888888889 & -7681.88888888889 \tabularnewline
131 & 75812 & 86457.8888888889 & -10645.8888888889 \tabularnewline
132 & 75426 & 48633.9 & 26792.1 \tabularnewline
133 & 74398 & 129880.852459016 & -55482.8524590164 \tabularnewline
134 & 74112 & 86457.8888888889 & -12345.8888888889 \tabularnewline
135 & 73567 & 86457.8888888889 & -12890.8888888889 \tabularnewline
136 & 69471 & 86457.8888888889 & -16986.8888888889 \tabularnewline
137 & 68948 & 86457.8888888889 & -17509.8888888889 \tabularnewline
138 & 67746 & 48633.9 & 19112.1 \tabularnewline
139 & 67507 & 129880.852459016 & -62373.8524590164 \tabularnewline
140 & 65029 & 86457.8888888889 & -21428.8888888889 \tabularnewline
141 & 64320 & 48633.9 & 15686.1 \tabularnewline
142 & 61857 & 86457.8888888889 & -24600.8888888889 \tabularnewline
143 & 61499 & 86457.8888888889 & -24958.8888888889 \tabularnewline
144 & 50999 & 48633.9 & 2365.1 \tabularnewline
145 & 46660 & 48633.9 & -1973.9 \tabularnewline
146 & 43287 & 48633.9 & -5346.9 \tabularnewline
147 & 38214 & 48633.9 & -10419.9 \tabularnewline
148 & 35523 & 48633.9 & -13110.9 \tabularnewline
149 & 32750 & 48633.9 & -15883.9 \tabularnewline
150 & 31414 & 48633.9 & -17219.9 \tabularnewline
151 & 24188 & 7809.92857142857 & 16378.0714285714 \tabularnewline
152 & 22938 & 7809.92857142857 & 15128.0714285714 \tabularnewline
153 & 21054 & 7809.92857142857 & 13244.0714285714 \tabularnewline
154 & 17547 & 7809.92857142857 & 9737.07142857143 \tabularnewline
155 & 14688 & 7809.92857142857 & 6878.07142857143 \tabularnewline
156 & 7199 & 7809.92857142857 & -610.928571428572 \tabularnewline
157 & 969 & 7809.92857142857 & -6840.92857142857 \tabularnewline
158 & 455 & 7809.92857142857 & -7354.92857142857 \tabularnewline
159 & 203 & 7809.92857142857 & -7606.92857142857 \tabularnewline
160 & 98 & 7809.92857142857 & -7711.92857142857 \tabularnewline
161 & 0 & 7809.92857142857 & -7809.92857142857 \tabularnewline
162 & 0 & 7809.92857142857 & -7809.92857142857 \tabularnewline
163 & 0 & 7809.92857142857 & -7809.92857142857 \tabularnewline
164 & 0 & 7809.92857142857 & -7809.92857142857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203559&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]293403[/C][C]176061.901639344[/C][C]117341.098360656[/C][/ROW]
[ROW][C]2[/C][C]277108[/C][C]129880.852459016[/C][C]147227.147540984[/C][/ROW]
[ROW][C]3[/C][C]264020[/C][C]176061.901639344[/C][C]87958.0983606557[/C][/ROW]
[ROW][C]4[/C][C]260646[/C][C]176061.901639344[/C][C]84584.0983606557[/C][/ROW]
[ROW][C]5[/C][C]246100[/C][C]176061.901639344[/C][C]70038.0983606557[/C][/ROW]
[ROW][C]6[/C][C]244051[/C][C]176061.901639344[/C][C]67989.0983606557[/C][/ROW]
[ROW][C]7[/C][C]241329[/C][C]176061.901639344[/C][C]65267.0983606557[/C][/ROW]
[ROW][C]8[/C][C]234730[/C][C]176061.901639344[/C][C]58668.0983606557[/C][/ROW]
[ROW][C]9[/C][C]234509[/C][C]176061.901639344[/C][C]58447.0983606557[/C][/ROW]
[ROW][C]10[/C][C]233482[/C][C]129880.852459016[/C][C]103601.147540984[/C][/ROW]
[ROW][C]11[/C][C]233406[/C][C]176061.901639344[/C][C]57344.0983606557[/C][/ROW]
[ROW][C]12[/C][C]228548[/C][C]176061.901639344[/C][C]52486.0983606557[/C][/ROW]
[ROW][C]13[/C][C]223914[/C][C]176061.901639344[/C][C]47852.0983606557[/C][/ROW]
[ROW][C]14[/C][C]223696[/C][C]176061.901639344[/C][C]47634.0983606557[/C][/ROW]
[ROW][C]15[/C][C]223004[/C][C]176061.901639344[/C][C]46942.0983606557[/C][/ROW]
[ROW][C]16[/C][C]213765[/C][C]176061.901639344[/C][C]37703.0983606557[/C][/ROW]
[ROW][C]17[/C][C]210554[/C][C]176061.901639344[/C][C]34492.0983606557[/C][/ROW]
[ROW][C]18[/C][C]202204[/C][C]129880.852459016[/C][C]72323.1475409836[/C][/ROW]
[ROW][C]19[/C][C]199512[/C][C]129880.852459016[/C][C]69631.1475409836[/C][/ROW]
[ROW][C]20[/C][C]195304[/C][C]176061.901639344[/C][C]19242.0983606557[/C][/ROW]
[ROW][C]21[/C][C]191467[/C][C]176061.901639344[/C][C]15405.0983606557[/C][/ROW]
[ROW][C]22[/C][C]191381[/C][C]176061.901639344[/C][C]15319.0983606557[/C][/ROW]
[ROW][C]23[/C][C]191276[/C][C]176061.901639344[/C][C]15214.0983606557[/C][/ROW]
[ROW][C]24[/C][C]190410[/C][C]176061.901639344[/C][C]14348.0983606557[/C][/ROW]
[ROW][C]25[/C][C]188967[/C][C]176061.901639344[/C][C]12905.0983606557[/C][/ROW]
[ROW][C]26[/C][C]188780[/C][C]176061.901639344[/C][C]12718.0983606557[/C][/ROW]
[ROW][C]27[/C][C]185139[/C][C]176061.901639344[/C][C]9077.09836065574[/C][/ROW]
[ROW][C]28[/C][C]185039[/C][C]176061.901639344[/C][C]8977.09836065574[/C][/ROW]
[ROW][C]29[/C][C]184217[/C][C]129880.852459016[/C][C]54336.1475409836[/C][/ROW]
[ROW][C]30[/C][C]181853[/C][C]176061.901639344[/C][C]5791.09836065574[/C][/ROW]
[ROW][C]31[/C][C]181379[/C][C]176061.901639344[/C][C]5317.09836065574[/C][/ROW]
[ROW][C]32[/C][C]181344[/C][C]176061.901639344[/C][C]5282.09836065574[/C][/ROW]
[ROW][C]33[/C][C]179562[/C][C]176061.901639344[/C][C]3500.09836065574[/C][/ROW]
[ROW][C]34[/C][C]178863[/C][C]129880.852459016[/C][C]48982.1475409836[/C][/ROW]
[ROW][C]35[/C][C]178140[/C][C]129880.852459016[/C][C]48259.1475409836[/C][/ROW]
[ROW][C]36[/C][C]176789[/C][C]176061.901639344[/C][C]727.098360655742[/C][/ROW]
[ROW][C]37[/C][C]176460[/C][C]176061.901639344[/C][C]398.098360655742[/C][/ROW]
[ROW][C]38[/C][C]175877[/C][C]176061.901639344[/C][C]-184.901639344258[/C][/ROW]
[ROW][C]39[/C][C]175568[/C][C]176061.901639344[/C][C]-493.901639344258[/C][/ROW]
[ROW][C]40[/C][C]174107[/C][C]176061.901639344[/C][C]-1954.90163934426[/C][/ROW]
[ROW][C]41[/C][C]173587[/C][C]176061.901639344[/C][C]-2474.90163934426[/C][/ROW]
[ROW][C]42[/C][C]173260[/C][C]129880.852459016[/C][C]43379.1475409836[/C][/ROW]
[ROW][C]43[/C][C]172684[/C][C]176061.901639344[/C][C]-3377.90163934426[/C][/ROW]
[ROW][C]44[/C][C]167845[/C][C]176061.901639344[/C][C]-8216.90163934426[/C][/ROW]
[ROW][C]45[/C][C]167131[/C][C]129880.852459016[/C][C]37250.1475409836[/C][/ROW]
[ROW][C]46[/C][C]167105[/C][C]129880.852459016[/C][C]37224.1475409836[/C][/ROW]
[ROW][C]47[/C][C]166790[/C][C]129880.852459016[/C][C]36909.1475409836[/C][/ROW]
[ROW][C]48[/C][C]164767[/C][C]129880.852459016[/C][C]34886.1475409836[/C][/ROW]
[ROW][C]49[/C][C]162810[/C][C]129880.852459016[/C][C]32929.1475409836[/C][/ROW]
[ROW][C]50[/C][C]162336[/C][C]176061.901639344[/C][C]-13725.9016393443[/C][/ROW]
[ROW][C]51[/C][C]161678[/C][C]129880.852459016[/C][C]31797.1475409836[/C][/ROW]
[ROW][C]52[/C][C]158980[/C][C]176061.901639344[/C][C]-17081.9016393443[/C][/ROW]
[ROW][C]53[/C][C]157250[/C][C]176061.901639344[/C][C]-18811.9016393443[/C][/ROW]
[ROW][C]54[/C][C]156833[/C][C]129880.852459016[/C][C]26952.1475409836[/C][/ROW]
[ROW][C]55[/C][C]155383[/C][C]176061.901639344[/C][C]-20678.9016393443[/C][/ROW]
[ROW][C]56[/C][C]154991[/C][C]176061.901639344[/C][C]-21070.9016393443[/C][/ROW]
[ROW][C]57[/C][C]154730[/C][C]176061.901639344[/C][C]-21331.9016393443[/C][/ROW]
[ROW][C]58[/C][C]151503[/C][C]176061.901639344[/C][C]-24558.9016393443[/C][/ROW]
[ROW][C]59[/C][C]146455[/C][C]176061.901639344[/C][C]-29606.9016393443[/C][/ROW]
[ROW][C]60[/C][C]143937[/C][C]176061.901639344[/C][C]-32124.9016393443[/C][/ROW]
[ROW][C]61[/C][C]142339[/C][C]176061.901639344[/C][C]-33722.9016393443[/C][/ROW]
[ROW][C]62[/C][C]142146[/C][C]176061.901639344[/C][C]-33915.9016393443[/C][/ROW]
[ROW][C]63[/C][C]142141[/C][C]129880.852459016[/C][C]12260.1475409836[/C][/ROW]
[ROW][C]64[/C][C]142069[/C][C]129880.852459016[/C][C]12188.1475409836[/C][/ROW]
[ROW][C]65[/C][C]141933[/C][C]176061.901639344[/C][C]-34128.9016393443[/C][/ROW]
[ROW][C]66[/C][C]139350[/C][C]129880.852459016[/C][C]9469.14754098361[/C][/ROW]
[ROW][C]67[/C][C]139144[/C][C]129880.852459016[/C][C]9263.14754098361[/C][/ROW]
[ROW][C]68[/C][C]137793[/C][C]129880.852459016[/C][C]7912.14754098361[/C][/ROW]
[ROW][C]69[/C][C]136911[/C][C]176061.901639344[/C][C]-39150.9016393443[/C][/ROW]
[ROW][C]70[/C][C]136548[/C][C]176061.901639344[/C][C]-39513.9016393443[/C][/ROW]
[ROW][C]71[/C][C]135171[/C][C]129880.852459016[/C][C]5290.14754098361[/C][/ROW]
[ROW][C]72[/C][C]134043[/C][C]129880.852459016[/C][C]4162.14754098361[/C][/ROW]
[ROW][C]73[/C][C]131876[/C][C]86457.8888888889[/C][C]45418.1111111111[/C][/ROW]
[ROW][C]74[/C][C]131122[/C][C]129880.852459016[/C][C]1241.14754098361[/C][/ROW]
[ROW][C]75[/C][C]130539[/C][C]129880.852459016[/C][C]658.147540983613[/C][/ROW]
[ROW][C]76[/C][C]130533[/C][C]129880.852459016[/C][C]652.147540983613[/C][/ROW]
[ROW][C]77[/C][C]130232[/C][C]176061.901639344[/C][C]-45829.9016393443[/C][/ROW]
[ROW][C]78[/C][C]129100[/C][C]129880.852459016[/C][C]-780.852459016387[/C][/ROW]
[ROW][C]79[/C][C]128655[/C][C]176061.901639344[/C][C]-47406.9016393443[/C][/ROW]
[ROW][C]80[/C][C]128066[/C][C]129880.852459016[/C][C]-1814.85245901639[/C][/ROW]
[ROW][C]81[/C][C]127619[/C][C]129880.852459016[/C][C]-2261.85245901639[/C][/ROW]
[ROW][C]82[/C][C]127324[/C][C]176061.901639344[/C][C]-48737.9016393443[/C][/ROW]
[ROW][C]83[/C][C]126683[/C][C]129880.852459016[/C][C]-3197.85245901639[/C][/ROW]
[ROW][C]84[/C][C]126681[/C][C]129880.852459016[/C][C]-3199.85245901639[/C][/ROW]
[ROW][C]85[/C][C]125971[/C][C]129880.852459016[/C][C]-3909.85245901639[/C][/ROW]
[ROW][C]86[/C][C]125366[/C][C]129880.852459016[/C][C]-4514.85245901639[/C][/ROW]
[ROW][C]87[/C][C]122433[/C][C]129880.852459016[/C][C]-7447.85245901639[/C][/ROW]
[ROW][C]88[/C][C]121135[/C][C]129880.852459016[/C][C]-8745.85245901639[/C][/ROW]
[ROW][C]89[/C][C]119291[/C][C]176061.901639344[/C][C]-56770.9016393443[/C][/ROW]
[ROW][C]90[/C][C]118958[/C][C]129880.852459016[/C][C]-10922.8524590164[/C][/ROW]
[ROW][C]91[/C][C]118807[/C][C]129880.852459016[/C][C]-11073.8524590164[/C][/ROW]
[ROW][C]92[/C][C]118372[/C][C]176061.901639344[/C][C]-57689.9016393443[/C][/ROW]
[ROW][C]93[/C][C]116900[/C][C]129880.852459016[/C][C]-12980.8524590164[/C][/ROW]
[ROW][C]94[/C][C]116775[/C][C]86457.8888888889[/C][C]30317.1111111111[/C][/ROW]
[ROW][C]95[/C][C]115199[/C][C]129880.852459016[/C][C]-14681.8524590164[/C][/ROW]
[ROW][C]96[/C][C]114928[/C][C]176061.901639344[/C][C]-61133.9016393443[/C][/ROW]
[ROW][C]97[/C][C]114397[/C][C]129880.852459016[/C][C]-15483.8524590164[/C][/ROW]
[ROW][C]98[/C][C]113337[/C][C]129880.852459016[/C][C]-16543.8524590164[/C][/ROW]
[ROW][C]99[/C][C]111664[/C][C]176061.901639344[/C][C]-64397.9016393443[/C][/ROW]
[ROW][C]100[/C][C]108715[/C][C]129880.852459016[/C][C]-21165.8524590164[/C][/ROW]
[ROW][C]101[/C][C]107342[/C][C]176061.901639344[/C][C]-68719.9016393443[/C][/ROW]
[ROW][C]102[/C][C]107335[/C][C]129880.852459016[/C][C]-22545.8524590164[/C][/ROW]
[ROW][C]103[/C][C]106539[/C][C]86457.8888888889[/C][C]20081.1111111111[/C][/ROW]
[ROW][C]104[/C][C]105615[/C][C]129880.852459016[/C][C]-24265.8524590164[/C][/ROW]
[ROW][C]105[/C][C]105410[/C][C]129880.852459016[/C][C]-24470.8524590164[/C][/ROW]
[ROW][C]106[/C][C]105324[/C][C]129880.852459016[/C][C]-24556.8524590164[/C][/ROW]
[ROW][C]107[/C][C]103012[/C][C]86457.8888888889[/C][C]16554.1111111111[/C][/ROW]
[ROW][C]108[/C][C]102531[/C][C]129880.852459016[/C][C]-27349.8524590164[/C][/ROW]
[ROW][C]109[/C][C]101324[/C][C]176061.901639344[/C][C]-74737.9016393443[/C][/ROW]
[ROW][C]110[/C][C]100885[/C][C]129880.852459016[/C][C]-28995.8524590164[/C][/ROW]
[ROW][C]111[/C][C]100672[/C][C]129880.852459016[/C][C]-29208.8524590164[/C][/ROW]
[ROW][C]112[/C][C]99946[/C][C]176061.901639344[/C][C]-76115.9016393443[/C][/ROW]
[ROW][C]113[/C][C]99768[/C][C]86457.8888888889[/C][C]13310.1111111111[/C][/ROW]
[ROW][C]114[/C][C]99246[/C][C]86457.8888888889[/C][C]12788.1111111111[/C][/ROW]
[ROW][C]115[/C][C]98599[/C][C]129880.852459016[/C][C]-31281.8524590164[/C][/ROW]
[ROW][C]116[/C][C]98030[/C][C]86457.8888888889[/C][C]11572.1111111111[/C][/ROW]
[ROW][C]117[/C][C]94763[/C][C]176061.901639344[/C][C]-81298.9016393443[/C][/ROW]
[ROW][C]118[/C][C]93340[/C][C]129880.852459016[/C][C]-36540.8524590164[/C][/ROW]
[ROW][C]119[/C][C]93125[/C][C]86457.8888888889[/C][C]6667.11111111111[/C][/ROW]
[ROW][C]120[/C][C]91185[/C][C]129880.852459016[/C][C]-38695.8524590164[/C][/ROW]
[ROW][C]121[/C][C]90961[/C][C]129880.852459016[/C][C]-38919.8524590164[/C][/ROW]
[ROW][C]122[/C][C]90938[/C][C]129880.852459016[/C][C]-38942.8524590164[/C][/ROW]
[ROW][C]123[/C][C]89318[/C][C]129880.852459016[/C][C]-40562.8524590164[/C][/ROW]
[ROW][C]124[/C][C]88817[/C][C]129880.852459016[/C][C]-41063.8524590164[/C][/ROW]
[ROW][C]125[/C][C]84944[/C][C]129880.852459016[/C][C]-44936.8524590164[/C][/ROW]
[ROW][C]126[/C][C]84572[/C][C]129880.852459016[/C][C]-45308.8524590164[/C][/ROW]
[ROW][C]127[/C][C]84256[/C][C]129880.852459016[/C][C]-45624.8524590164[/C][/ROW]
[ROW][C]128[/C][C]80953[/C][C]129880.852459016[/C][C]-48927.8524590164[/C][/ROW]
[ROW][C]129[/C][C]78800[/C][C]86457.8888888889[/C][C]-7657.88888888889[/C][/ROW]
[ROW][C]130[/C][C]78776[/C][C]86457.8888888889[/C][C]-7681.88888888889[/C][/ROW]
[ROW][C]131[/C][C]75812[/C][C]86457.8888888889[/C][C]-10645.8888888889[/C][/ROW]
[ROW][C]132[/C][C]75426[/C][C]48633.9[/C][C]26792.1[/C][/ROW]
[ROW][C]133[/C][C]74398[/C][C]129880.852459016[/C][C]-55482.8524590164[/C][/ROW]
[ROW][C]134[/C][C]74112[/C][C]86457.8888888889[/C][C]-12345.8888888889[/C][/ROW]
[ROW][C]135[/C][C]73567[/C][C]86457.8888888889[/C][C]-12890.8888888889[/C][/ROW]
[ROW][C]136[/C][C]69471[/C][C]86457.8888888889[/C][C]-16986.8888888889[/C][/ROW]
[ROW][C]137[/C][C]68948[/C][C]86457.8888888889[/C][C]-17509.8888888889[/C][/ROW]
[ROW][C]138[/C][C]67746[/C][C]48633.9[/C][C]19112.1[/C][/ROW]
[ROW][C]139[/C][C]67507[/C][C]129880.852459016[/C][C]-62373.8524590164[/C][/ROW]
[ROW][C]140[/C][C]65029[/C][C]86457.8888888889[/C][C]-21428.8888888889[/C][/ROW]
[ROW][C]141[/C][C]64320[/C][C]48633.9[/C][C]15686.1[/C][/ROW]
[ROW][C]142[/C][C]61857[/C][C]86457.8888888889[/C][C]-24600.8888888889[/C][/ROW]
[ROW][C]143[/C][C]61499[/C][C]86457.8888888889[/C][C]-24958.8888888889[/C][/ROW]
[ROW][C]144[/C][C]50999[/C][C]48633.9[/C][C]2365.1[/C][/ROW]
[ROW][C]145[/C][C]46660[/C][C]48633.9[/C][C]-1973.9[/C][/ROW]
[ROW][C]146[/C][C]43287[/C][C]48633.9[/C][C]-5346.9[/C][/ROW]
[ROW][C]147[/C][C]38214[/C][C]48633.9[/C][C]-10419.9[/C][/ROW]
[ROW][C]148[/C][C]35523[/C][C]48633.9[/C][C]-13110.9[/C][/ROW]
[ROW][C]149[/C][C]32750[/C][C]48633.9[/C][C]-15883.9[/C][/ROW]
[ROW][C]150[/C][C]31414[/C][C]48633.9[/C][C]-17219.9[/C][/ROW]
[ROW][C]151[/C][C]24188[/C][C]7809.92857142857[/C][C]16378.0714285714[/C][/ROW]
[ROW][C]152[/C][C]22938[/C][C]7809.92857142857[/C][C]15128.0714285714[/C][/ROW]
[ROW][C]153[/C][C]21054[/C][C]7809.92857142857[/C][C]13244.0714285714[/C][/ROW]
[ROW][C]154[/C][C]17547[/C][C]7809.92857142857[/C][C]9737.07142857143[/C][/ROW]
[ROW][C]155[/C][C]14688[/C][C]7809.92857142857[/C][C]6878.07142857143[/C][/ROW]
[ROW][C]156[/C][C]7199[/C][C]7809.92857142857[/C][C]-610.928571428572[/C][/ROW]
[ROW][C]157[/C][C]969[/C][C]7809.92857142857[/C][C]-6840.92857142857[/C][/ROW]
[ROW][C]158[/C][C]455[/C][C]7809.92857142857[/C][C]-7354.92857142857[/C][/ROW]
[ROW][C]159[/C][C]203[/C][C]7809.92857142857[/C][C]-7606.92857142857[/C][/ROW]
[ROW][C]160[/C][C]98[/C][C]7809.92857142857[/C][C]-7711.92857142857[/C][/ROW]
[ROW][C]161[/C][C]0[/C][C]7809.92857142857[/C][C]-7809.92857142857[/C][/ROW]
[ROW][C]162[/C][C]0[/C][C]7809.92857142857[/C][C]-7809.92857142857[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]7809.92857142857[/C][C]-7809.92857142857[/C][/ROW]
[ROW][C]164[/C][C]0[/C][C]7809.92857142857[/C][C]-7809.92857142857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203559&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203559&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
1293403176061.901639344117341.098360656
2277108129880.852459016147227.147540984
3264020176061.90163934487958.0983606557
4260646176061.90163934484584.0983606557
5246100176061.90163934470038.0983606557
6244051176061.90163934467989.0983606557
7241329176061.90163934465267.0983606557
8234730176061.90163934458668.0983606557
9234509176061.90163934458447.0983606557
10233482129880.852459016103601.147540984
11233406176061.90163934457344.0983606557
12228548176061.90163934452486.0983606557
13223914176061.90163934447852.0983606557
14223696176061.90163934447634.0983606557
15223004176061.90163934446942.0983606557
16213765176061.90163934437703.0983606557
17210554176061.90163934434492.0983606557
18202204129880.85245901672323.1475409836
19199512129880.85245901669631.1475409836
20195304176061.90163934419242.0983606557
21191467176061.90163934415405.0983606557
22191381176061.90163934415319.0983606557
23191276176061.90163934415214.0983606557
24190410176061.90163934414348.0983606557
25188967176061.90163934412905.0983606557
26188780176061.90163934412718.0983606557
27185139176061.9016393449077.09836065574
28185039176061.9016393448977.09836065574
29184217129880.85245901654336.1475409836
30181853176061.9016393445791.09836065574
31181379176061.9016393445317.09836065574
32181344176061.9016393445282.09836065574
33179562176061.9016393443500.09836065574
34178863129880.85245901648982.1475409836
35178140129880.85245901648259.1475409836
36176789176061.901639344727.098360655742
37176460176061.901639344398.098360655742
38175877176061.901639344-184.901639344258
39175568176061.901639344-493.901639344258
40174107176061.901639344-1954.90163934426
41173587176061.901639344-2474.90163934426
42173260129880.85245901643379.1475409836
43172684176061.901639344-3377.90163934426
44167845176061.901639344-8216.90163934426
45167131129880.85245901637250.1475409836
46167105129880.85245901637224.1475409836
47166790129880.85245901636909.1475409836
48164767129880.85245901634886.1475409836
49162810129880.85245901632929.1475409836
50162336176061.901639344-13725.9016393443
51161678129880.85245901631797.1475409836
52158980176061.901639344-17081.9016393443
53157250176061.901639344-18811.9016393443
54156833129880.85245901626952.1475409836
55155383176061.901639344-20678.9016393443
56154991176061.901639344-21070.9016393443
57154730176061.901639344-21331.9016393443
58151503176061.901639344-24558.9016393443
59146455176061.901639344-29606.9016393443
60143937176061.901639344-32124.9016393443
61142339176061.901639344-33722.9016393443
62142146176061.901639344-33915.9016393443
63142141129880.85245901612260.1475409836
64142069129880.85245901612188.1475409836
65141933176061.901639344-34128.9016393443
66139350129880.8524590169469.14754098361
67139144129880.8524590169263.14754098361
68137793129880.8524590167912.14754098361
69136911176061.901639344-39150.9016393443
70136548176061.901639344-39513.9016393443
71135171129880.8524590165290.14754098361
72134043129880.8524590164162.14754098361
7313187686457.888888888945418.1111111111
74131122129880.8524590161241.14754098361
75130539129880.852459016658.147540983613
76130533129880.852459016652.147540983613
77130232176061.901639344-45829.9016393443
78129100129880.852459016-780.852459016387
79128655176061.901639344-47406.9016393443
80128066129880.852459016-1814.85245901639
81127619129880.852459016-2261.85245901639
82127324176061.901639344-48737.9016393443
83126683129880.852459016-3197.85245901639
84126681129880.852459016-3199.85245901639
85125971129880.852459016-3909.85245901639
86125366129880.852459016-4514.85245901639
87122433129880.852459016-7447.85245901639
88121135129880.852459016-8745.85245901639
89119291176061.901639344-56770.9016393443
90118958129880.852459016-10922.8524590164
91118807129880.852459016-11073.8524590164
92118372176061.901639344-57689.9016393443
93116900129880.852459016-12980.8524590164
9411677586457.888888888930317.1111111111
95115199129880.852459016-14681.8524590164
96114928176061.901639344-61133.9016393443
97114397129880.852459016-15483.8524590164
98113337129880.852459016-16543.8524590164
99111664176061.901639344-64397.9016393443
100108715129880.852459016-21165.8524590164
101107342176061.901639344-68719.9016393443
102107335129880.852459016-22545.8524590164
10310653986457.888888888920081.1111111111
104105615129880.852459016-24265.8524590164
105105410129880.852459016-24470.8524590164
106105324129880.852459016-24556.8524590164
10710301286457.888888888916554.1111111111
108102531129880.852459016-27349.8524590164
109101324176061.901639344-74737.9016393443
110100885129880.852459016-28995.8524590164
111100672129880.852459016-29208.8524590164
11299946176061.901639344-76115.9016393443
1139976886457.888888888913310.1111111111
1149924686457.888888888912788.1111111111
11598599129880.852459016-31281.8524590164
1169803086457.888888888911572.1111111111
11794763176061.901639344-81298.9016393443
11893340129880.852459016-36540.8524590164
1199312586457.88888888896667.11111111111
12091185129880.852459016-38695.8524590164
12190961129880.852459016-38919.8524590164
12290938129880.852459016-38942.8524590164
12389318129880.852459016-40562.8524590164
12488817129880.852459016-41063.8524590164
12584944129880.852459016-44936.8524590164
12684572129880.852459016-45308.8524590164
12784256129880.852459016-45624.8524590164
12880953129880.852459016-48927.8524590164
1297880086457.8888888889-7657.88888888889
1307877686457.8888888889-7681.88888888889
1317581286457.8888888889-10645.8888888889
1327542648633.926792.1
13374398129880.852459016-55482.8524590164
1347411286457.8888888889-12345.8888888889
1357356786457.8888888889-12890.8888888889
1366947186457.8888888889-16986.8888888889
1376894886457.8888888889-17509.8888888889
1386774648633.919112.1
13967507129880.852459016-62373.8524590164
1406502986457.8888888889-21428.8888888889
1416432048633.915686.1
1426185786457.8888888889-24600.8888888889
1436149986457.8888888889-24958.8888888889
1445099948633.92365.1
1454666048633.9-1973.9
1464328748633.9-5346.9
1473821448633.9-10419.9
1483552348633.9-13110.9
1493275048633.9-15883.9
1503141448633.9-17219.9
151241887809.9285714285716378.0714285714
152229387809.9285714285715128.0714285714
153210547809.9285714285713244.0714285714
154175477809.928571428579737.07142857143
155146887809.928571428576878.07142857143
15671997809.92857142857-610.928571428572
1579697809.92857142857-6840.92857142857
1584557809.92857142857-7354.92857142857
1592037809.92857142857-7606.92857142857
160987809.92857142857-7711.92857142857
16107809.92857142857-7809.92857142857
16207809.92857142857-7809.92857142857
16307809.92857142857-7809.92857142857
16407809.92857142857-7809.92857142857



Parameters (Session):
par1 = kendall ;
Parameters (R input):
par1 = 1 ; par2 = none ; par3 = ; 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')
}