Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationTue, 11 Dec 2012 09:20:40 -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/11/t13552356491rf53t3jkalu6wq.htm/, Retrieved Thu, 28 Mar 2024 23:55:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198506, Retrieved Thu, 28 Mar 2024 23:55:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 20:21:33] [b98453cac15ba1066b407e146608df68]
- RMP     [Recursive Partitioning (Regression Trees)] [WS 10] [2012-12-11 14:20:40] [0d2ad79739942b80a90a457d326f3d01] [Current]
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Dataseries X:
1	1	41	38	13	12	14
1	1	39	32	16	11	18
1	1	30	35	19	15	11
1	0	31	33	15	6	12
1	1	34	37	14	13	16
1	1	35	29	13	10	18
1	1	39	31	19	12	14
1	1	34	36	15	14	14
1	1	36	35	14	12	15
1	1	37	38	15	9	15
1	0	38	31	16	10	17
1	1	36	34	16	12	19
1	0	38	35	16	12	10
1	1	39	38	16	11	16
1	1	33	37	17	15	18
1	0	32	33	15	12	14
1	0	36	32	15	10	14
1	1	38	38	20	12	17
1	0	39	38	18	11	14
1	1	32	32	16	12	16
1	0	32	33	16	11	18
1	1	31	31	16	12	11
1	1	39	38	19	13	14
1	1	37	39	16	11	12
1	0	39	32	17	12	17
1	1	41	32	17	13	9
1	0	36	35	16	10	16
1	1	33	37	15	14	14
1	1	33	33	16	12	15
1	0	34	33	14	10	11
1	1	31	31	15	12	16
1	0	27	32	12	8	13
1	1	37	31	14	10	17
1	1	34	37	16	12	15
1	0	34	30	14	12	14
1	0	32	33	10	7	16
1	0	29	31	10	9	9
1	0	36	33	14	12	15
1	1	29	31	16	10	17
1	0	35	33	16	10	13
1	0	37	32	16	10	15
1	1	34	33	14	12	16
1	0	38	32	20	15	16
1	0	35	33	14	10	12
1	1	38	28	14	10	15
1	1	37	35	11	12	11
1	1	38	39	14	13	15
1	1	33	34	15	11	15
1	1	36	38	16	11	17
1	0	38	32	14	12	13
1	1	32	38	16	14	16
1	0	32	30	14	10	14
1	0	32	33	12	12	11
1	1	34	38	16	13	12
1	0	32	32	9	5	12
1	1	37	35	14	6	15
1	1	39	34	16	12	16
1	1	29	34	16	12	15
1	0	37	36	15	11	12
1	1	35	34	16	10	12
1	0	30	28	12	7	8
1	0	38	34	16	12	13
1	1	34	35	16	14	11
1	1	31	35	14	11	14
1	1	34	31	16	12	15
1	0	35	37	17	13	10
1	1	36	35	18	14	11
1	0	30	27	18	11	12
1	1	39	40	12	12	15
1	0	35	37	16	12	15
1	0	38	36	10	8	14
1	1	31	38	14	11	16
1	1	34	39	18	14	15
1	0	38	41	18	14	15
1	0	34	27	16	12	13
1	1	39	30	17	9	12
1	1	37	37	16	13	17
1	1	34	31	16	11	13
1	0	28	31	13	12	15
1	0	37	27	16	12	13
1	0	33	36	16	12	15
1	1	35	37	16	12	15
1	0	37	33	15	12	16
1	1	32	34	15	11	15
1	1	33	31	16	10	14
1	0	38	39	14	9	15
1	1	33	34	16	12	14
1	1	29	32	16	12	13
1	1	33	33	15	12	7
1	1	31	36	12	9	17
1	1	36	32	17	15	13
1	1	35	41	16	12	15
1	1	32	28	15	12	14
1	1	29	30	13	12	13
1	1	39	36	16	10	16
1	1	37	35	16	13	12
1	1	35	31	16	9	14
1	0	37	34	16	12	17
1	0	32	36	14	10	15
1	1	38	36	16	14	17
1	0	37	35	16	11	12
1	1	36	37	20	15	16
1	0	32	28	15	11	11
1	1	33	39	16	11	15
1	0	40	32	13	12	9
1	1	38	35	17	12	16
1	0	41	39	16	12	15
1	0	36	35	16	11	10
1	1	43	42	12	7	10
1	1	30	34	16	12	15
1	1	31	33	16	14	11
1	1	32	41	17	11	13
1	1	37	34	12	10	18
1	0	37	32	18	13	16
1	1	33	40	14	13	14
1	1	34	40	14	8	14
1	1	33	35	13	11	14
1	1	38	36	16	12	14
1	0	33	37	13	11	12
1	1	31	27	16	13	14
1	1	38	39	13	12	15
1	1	37	38	16	14	15
1	1	36	31	15	13	15
1	1	31	33	16	15	13
1	0	39	32	15	10	17
1	1	44	39	17	11	17
1	1	33	36	15	9	19
1	1	35	33	12	11	15
1	0	32	33	16	10	13
1	0	28	32	10	11	9
1	1	40	37	16	8	15
1	0	27	30	12	11	15
1	0	37	38	14	12	15
1	1	32	29	15	12	16
1	0	28	22	13	9	11
1	0	34	35	15	11	14
1	1	30	35	11	10	11
1	1	35	34	12	8	15
1	0	31	35	11	9	13
1	1	32	34	16	8	15
1	0	30	37	15	9	16
1	1	30	35	17	15	14
1	0	31	23	16	11	15
1	1	40	31	10	8	16
1	1	32	27	18	13	16
1	0	36	36	13	12	11
1	0	32	31	16	12	12
1	0	35	32	13	9	9
1	1	38	39	10	7	16
1	1	42	37	15	13	13
1	0	34	38	16	9	16
1	1	35	39	16	6	12
1	1	38	34	14	8	9
1	1	33	31	10	8	13
1	1	32	37	13	6	14
1	1	33	36	15	9	19
1	1	34	32	16	11	13
1	1	32	38	12	8	12
0	0	27	26	13	10	10
0	0	31	26	12	8	14
0	0	38	33	17	14	16
0	1	34	39	15	10	10
0	0	24	30	10	8	11
0	0	30	33	14	11	14
0	1	26	25	11	12	12
0	1	34	38	13	12	9
0	0	27	37	16	12	9
0	0	37	31	12	5	11
0	1	36	37	16	12	16
0	0	41	35	12	10	9
0	1	29	25	9	7	13
0	1	36	28	12	12	16
0	0	32	35	15	11	13
0	1	37	33	12	8	9
0	0	30	30	12	9	12
0	1	31	31	14	10	16
0	1	38	37	12	9	11
0	1	36	36	16	12	14
0	0	35	30	11	6	13
0	0	31	36	19	15	15
0	0	38	32	15	12	14
0	1	22	28	8	12	16
0	1	32	36	16	12	13
0	0	36	34	17	11	14
0	1	39	31	12	7	15
0	0	28	28	11	7	13
0	0	32	36	11	5	11
0	1	32	36	14	12	11
0	1	38	40	16	12	14
0	1	32	33	12	3	15
0	1	35	37	16	11	11
0	1	32	32	13	10	15
0	0	37	38	15	12	12
0	1	34	31	16	9	14
0	1	33	37	16	12	14
0	0	33	33	14	9	8
0	0	30	30	16	12	9
0	0	24	30	14	10	15
0	0	34	31	11	9	17
0	0	34	32	12	12	13
0	1	33	34	15	8	15
0	1	34	36	15	11	15
0	1	35	37	16	11	14
0	0	35	36	16	12	16
0	0	36	33	11	10	13
0	0	34	33	15	10	16
0	1	34	33	12	12	9
0	0	41	44	12	12	16
0	0	32	39	15	11	11
0	0	30	32	15	8	10
0	1	35	35	16	12	11
0	0	28	25	14	10	15
0	1	33	35	17	11	17
0	1	39	34	14	10	14
0	0	36	35	13	8	8
0	1	36	39	15	12	15
0	0	35	33	13	12	11
0	0	38	36	14	10	16
0	1	33	32	15	12	10
0	0	31	32	12	9	15
0	1	32	36	8	6	16
0	0	31	32	14	10	19
0	0	33	34	14	9	12
0	0	34	33	11	9	8
0	0	34	35	12	9	11
0	1	34	30	13	6	14
0	0	33	38	10	10	9
0	0	32	34	16	6	15
0	1	41	33	18	14	13
0	1	34	32	13	10	16
0	0	36	31	11	10	11
0	0	37	30	4	6	12
0	0	36	27	13	12	13
0	1	29	31	16	12	10
0	0	37	30	10	7	11
0	0	27	32	12	8	12
0	0	35	35	12	11	8
0	0	28	28	10	3	12
0	0	35	33	13	6	12
0	0	29	35	12	8	11
0	0	32	35	14	9	13
0	1	36	32	10	9	14
0	1	19	21	12	8	10
0	1	21	20	12	9	12
0	0	31	34	11	7	15
0	0	33	32	10	7	13
0	1	36	34	12	6	13
0	1	33	32	16	9	13
0	0	37	33	12	10	12
0	0	34	33	14	11	12
0	0	35	37	16	12	9
0	1	31	32	14	8	9
0	1	37	34	13	11	15
0	1	35	30	4	3	10
0	1	27	30	15	11	14
0	0	34	38	11	12	15
0	0	40	36	11	7	7
0	0	29	32	14	9	14
0	0	38	34	15	12	8
0	1	34	33	14	8	10
0	0	21	27	13	11	13
0	0	36	32	11	8	13
0	1	38	34	15	10	13
0	0	30	29	11	8	8
0	0	35	35	13	7	12
0	1	30	27	13	8	13
0	1	36	33	16	10	12
0	0	34	38	13	8	10
0	1	35	36	16	12	13
0	0	34	33	16	14	12
0	0	32	39	12	7	9
0	1	33	29	7	6	15
0	0	33	32	16	11	13
0	1	26	34	5	4	13
0	0	35	38	16	9	13
0	0	21	17	4	5	15
0	0	38	35	12	9	15
0	0	35	32	15	11	14
0	1	33	34	14	12	15
0	0	37	36	11	9	11
0	0	38	31	16	12	15
0	1	34	35	15	10	14
0	0	27	29	12	9	13
0	1	16	22	6	6	12
0	0	40	41	16	10	16
0	0	36	36	10	9	16
0	1	42	42	15	13	9
0	1	30	33	14	12	14




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198506&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198506&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198506&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'Herman Ole Andreas Wold' @ wold.wessa.net







Goodness of Fit
Correlation0.5474
R-squared0.2997
RMSE3.3906

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.5474 \tabularnewline
R-squared & 0.2997 \tabularnewline
RMSE & 3.3906 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198506&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.5474[/C][/ROW]
[ROW][C]R-squared[/C][C]0.2997[/C][/ROW]
[ROW][C]RMSE[/C][C]3.3906[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198506&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198506&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
Correlation0.5474
R-squared0.2997
RMSE3.3906







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
14136.44.6
23934.33673469387764.66326530612245
33034.3367346938776-4.33673469387755
43134.3367346938776-3.33673469387755
53434.3367346938776-0.336734693877553
63531.21052631578953.78947368421053
73934.33673469387764.66326530612245
83434.3367346938776-0.336734693877553
93634.33673469387761.66326530612245
103736.40.600000000000001
113834.33673469387763.66326530612245
123634.33673469387761.66326530612245
133834.33673469387763.66326530612245
143936.42.6
153334.3367346938776-1.33673469387755
163234.3367346938776-2.33673469387755
173634.33673469387761.66326530612245
183836.41.6
193936.42.6
203234.3367346938776-2.33673469387755
213234.3367346938776-2.33673469387755
223134.3367346938776-3.33673469387755
233936.42.6
243736.40.600000000000001
253934.33673469387764.66326530612245
264134.33673469387766.66326530612245
273634.33673469387761.66326530612245
283334.3367346938776-1.33673469387755
293334.3367346938776-1.33673469387755
303434.3367346938776-0.336734693877553
313134.3367346938776-3.33673469387755
322734.3367346938776-7.33673469387755
333734.33673469387762.66326530612245
343434.3367346938776-0.336734693877553
353431.21052631578952.78947368421053
363234.3367346938776-2.33673469387755
372934.3367346938776-5.33673469387755
383634.33673469387761.66326530612245
392934.3367346938776-5.33673469387755
403534.33673469387760.663265306122447
413734.33673469387762.66326530612245
423434.3367346938776-0.336734693877553
433834.33673469387763.66326530612245
443534.33673469387760.663265306122447
453831.21052631578956.78947368421053
463734.33673469387762.66326530612245
473836.41.6
483334.3367346938776-1.33673469387755
493636.4-0.399999999999999
503834.33673469387763.66326530612245
513236.4-4.4
523231.21052631578950.789473684210527
533234.3367346938776-2.33673469387755
543436.4-2.4
553234.3367346938776-2.33673469387755
563734.33673469387762.66326530612245
573934.33673469387764.66326530612245
582934.3367346938776-5.33673469387755
593734.33673469387762.66326530612245
603534.33673469387760.663265306122447
613031.2105263157895-1.21052631578947
623834.33673469387763.66326530612245
633434.3367346938776-0.336734693877553
643134.3367346938776-3.33673469387755
653434.3367346938776-0.336734693877553
663534.33673469387760.663265306122447
673634.33673469387761.66326530612245
683031.2105263157895-1.21052631578947
693936.42.6
703534.33673469387760.663265306122447
713834.33673469387763.66326530612245
723136.4-5.4
733436.4-2.4
743836.41.6
753431.21052631578952.78947368421053
763931.21052631578957.78947368421053
773734.33673469387762.66326530612245
783434.3367346938776-0.336734693877553
792834.3367346938776-6.33673469387755
803731.21052631578955.78947368421053
813334.3367346938776-1.33673469387755
823534.33673469387760.663265306122447
833734.33673469387762.66326530612245
843234.3367346938776-2.33673469387755
853334.3367346938776-1.33673469387755
863836.41.6
873334.3367346938776-1.33673469387755
882934.3367346938776-5.33673469387755
893334.3367346938776-1.33673469387755
903134.3367346938776-3.33673469387755
913634.33673469387761.66326530612245
923536.4-1.4
933231.21052631578950.789473684210527
942931.2105263157895-2.21052631578947
953934.33673469387764.66326530612245
963734.33673469387762.66326530612245
973534.33673469387760.663265306122447
983734.33673469387762.66326530612245
993234.3367346938776-2.33673469387755
1003834.33673469387763.66326530612245
1013734.33673469387762.66326530612245
1023634.33673469387761.66326530612245
1033231.21052631578950.789473684210527
1043336.4-3.4
1054034.33673469387765.66326530612245
1063834.33673469387763.66326530612245
1074136.44.6
1083634.33673469387761.66326530612245
1094336.46.6
1103034.3367346938776-4.33673469387755
1113134.3367346938776-3.33673469387755
1123236.4-4.4
1133734.33673469387762.66326530612245
1143734.33673469387762.66326530612245
1153336.4-3.4
1163436.4-2.4
1173334.3367346938776-1.33673469387755
1183834.33673469387763.66326530612245
1193334.3367346938776-1.33673469387755
1203131.2105263157895-0.210526315789473
1213836.41.6
1223736.40.600000000000001
1233634.33673469387761.66326530612245
1243134.3367346938776-3.33673469387755
1253934.33673469387764.66326530612245
1264436.47.6
1273334.3367346938776-1.33673469387755
1283534.33673469387760.663265306122447
1293234.3367346938776-2.33673469387755
1302834.3367346938776-6.33673469387755
1314034.33673469387765.66326530612245
1322731.2105263157895-4.21052631578947
1333736.40.600000000000001
1343231.21052631578950.789473684210527
1352824.33333333333333.66666666666667
1363434.3367346938776-0.336734693877553
1373034.3367346938776-4.33673469387755
1383534.33673469387760.663265306122447
1393134.3367346938776-3.33673469387755
1403234.3367346938776-2.33673469387755
1413034.3367346938776-4.33673469387755
1423034.3367346938776-4.33673469387755
1433124.33333333333336.66666666666667
1444034.33673469387765.66326530612245
1453231.21052631578950.789473684210527
1463634.33673469387761.66326530612245
1473234.3367346938776-2.33673469387755
1483534.33673469387760.663265306122447
1493836.41.6
1504234.33673469387767.66326530612245
1513436.4-2.4
1523536.4-1.4
1533834.33673469387763.66326530612245
1543334.3367346938776-1.33673469387755
1553234.3367346938776-2.33673469387755
1563334.3367346938776-1.33673469387755
1573434.3367346938776-0.336734693877553
1583236.4-4.4
1592731.2105263157895-4.21052631578947
1603131.2105263157895-0.210526315789473
1613834.33673469387763.66326530612245
1623436.4-2.4
1632431.2105263157895-7.21052631578947
1643034.3367346938776-4.33673469387755
1652624.33333333333331.66666666666667
1663436.4-2.4
1672734.3367346938776-7.33673469387755
1683734.33673469387762.66326530612245
1693634.33673469387761.66326530612245
1704134.33673469387766.66326530612245
1712924.33333333333334.66666666666667
1723631.21052631578954.78947368421053
1733234.3367346938776-2.33673469387755
1743734.33673469387762.66326530612245
1753031.2105263157895-1.21052631578947
1763134.3367346938776-3.33673469387755
1773834.33673469387763.66326530612245
1783634.33673469387761.66326530612245
1793531.21052631578953.78947368421053
1803134.3367346938776-3.33673469387755
1813834.33673469387763.66326530612245
1822231.2105263157895-9.21052631578947
1833234.3367346938776-2.33673469387755
1843634.33673469387761.66326530612245
1853934.33673469387764.66326530612245
1862831.2105263157895-3.21052631578947
1873234.3367346938776-2.33673469387755
1883234.3367346938776-2.33673469387755
1893836.41.6
1903234.3367346938776-2.33673469387755
1913534.33673469387760.663265306122447
1923234.3367346938776-2.33673469387755
1933736.40.600000000000001
1943434.3367346938776-0.336734693877553
1953334.3367346938776-1.33673469387755
1963334.3367346938776-1.33673469387755
1973031.2105263157895-1.21052631578947
1982431.2105263157895-7.21052631578947
1993434.3367346938776-0.336734693877553
2003434.3367346938776-0.336734693877553
2013334.3367346938776-1.33673469387755
2023434.3367346938776-0.336734693877553
2033534.33673469387760.663265306122447
2043534.33673469387760.663265306122447
2053634.33673469387761.66326530612245
2063434.3367346938776-0.336734693877553
2073434.3367346938776-0.336734693877553
2084136.44.6
2093236.4-4.4
2103034.3367346938776-4.33673469387755
2113534.33673469387760.663265306122447
2122824.33333333333333.66666666666667
2133334.3367346938776-1.33673469387755
2143934.33673469387764.66326530612245
2153634.33673469387761.66326530612245
2163636.4-0.399999999999999
2173534.33673469387760.663265306122447
2183834.33673469387763.66326530612245
2193334.3367346938776-1.33673469387755
2203134.3367346938776-3.33673469387755
2213234.3367346938776-2.33673469387755
2223134.3367346938776-3.33673469387755
2233334.3367346938776-1.33673469387755
2243434.3367346938776-0.336734693877553
2253434.3367346938776-0.336734693877553
2263431.21052631578952.78947368421053
2273336.4-3.4
2283234.3367346938776-2.33673469387755
2294134.33673469387766.66326530612245
2303434.3367346938776-0.336734693877553
2313634.33673469387761.66326530612245
2323731.21052631578955.78947368421053
2333631.21052631578954.78947368421053
2342934.3367346938776-5.33673469387755
2353731.21052631578955.78947368421053
2362734.3367346938776-7.33673469387755
2373534.33673469387760.663265306122447
2382831.2105263157895-3.21052631578947
2393534.33673469387760.663265306122447
2402934.3367346938776-5.33673469387755
2413234.3367346938776-2.33673469387755
2423634.33673469387761.66326530612245
2431924.3333333333333-5.33333333333333
2442124.3333333333333-3.33333333333333
2453134.3367346938776-3.33673469387755
2463334.3367346938776-1.33673469387755
2473634.33673469387761.66326530612245
2483334.3367346938776-1.33673469387755
2493734.33673469387762.66326530612245
2503434.3367346938776-0.336734693877553
2513534.33673469387760.663265306122447
2523134.3367346938776-3.33673469387755
2533734.33673469387762.66326530612245
2543531.21052631578953.78947368421053
2552731.2105263157895-4.21052631578947
2563436.4-2.4
2574034.33673469387765.66326530612245
2582934.3367346938776-5.33673469387755
2593834.33673469387763.66326530612245
2603434.3367346938776-0.336734693877553
2612131.2105263157895-10.2105263157895
2623634.33673469387761.66326530612245
2633834.33673469387763.66326530612245
2643031.2105263157895-1.21052631578947
2653534.33673469387760.663265306122447
2663031.2105263157895-1.21052631578947
2673634.33673469387761.66326530612245
2683436.4-2.4
2693534.33673469387760.663265306122447
2703434.3367346938776-0.336734693877553
2713236.4-4.4
2723331.21052631578951.78947368421053
2733334.3367346938776-1.33673469387755
2742634.3367346938776-8.33673469387755
2753536.4-1.4
2762124.3333333333333-3.33333333333333
2773834.33673469387763.66326530612245
2783534.33673469387760.663265306122447
2793334.3367346938776-1.33673469387755
2803734.33673469387762.66326530612245
2813834.33673469387763.66326530612245
2823434.3367346938776-0.336734693877553
2832731.2105263157895-4.21052631578947
2841624.3333333333333-8.33333333333333
2854036.43.6
2863634.33673469387761.66326530612245
2874236.45.6
2883034.3367346938776-4.33673469387755

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 41 & 36.4 & 4.6 \tabularnewline
2 & 39 & 34.3367346938776 & 4.66326530612245 \tabularnewline
3 & 30 & 34.3367346938776 & -4.33673469387755 \tabularnewline
4 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
5 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
6 & 35 & 31.2105263157895 & 3.78947368421053 \tabularnewline
7 & 39 & 34.3367346938776 & 4.66326530612245 \tabularnewline
8 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
9 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
10 & 37 & 36.4 & 0.600000000000001 \tabularnewline
11 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
12 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
13 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
14 & 39 & 36.4 & 2.6 \tabularnewline
15 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
16 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
17 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
18 & 38 & 36.4 & 1.6 \tabularnewline
19 & 39 & 36.4 & 2.6 \tabularnewline
20 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
21 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
22 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
23 & 39 & 36.4 & 2.6 \tabularnewline
24 & 37 & 36.4 & 0.600000000000001 \tabularnewline
25 & 39 & 34.3367346938776 & 4.66326530612245 \tabularnewline
26 & 41 & 34.3367346938776 & 6.66326530612245 \tabularnewline
27 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
28 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
29 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
30 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
31 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
32 & 27 & 34.3367346938776 & -7.33673469387755 \tabularnewline
33 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
34 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
35 & 34 & 31.2105263157895 & 2.78947368421053 \tabularnewline
36 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
37 & 29 & 34.3367346938776 & -5.33673469387755 \tabularnewline
38 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
39 & 29 & 34.3367346938776 & -5.33673469387755 \tabularnewline
40 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
41 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
42 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
43 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
44 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
45 & 38 & 31.2105263157895 & 6.78947368421053 \tabularnewline
46 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
47 & 38 & 36.4 & 1.6 \tabularnewline
48 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
49 & 36 & 36.4 & -0.399999999999999 \tabularnewline
50 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
51 & 32 & 36.4 & -4.4 \tabularnewline
52 & 32 & 31.2105263157895 & 0.789473684210527 \tabularnewline
53 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
54 & 34 & 36.4 & -2.4 \tabularnewline
55 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
56 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
57 & 39 & 34.3367346938776 & 4.66326530612245 \tabularnewline
58 & 29 & 34.3367346938776 & -5.33673469387755 \tabularnewline
59 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
60 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
61 & 30 & 31.2105263157895 & -1.21052631578947 \tabularnewline
62 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
63 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
64 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
65 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
66 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
67 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
68 & 30 & 31.2105263157895 & -1.21052631578947 \tabularnewline
69 & 39 & 36.4 & 2.6 \tabularnewline
70 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
71 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
72 & 31 & 36.4 & -5.4 \tabularnewline
73 & 34 & 36.4 & -2.4 \tabularnewline
74 & 38 & 36.4 & 1.6 \tabularnewline
75 & 34 & 31.2105263157895 & 2.78947368421053 \tabularnewline
76 & 39 & 31.2105263157895 & 7.78947368421053 \tabularnewline
77 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
78 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
79 & 28 & 34.3367346938776 & -6.33673469387755 \tabularnewline
80 & 37 & 31.2105263157895 & 5.78947368421053 \tabularnewline
81 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
82 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
83 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
84 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
85 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
86 & 38 & 36.4 & 1.6 \tabularnewline
87 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
88 & 29 & 34.3367346938776 & -5.33673469387755 \tabularnewline
89 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
90 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
91 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
92 & 35 & 36.4 & -1.4 \tabularnewline
93 & 32 & 31.2105263157895 & 0.789473684210527 \tabularnewline
94 & 29 & 31.2105263157895 & -2.21052631578947 \tabularnewline
95 & 39 & 34.3367346938776 & 4.66326530612245 \tabularnewline
96 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
97 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
98 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
99 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
100 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
101 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
102 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
103 & 32 & 31.2105263157895 & 0.789473684210527 \tabularnewline
104 & 33 & 36.4 & -3.4 \tabularnewline
105 & 40 & 34.3367346938776 & 5.66326530612245 \tabularnewline
106 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
107 & 41 & 36.4 & 4.6 \tabularnewline
108 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
109 & 43 & 36.4 & 6.6 \tabularnewline
110 & 30 & 34.3367346938776 & -4.33673469387755 \tabularnewline
111 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
112 & 32 & 36.4 & -4.4 \tabularnewline
113 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
114 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
115 & 33 & 36.4 & -3.4 \tabularnewline
116 & 34 & 36.4 & -2.4 \tabularnewline
117 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
118 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
119 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
120 & 31 & 31.2105263157895 & -0.210526315789473 \tabularnewline
121 & 38 & 36.4 & 1.6 \tabularnewline
122 & 37 & 36.4 & 0.600000000000001 \tabularnewline
123 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
124 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
125 & 39 & 34.3367346938776 & 4.66326530612245 \tabularnewline
126 & 44 & 36.4 & 7.6 \tabularnewline
127 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
128 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
129 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
130 & 28 & 34.3367346938776 & -6.33673469387755 \tabularnewline
131 & 40 & 34.3367346938776 & 5.66326530612245 \tabularnewline
132 & 27 & 31.2105263157895 & -4.21052631578947 \tabularnewline
133 & 37 & 36.4 & 0.600000000000001 \tabularnewline
134 & 32 & 31.2105263157895 & 0.789473684210527 \tabularnewline
135 & 28 & 24.3333333333333 & 3.66666666666667 \tabularnewline
136 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
137 & 30 & 34.3367346938776 & -4.33673469387755 \tabularnewline
138 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
139 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
140 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
141 & 30 & 34.3367346938776 & -4.33673469387755 \tabularnewline
142 & 30 & 34.3367346938776 & -4.33673469387755 \tabularnewline
143 & 31 & 24.3333333333333 & 6.66666666666667 \tabularnewline
144 & 40 & 34.3367346938776 & 5.66326530612245 \tabularnewline
145 & 32 & 31.2105263157895 & 0.789473684210527 \tabularnewline
146 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
147 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
148 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
149 & 38 & 36.4 & 1.6 \tabularnewline
150 & 42 & 34.3367346938776 & 7.66326530612245 \tabularnewline
151 & 34 & 36.4 & -2.4 \tabularnewline
152 & 35 & 36.4 & -1.4 \tabularnewline
153 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
154 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
155 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
156 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
157 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
158 & 32 & 36.4 & -4.4 \tabularnewline
159 & 27 & 31.2105263157895 & -4.21052631578947 \tabularnewline
160 & 31 & 31.2105263157895 & -0.210526315789473 \tabularnewline
161 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
162 & 34 & 36.4 & -2.4 \tabularnewline
163 & 24 & 31.2105263157895 & -7.21052631578947 \tabularnewline
164 & 30 & 34.3367346938776 & -4.33673469387755 \tabularnewline
165 & 26 & 24.3333333333333 & 1.66666666666667 \tabularnewline
166 & 34 & 36.4 & -2.4 \tabularnewline
167 & 27 & 34.3367346938776 & -7.33673469387755 \tabularnewline
168 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
169 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
170 & 41 & 34.3367346938776 & 6.66326530612245 \tabularnewline
171 & 29 & 24.3333333333333 & 4.66666666666667 \tabularnewline
172 & 36 & 31.2105263157895 & 4.78947368421053 \tabularnewline
173 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
174 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
175 & 30 & 31.2105263157895 & -1.21052631578947 \tabularnewline
176 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
177 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
178 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
179 & 35 & 31.2105263157895 & 3.78947368421053 \tabularnewline
180 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
181 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
182 & 22 & 31.2105263157895 & -9.21052631578947 \tabularnewline
183 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
184 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
185 & 39 & 34.3367346938776 & 4.66326530612245 \tabularnewline
186 & 28 & 31.2105263157895 & -3.21052631578947 \tabularnewline
187 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
188 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
189 & 38 & 36.4 & 1.6 \tabularnewline
190 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
191 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
192 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
193 & 37 & 36.4 & 0.600000000000001 \tabularnewline
194 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
195 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
196 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
197 & 30 & 31.2105263157895 & -1.21052631578947 \tabularnewline
198 & 24 & 31.2105263157895 & -7.21052631578947 \tabularnewline
199 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
200 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
201 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
202 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
203 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
204 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
205 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
206 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
207 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
208 & 41 & 36.4 & 4.6 \tabularnewline
209 & 32 & 36.4 & -4.4 \tabularnewline
210 & 30 & 34.3367346938776 & -4.33673469387755 \tabularnewline
211 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
212 & 28 & 24.3333333333333 & 3.66666666666667 \tabularnewline
213 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
214 & 39 & 34.3367346938776 & 4.66326530612245 \tabularnewline
215 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
216 & 36 & 36.4 & -0.399999999999999 \tabularnewline
217 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
218 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
219 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
220 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
221 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
222 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
223 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
224 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
225 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
226 & 34 & 31.2105263157895 & 2.78947368421053 \tabularnewline
227 & 33 & 36.4 & -3.4 \tabularnewline
228 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
229 & 41 & 34.3367346938776 & 6.66326530612245 \tabularnewline
230 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
231 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
232 & 37 & 31.2105263157895 & 5.78947368421053 \tabularnewline
233 & 36 & 31.2105263157895 & 4.78947368421053 \tabularnewline
234 & 29 & 34.3367346938776 & -5.33673469387755 \tabularnewline
235 & 37 & 31.2105263157895 & 5.78947368421053 \tabularnewline
236 & 27 & 34.3367346938776 & -7.33673469387755 \tabularnewline
237 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
238 & 28 & 31.2105263157895 & -3.21052631578947 \tabularnewline
239 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
240 & 29 & 34.3367346938776 & -5.33673469387755 \tabularnewline
241 & 32 & 34.3367346938776 & -2.33673469387755 \tabularnewline
242 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
243 & 19 & 24.3333333333333 & -5.33333333333333 \tabularnewline
244 & 21 & 24.3333333333333 & -3.33333333333333 \tabularnewline
245 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
246 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
247 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
248 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
249 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
250 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
251 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
252 & 31 & 34.3367346938776 & -3.33673469387755 \tabularnewline
253 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
254 & 35 & 31.2105263157895 & 3.78947368421053 \tabularnewline
255 & 27 & 31.2105263157895 & -4.21052631578947 \tabularnewline
256 & 34 & 36.4 & -2.4 \tabularnewline
257 & 40 & 34.3367346938776 & 5.66326530612245 \tabularnewline
258 & 29 & 34.3367346938776 & -5.33673469387755 \tabularnewline
259 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
260 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
261 & 21 & 31.2105263157895 & -10.2105263157895 \tabularnewline
262 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
263 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
264 & 30 & 31.2105263157895 & -1.21052631578947 \tabularnewline
265 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
266 & 30 & 31.2105263157895 & -1.21052631578947 \tabularnewline
267 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
268 & 34 & 36.4 & -2.4 \tabularnewline
269 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
270 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
271 & 32 & 36.4 & -4.4 \tabularnewline
272 & 33 & 31.2105263157895 & 1.78947368421053 \tabularnewline
273 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
274 & 26 & 34.3367346938776 & -8.33673469387755 \tabularnewline
275 & 35 & 36.4 & -1.4 \tabularnewline
276 & 21 & 24.3333333333333 & -3.33333333333333 \tabularnewline
277 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
278 & 35 & 34.3367346938776 & 0.663265306122447 \tabularnewline
279 & 33 & 34.3367346938776 & -1.33673469387755 \tabularnewline
280 & 37 & 34.3367346938776 & 2.66326530612245 \tabularnewline
281 & 38 & 34.3367346938776 & 3.66326530612245 \tabularnewline
282 & 34 & 34.3367346938776 & -0.336734693877553 \tabularnewline
283 & 27 & 31.2105263157895 & -4.21052631578947 \tabularnewline
284 & 16 & 24.3333333333333 & -8.33333333333333 \tabularnewline
285 & 40 & 36.4 & 3.6 \tabularnewline
286 & 36 & 34.3367346938776 & 1.66326530612245 \tabularnewline
287 & 42 & 36.4 & 5.6 \tabularnewline
288 & 30 & 34.3367346938776 & -4.33673469387755 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198506&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]41[/C][C]36.4[/C][C]4.6[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.3367346938776[/C][C]4.66326530612245[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]34.3367346938776[/C][C]-4.33673469387755[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]31.2105263157895[/C][C]3.78947368421053[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.3367346938776[/C][C]4.66326530612245[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.4[/C][C]0.600000000000001[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]36.4[/C][C]2.6[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]36.4[/C][C]1.6[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.4[/C][C]2.6[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.4[/C][C]2.6[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.4[/C][C]0.600000000000001[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]34.3367346938776[/C][C]4.66326530612245[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.3367346938776[/C][C]6.66326530612245[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]34.3367346938776[/C][C]-7.33673469387755[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]31.2105263157895[/C][C]2.78947368421053[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]34.3367346938776[/C][C]-5.33673469387755[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]34.3367346938776[/C][C]-5.33673469387755[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]31.2105263157895[/C][C]6.78947368421053[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]36.4[/C][C]1.6[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.4[/C][C]-0.399999999999999[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.4[/C][C]-4.4[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]31.2105263157895[/C][C]0.789473684210527[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]36.4[/C][C]-2.4[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.3367346938776[/C][C]4.66326530612245[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.3367346938776[/C][C]-5.33673469387755[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.2105263157895[/C][C]-1.21052631578947[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]31.2105263157895[/C][C]-1.21052631578947[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]36.4[/C][C]2.6[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]36.4[/C][C]-5.4[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.4[/C][C]-2.4[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]36.4[/C][C]1.6[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]31.2105263157895[/C][C]2.78947368421053[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]31.2105263157895[/C][C]7.78947368421053[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]34.3367346938776[/C][C]-6.33673469387755[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]31.2105263157895[/C][C]5.78947368421053[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]82[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]83[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]84[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]85[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]86[/C][C]38[/C][C]36.4[/C][C]1.6[/C][/ROW]
[ROW][C]87[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]88[/C][C]29[/C][C]34.3367346938776[/C][C]-5.33673469387755[/C][/ROW]
[ROW][C]89[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]90[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]91[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]92[/C][C]35[/C][C]36.4[/C][C]-1.4[/C][/ROW]
[ROW][C]93[/C][C]32[/C][C]31.2105263157895[/C][C]0.789473684210527[/C][/ROW]
[ROW][C]94[/C][C]29[/C][C]31.2105263157895[/C][C]-2.21052631578947[/C][/ROW]
[ROW][C]95[/C][C]39[/C][C]34.3367346938776[/C][C]4.66326530612245[/C][/ROW]
[ROW][C]96[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]97[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]98[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]99[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]100[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]101[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]102[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]103[/C][C]32[/C][C]31.2105263157895[/C][C]0.789473684210527[/C][/ROW]
[ROW][C]104[/C][C]33[/C][C]36.4[/C][C]-3.4[/C][/ROW]
[ROW][C]105[/C][C]40[/C][C]34.3367346938776[/C][C]5.66326530612245[/C][/ROW]
[ROW][C]106[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]107[/C][C]41[/C][C]36.4[/C][C]4.6[/C][/ROW]
[ROW][C]108[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]109[/C][C]43[/C][C]36.4[/C][C]6.6[/C][/ROW]
[ROW][C]110[/C][C]30[/C][C]34.3367346938776[/C][C]-4.33673469387755[/C][/ROW]
[ROW][C]111[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]112[/C][C]32[/C][C]36.4[/C][C]-4.4[/C][/ROW]
[ROW][C]113[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]114[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]115[/C][C]33[/C][C]36.4[/C][C]-3.4[/C][/ROW]
[ROW][C]116[/C][C]34[/C][C]36.4[/C][C]-2.4[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]118[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]120[/C][C]31[/C][C]31.2105263157895[/C][C]-0.210526315789473[/C][/ROW]
[ROW][C]121[/C][C]38[/C][C]36.4[/C][C]1.6[/C][/ROW]
[ROW][C]122[/C][C]37[/C][C]36.4[/C][C]0.600000000000001[/C][/ROW]
[ROW][C]123[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]124[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]125[/C][C]39[/C][C]34.3367346938776[/C][C]4.66326530612245[/C][/ROW]
[ROW][C]126[/C][C]44[/C][C]36.4[/C][C]7.6[/C][/ROW]
[ROW][C]127[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]128[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]129[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]130[/C][C]28[/C][C]34.3367346938776[/C][C]-6.33673469387755[/C][/ROW]
[ROW][C]131[/C][C]40[/C][C]34.3367346938776[/C][C]5.66326530612245[/C][/ROW]
[ROW][C]132[/C][C]27[/C][C]31.2105263157895[/C][C]-4.21052631578947[/C][/ROW]
[ROW][C]133[/C][C]37[/C][C]36.4[/C][C]0.600000000000001[/C][/ROW]
[ROW][C]134[/C][C]32[/C][C]31.2105263157895[/C][C]0.789473684210527[/C][/ROW]
[ROW][C]135[/C][C]28[/C][C]24.3333333333333[/C][C]3.66666666666667[/C][/ROW]
[ROW][C]136[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]137[/C][C]30[/C][C]34.3367346938776[/C][C]-4.33673469387755[/C][/ROW]
[ROW][C]138[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]139[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]140[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]34.3367346938776[/C][C]-4.33673469387755[/C][/ROW]
[ROW][C]142[/C][C]30[/C][C]34.3367346938776[/C][C]-4.33673469387755[/C][/ROW]
[ROW][C]143[/C][C]31[/C][C]24.3333333333333[/C][C]6.66666666666667[/C][/ROW]
[ROW][C]144[/C][C]40[/C][C]34.3367346938776[/C][C]5.66326530612245[/C][/ROW]
[ROW][C]145[/C][C]32[/C][C]31.2105263157895[/C][C]0.789473684210527[/C][/ROW]
[ROW][C]146[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]148[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]149[/C][C]38[/C][C]36.4[/C][C]1.6[/C][/ROW]
[ROW][C]150[/C][C]42[/C][C]34.3367346938776[/C][C]7.66326530612245[/C][/ROW]
[ROW][C]151[/C][C]34[/C][C]36.4[/C][C]-2.4[/C][/ROW]
[ROW][C]152[/C][C]35[/C][C]36.4[/C][C]-1.4[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]154[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]155[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]157[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]36.4[/C][C]-4.4[/C][/ROW]
[ROW][C]159[/C][C]27[/C][C]31.2105263157895[/C][C]-4.21052631578947[/C][/ROW]
[ROW][C]160[/C][C]31[/C][C]31.2105263157895[/C][C]-0.210526315789473[/C][/ROW]
[ROW][C]161[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]36.4[/C][C]-2.4[/C][/ROW]
[ROW][C]163[/C][C]24[/C][C]31.2105263157895[/C][C]-7.21052631578947[/C][/ROW]
[ROW][C]164[/C][C]30[/C][C]34.3367346938776[/C][C]-4.33673469387755[/C][/ROW]
[ROW][C]165[/C][C]26[/C][C]24.3333333333333[/C][C]1.66666666666667[/C][/ROW]
[ROW][C]166[/C][C]34[/C][C]36.4[/C][C]-2.4[/C][/ROW]
[ROW][C]167[/C][C]27[/C][C]34.3367346938776[/C][C]-7.33673469387755[/C][/ROW]
[ROW][C]168[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]169[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]170[/C][C]41[/C][C]34.3367346938776[/C][C]6.66326530612245[/C][/ROW]
[ROW][C]171[/C][C]29[/C][C]24.3333333333333[/C][C]4.66666666666667[/C][/ROW]
[ROW][C]172[/C][C]36[/C][C]31.2105263157895[/C][C]4.78947368421053[/C][/ROW]
[ROW][C]173[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]174[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]175[/C][C]30[/C][C]31.2105263157895[/C][C]-1.21052631578947[/C][/ROW]
[ROW][C]176[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]177[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]178[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]179[/C][C]35[/C][C]31.2105263157895[/C][C]3.78947368421053[/C][/ROW]
[ROW][C]180[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]181[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]182[/C][C]22[/C][C]31.2105263157895[/C][C]-9.21052631578947[/C][/ROW]
[ROW][C]183[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]184[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]185[/C][C]39[/C][C]34.3367346938776[/C][C]4.66326530612245[/C][/ROW]
[ROW][C]186[/C][C]28[/C][C]31.2105263157895[/C][C]-3.21052631578947[/C][/ROW]
[ROW][C]187[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]188[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]189[/C][C]38[/C][C]36.4[/C][C]1.6[/C][/ROW]
[ROW][C]190[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]191[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]192[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]193[/C][C]37[/C][C]36.4[/C][C]0.600000000000001[/C][/ROW]
[ROW][C]194[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]195[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]196[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]197[/C][C]30[/C][C]31.2105263157895[/C][C]-1.21052631578947[/C][/ROW]
[ROW][C]198[/C][C]24[/C][C]31.2105263157895[/C][C]-7.21052631578947[/C][/ROW]
[ROW][C]199[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]200[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]201[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]202[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]203[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]204[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]205[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]206[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]207[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]208[/C][C]41[/C][C]36.4[/C][C]4.6[/C][/ROW]
[ROW][C]209[/C][C]32[/C][C]36.4[/C][C]-4.4[/C][/ROW]
[ROW][C]210[/C][C]30[/C][C]34.3367346938776[/C][C]-4.33673469387755[/C][/ROW]
[ROW][C]211[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]212[/C][C]28[/C][C]24.3333333333333[/C][C]3.66666666666667[/C][/ROW]
[ROW][C]213[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]214[/C][C]39[/C][C]34.3367346938776[/C][C]4.66326530612245[/C][/ROW]
[ROW][C]215[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]216[/C][C]36[/C][C]36.4[/C][C]-0.399999999999999[/C][/ROW]
[ROW][C]217[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]218[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]219[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]220[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]221[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]222[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]223[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]224[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]225[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]226[/C][C]34[/C][C]31.2105263157895[/C][C]2.78947368421053[/C][/ROW]
[ROW][C]227[/C][C]33[/C][C]36.4[/C][C]-3.4[/C][/ROW]
[ROW][C]228[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]229[/C][C]41[/C][C]34.3367346938776[/C][C]6.66326530612245[/C][/ROW]
[ROW][C]230[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]231[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]232[/C][C]37[/C][C]31.2105263157895[/C][C]5.78947368421053[/C][/ROW]
[ROW][C]233[/C][C]36[/C][C]31.2105263157895[/C][C]4.78947368421053[/C][/ROW]
[ROW][C]234[/C][C]29[/C][C]34.3367346938776[/C][C]-5.33673469387755[/C][/ROW]
[ROW][C]235[/C][C]37[/C][C]31.2105263157895[/C][C]5.78947368421053[/C][/ROW]
[ROW][C]236[/C][C]27[/C][C]34.3367346938776[/C][C]-7.33673469387755[/C][/ROW]
[ROW][C]237[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]238[/C][C]28[/C][C]31.2105263157895[/C][C]-3.21052631578947[/C][/ROW]
[ROW][C]239[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]240[/C][C]29[/C][C]34.3367346938776[/C][C]-5.33673469387755[/C][/ROW]
[ROW][C]241[/C][C]32[/C][C]34.3367346938776[/C][C]-2.33673469387755[/C][/ROW]
[ROW][C]242[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]243[/C][C]19[/C][C]24.3333333333333[/C][C]-5.33333333333333[/C][/ROW]
[ROW][C]244[/C][C]21[/C][C]24.3333333333333[/C][C]-3.33333333333333[/C][/ROW]
[ROW][C]245[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]246[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]247[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]248[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]249[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]250[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]251[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]252[/C][C]31[/C][C]34.3367346938776[/C][C]-3.33673469387755[/C][/ROW]
[ROW][C]253[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]254[/C][C]35[/C][C]31.2105263157895[/C][C]3.78947368421053[/C][/ROW]
[ROW][C]255[/C][C]27[/C][C]31.2105263157895[/C][C]-4.21052631578947[/C][/ROW]
[ROW][C]256[/C][C]34[/C][C]36.4[/C][C]-2.4[/C][/ROW]
[ROW][C]257[/C][C]40[/C][C]34.3367346938776[/C][C]5.66326530612245[/C][/ROW]
[ROW][C]258[/C][C]29[/C][C]34.3367346938776[/C][C]-5.33673469387755[/C][/ROW]
[ROW][C]259[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]260[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]261[/C][C]21[/C][C]31.2105263157895[/C][C]-10.2105263157895[/C][/ROW]
[ROW][C]262[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]263[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]264[/C][C]30[/C][C]31.2105263157895[/C][C]-1.21052631578947[/C][/ROW]
[ROW][C]265[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]266[/C][C]30[/C][C]31.2105263157895[/C][C]-1.21052631578947[/C][/ROW]
[ROW][C]267[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]268[/C][C]34[/C][C]36.4[/C][C]-2.4[/C][/ROW]
[ROW][C]269[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]270[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]271[/C][C]32[/C][C]36.4[/C][C]-4.4[/C][/ROW]
[ROW][C]272[/C][C]33[/C][C]31.2105263157895[/C][C]1.78947368421053[/C][/ROW]
[ROW][C]273[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]274[/C][C]26[/C][C]34.3367346938776[/C][C]-8.33673469387755[/C][/ROW]
[ROW][C]275[/C][C]35[/C][C]36.4[/C][C]-1.4[/C][/ROW]
[ROW][C]276[/C][C]21[/C][C]24.3333333333333[/C][C]-3.33333333333333[/C][/ROW]
[ROW][C]277[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]278[/C][C]35[/C][C]34.3367346938776[/C][C]0.663265306122447[/C][/ROW]
[ROW][C]279[/C][C]33[/C][C]34.3367346938776[/C][C]-1.33673469387755[/C][/ROW]
[ROW][C]280[/C][C]37[/C][C]34.3367346938776[/C][C]2.66326530612245[/C][/ROW]
[ROW][C]281[/C][C]38[/C][C]34.3367346938776[/C][C]3.66326530612245[/C][/ROW]
[ROW][C]282[/C][C]34[/C][C]34.3367346938776[/C][C]-0.336734693877553[/C][/ROW]
[ROW][C]283[/C][C]27[/C][C]31.2105263157895[/C][C]-4.21052631578947[/C][/ROW]
[ROW][C]284[/C][C]16[/C][C]24.3333333333333[/C][C]-8.33333333333333[/C][/ROW]
[ROW][C]285[/C][C]40[/C][C]36.4[/C][C]3.6[/C][/ROW]
[ROW][C]286[/C][C]36[/C][C]34.3367346938776[/C][C]1.66326530612245[/C][/ROW]
[ROW][C]287[/C][C]42[/C][C]36.4[/C][C]5.6[/C][/ROW]
[ROW][C]288[/C][C]30[/C][C]34.3367346938776[/C][C]-4.33673469387755[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198506&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198506&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
14136.44.6
23934.33673469387764.66326530612245
33034.3367346938776-4.33673469387755
43134.3367346938776-3.33673469387755
53434.3367346938776-0.336734693877553
63531.21052631578953.78947368421053
73934.33673469387764.66326530612245
83434.3367346938776-0.336734693877553
93634.33673469387761.66326530612245
103736.40.600000000000001
113834.33673469387763.66326530612245
123634.33673469387761.66326530612245
133834.33673469387763.66326530612245
143936.42.6
153334.3367346938776-1.33673469387755
163234.3367346938776-2.33673469387755
173634.33673469387761.66326530612245
183836.41.6
193936.42.6
203234.3367346938776-2.33673469387755
213234.3367346938776-2.33673469387755
223134.3367346938776-3.33673469387755
233936.42.6
243736.40.600000000000001
253934.33673469387764.66326530612245
264134.33673469387766.66326530612245
273634.33673469387761.66326530612245
283334.3367346938776-1.33673469387755
293334.3367346938776-1.33673469387755
303434.3367346938776-0.336734693877553
313134.3367346938776-3.33673469387755
322734.3367346938776-7.33673469387755
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2883034.3367346938776-4.33673469387755



Parameters (Session):
par1 = 3 ; par2 = none ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 3 ; par2 = none ; par3 = 3 ; par4 = no ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}