Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationFri, 07 Dec 2012 09:46:54 -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/07/t1354891659m55xp2f62v1v7x6.htm/, Retrieved Thu, 28 Mar 2024 21:34:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197411, Retrieved Thu, 28 Mar 2024 21:34:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 Tutorial] [2010-11-18 16:04:53] [afe9379cca749d06b3d6872e02cc47ed]
-    D    [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [afe9379cca749d06b3d6872e02cc47ed]
- R  D      [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:29:34] [f5fdea4413921432bb019d1f20c4f2ec]
- R  D        [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:47:41] [f5fdea4413921432bb019d1f20c4f2ec]
- RMP           [Kendall tau Correlation Matrix] [workshop 10 a] [2012-12-07 13:55:33] [dbae308bdff61c0f4902cc85498d0d35]
- RMP               [Recursive Partitioning (Regression Trees)] [workshop 10 d] [2012-12-07 14:46:54] [7915dafcfdccff56a257085e1714b048] [Current]
-   P                 [Recursive Partitioning (Regression Trees)] [WS 10 recursive p...] [2012-12-10 17:38:57] [8c30f4dd45e15fd207e4faf2fdf6253e]
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Dataseries X:
1	26	21	21	23	17	23	4
1	20	16	15	24	17	20	4
1	19	19	18	22	18	20	6
2	19	18	11	20	21	21	8
1	20	16	8	24	20	24	8
1	25	23	19	27	28	22	4
2	25	17	4	28	19	23	4
1	22	12	20	27	22	20	8
1	26	19	16	24	16	25	5
1	22	16	14	23	18	23	4
2	17	19	10	24	25	27	4
2	22	20	13	27	17	27	4
1	19	13	14	27	14	22	4
1	24	20	8	28	11	24	4
1	26	27	23	27	27	25	4
2	21	17	11	23	20	22	8
1	13	8	9	24	22	28	4
2	26	25	24	28	22	28	4
2	20	26	5	27	21	27	4
1	22	13	15	25	23	25	8
2	14	19	5	19	17	16	4
1	21	15	19	24	24	28	7
1	7	5	6	20	14	21	4
2	23	16	13	28	17	24	4
1	17	14	11	26	23	27	5
1	25	24	17	23	24	14	4
1	25	24	17	23	24	14	4
1	19	9	5	20	8	27	4
2	20	19	9	11	22	20	4
1	23	19	15	24	23	21	4
2	22	25	17	25	25	22	4
1	22	19	17	23	21	21	4
1	21	18	20	18	24	12	15
2	15	15	12	20	15	20	10
2	20	12	7	20	22	24	4
2	22	21	16	24	21	19	8
1	18	12	7	23	25	28	4
2	20	15	14	25	16	23	4
2	28	28	24	28	28	27	4
1	22	25	15	26	23	22	4
1	18	19	15	26	21	27	7
1	23	20	10	23	21	26	4
1	20	24	14	22	26	22	6
2	25	26	18	24	22	21	5
2	26	25	12	21	21	19	4
1	15	12	9	20	18	24	16
2	17	12	9	22	12	19	5
2	23	15	8	20	25	26	12
1	21	17	18	25	17	22	6
2	13	14	10	20	24	28	9
1	18	16	17	22	15	21	9
1	19	11	14	23	13	23	4
1	22	20	16	25	26	28	5
1	16	11	10	23	16	10	4
2	24	22	19	23	24	24	4
1	18	20	10	22	21	21	5
1	20	19	14	24	20	21	4
1	24	17	10	25	14	24	4
2	14	21	4	21	25	24	4
2	22	23	19	12	25	25	5
1	24	18	9	17	20	25	4
1	18	17	12	20	22	23	6
1	21	27	16	23	20	21	4
2	23	25	11	23	26	16	4
1	17	19	18	20	18	17	18
2	22	22	11	28	22	25	4
2	24	24	24	24	24	24	6
2	21	20	17	24	17	23	4
1	22	19	18	24	24	25	4
1	16	11	9	24	20	23	5
1	21	22	19	28	19	28	4
2	23	22	18	25	20	26	4
2	22	16	12	21	15	22	5
1	24	20	23	25	23	19	10
1	24	24	22	25	26	26	5
1	16	16	14	18	22	18	8
1	16	16	14	17	20	18	8
2	21	22	16	26	24	25	5
2	26	24	23	28	26	27	4
2	15	16	7	21	21	12	4
2	25	27	10	27	25	15	4
1	18	11	12	22	13	21	5
0	23	21	12	21	20	23	4
1	20	20	12	25	22	22	4
2	17	20	17	22	23	21	8
2	25	27	21	23	28	24	4
1	24	20	16	26	22	27	5
1	17	12	11	19	20	22	14
1	19	8	14	25	6	28	8
1	20	21	13	21	21	26	8
1	15	18	9	13	20	10	4
2	27	24	19	24	18	19	4
1	22	16	13	25	23	22	6
1	23	18	19	26	20	21	4
1	16	20	13	25	24	24	7
1	19	20	13	25	22	25	7
2	25	19	13	22	21	21	4
1	19	17	14	21	18	20	6
2	19	16	12	23	21	21	4
2	26	26	22	25	23	24	7
1	21	15	11	24	23	23	4
2	20	22	5	21	15	18	4
1	24	17	18	21	21	24	8
1	22	23	19	25	24	24	4
2	20	21	14	22	23	19	4
1	18	19	15	20	21	20	10
2	18	14	12	20	21	18	8
1	24	17	19	23	20	20	6
1	24	12	15	28	11	27	4
1	22	24	17	23	22	23	4
1	23	18	8	28	27	26	4
1	22	20	10	24	25	23	5
1	20	16	12	18	18	17	4
1	18	20	12	20	20	21	6
1	25	22	20	28	24	25	4
2	18	12	12	21	10	23	5
1	16	16	12	21	27	27	7
1	20	17	14	25	21	24	8
2	19	22	6	19	21	20	5
1	15	12	10	18	18	27	8
1	19	14	18	21	15	21	10
1	19	23	18	22	24	24	8
1	16	15	7	24	22	21	5
1	17	17	18	15	14	15	12
1	28	28	9	28	28	25	4
2	23	20	17	26	18	25	5
1	25	23	22	23	26	22	4
1	20	13	11	26	17	24	6
2	17	18	15	20	19	21	4
2	23	23	17	22	22	22	4
1	16	19	15	20	18	23	7
2	23	23	22	23	24	22	7
2	11	12	9	22	15	20	10
2	18	16	13	24	18	23	4
2	24	23	20	23	26	25	5
1	23	13	14	22	11	23	8
1	21	22	14	26	26	22	11
2	16	18	12	23	21	25	7
2	24	23	20	27	23	26	4
1	23	20	20	23	23	22	8
1	18	10	8	21	15	24	6
1	20	17	17	26	22	24	7
1	9	18	9	23	26	25	5
2	24	15	18	21	16	20	4
1	25	23	22	27	20	26	8
1	20	17	10	19	18	21	4
2	21	17	13	23	22	26	8
2	25	22	15	25	16	21	6
2	22	20	18	23	19	22	4
2	21	20	18	22	20	16	9
1	21	19	12	22	19	26	5
1	22	18	12	25	23	28	6
1	27	22	20	25	24	18	4
2	24	20	12	28	25	25	4
2	24	22	16	28	21	23	4
2	21	18	16	20	21	21	5
1	18	16	18	25	23	20	6
1	16	16	16	19	27	25	16
1	22	16	13	25	23	22	6
1	20	16	17	22	18	21	6
2	18	17	13	18	16	16	4
1	20	18	17	20	16	18	4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197411&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197411&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197411&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 time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Goodness of Fit
Correlation0.255
R-squared0.065
RMSE0.4821

\begin{tabular}{lllllllll}
\hline
Goodness of Fit \tabularnewline
Correlation & 0.255 \tabularnewline
R-squared & 0.065 \tabularnewline
RMSE & 0.4821 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197411&T=1

[TABLE]
[ROW][C]Goodness of Fit[/C][/ROW]
[ROW][C]Correlation[/C][C]0.255[/C][/ROW]
[ROW][C]R-squared[/C][C]0.065[/C][/ROW]
[ROW][C]RMSE[/C][C]0.4821[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197411&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197411&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
Correlation0.255
R-squared0.065
RMSE0.4821







Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
111.30508474576271-0.305084745762712
211.30508474576271-0.305084745762712
311.30508474576271-0.305084745762712
421.305084745762710.694915254237288
511.30508474576271-0.305084745762712
611.59090909090909-0.590909090909091
721.305084745762710.694915254237288
811.30508474576271-0.305084745762712
911.30508474576271-0.305084745762712
1011.30508474576271-0.305084745762712
1121.305084745762710.694915254237288
1221.305084745762710.694915254237288
1311.30508474576271-0.305084745762712
1411.30508474576271-0.305084745762712
1511.59090909090909-0.590909090909091
1621.305084745762710.694915254237288
1711.30508474576271-0.305084745762712
1821.590909090909090.409090909090909
1921.590909090909090.409090909090909
2011.30508474576271-0.305084745762712
2121.305084745762710.694915254237288
2211.30508474576271-0.305084745762712
2311.30508474576271-0.305084745762712
2421.305084745762710.694915254237288
2511.30508474576271-0.305084745762712
2611.59090909090909-0.590909090909091
2711.59090909090909-0.590909090909091
2811.30508474576271-0.305084745762712
2921.305084745762710.694915254237288
3011.30508474576271-0.305084745762712
3121.590909090909090.409090909090909
3211.30508474576271-0.305084745762712
3311.30508474576271-0.305084745762712
3421.305084745762710.694915254237288
3521.305084745762710.694915254237288
3621.305084745762710.694915254237288
3711.30508474576271-0.305084745762712
3821.305084745762710.694915254237288
3921.590909090909090.409090909090909
4011.59090909090909-0.590909090909091
4111.30508474576271-0.305084745762712
4211.30508474576271-0.305084745762712
4311.59090909090909-0.590909090909091
4421.590909090909090.409090909090909
4521.590909090909090.409090909090909
4611.30508474576271-0.305084745762712
4721.305084745762710.694915254237288
4821.305084745762710.694915254237288
4911.30508474576271-0.305084745762712
5021.305084745762710.694915254237288
5111.30508474576271-0.305084745762712
5211.30508474576271-0.305084745762712
5311.30508474576271-0.305084745762712
5411.30508474576271-0.305084745762712
5521.590909090909090.409090909090909
5611.30508474576271-0.305084745762712
5711.30508474576271-0.305084745762712
5811.30508474576271-0.305084745762712
5921.305084745762710.694915254237288
6021.590909090909090.409090909090909
6111.30508474576271-0.305084745762712
6211.30508474576271-0.305084745762712
6311.59090909090909-0.590909090909091
6421.590909090909090.409090909090909
6511.30508474576271-0.305084745762712
6621.590909090909090.409090909090909
6721.590909090909090.409090909090909
6821.305084745762710.694915254237288
6911.30508474576271-0.305084745762712
7011.30508474576271-0.305084745762712
7111.59090909090909-0.590909090909091
7221.590909090909090.409090909090909
7321.305084745762710.694915254237288
7411.30508474576271-0.305084745762712
7511.59090909090909-0.590909090909091
7611.30508474576271-0.305084745762712
7711.30508474576271-0.305084745762712
7821.590909090909090.409090909090909
7921.590909090909090.409090909090909
8021.305084745762710.694915254237288
8121.590909090909090.409090909090909
8211.30508474576271-0.305084745762712
8301.30508474576271-1.30508474576271
8411.30508474576271-0.305084745762712
8521.305084745762710.694915254237288
8621.590909090909090.409090909090909
8711.30508474576271-0.305084745762712
8811.30508474576271-0.305084745762712
8911.30508474576271-0.305084745762712
9011.30508474576271-0.305084745762712
9111.30508474576271-0.305084745762712
9221.590909090909090.409090909090909
9311.30508474576271-0.305084745762712
9411.30508474576271-0.305084745762712
9511.30508474576271-0.305084745762712
9611.30508474576271-0.305084745762712
9721.305084745762710.694915254237288
9811.30508474576271-0.305084745762712
9921.305084745762710.694915254237288
10021.590909090909090.409090909090909
10111.30508474576271-0.305084745762712
10221.590909090909090.409090909090909
10311.30508474576271-0.305084745762712
10411.59090909090909-0.590909090909091
10521.305084745762710.694915254237288
10611.30508474576271-0.305084745762712
10721.305084745762710.694915254237288
10811.30508474576271-0.305084745762712
10911.30508474576271-0.305084745762712
11011.59090909090909-0.590909090909091
11111.30508474576271-0.305084745762712
11211.30508474576271-0.305084745762712
11311.30508474576271-0.305084745762712
11411.30508474576271-0.305084745762712
11511.59090909090909-0.590909090909091
11621.305084745762710.694915254237288
11711.30508474576271-0.305084745762712
11811.30508474576271-0.305084745762712
11921.590909090909090.409090909090909
12011.30508474576271-0.305084745762712
12111.30508474576271-0.305084745762712
12211.59090909090909-0.590909090909091
12311.30508474576271-0.305084745762712
12411.30508474576271-0.305084745762712
12511.59090909090909-0.590909090909091
12621.305084745762710.694915254237288
12711.59090909090909-0.590909090909091
12811.30508474576271-0.305084745762712
12921.305084745762710.694915254237288
13021.590909090909090.409090909090909
13111.30508474576271-0.305084745762712
13221.590909090909090.409090909090909
13321.305084745762710.694915254237288
13421.305084745762710.694915254237288
13521.590909090909090.409090909090909
13611.30508474576271-0.305084745762712
13711.59090909090909-0.590909090909091
13821.305084745762710.694915254237288
13921.590909090909090.409090909090909
14011.30508474576271-0.305084745762712
14111.30508474576271-0.305084745762712
14211.30508474576271-0.305084745762712
14311.30508474576271-0.305084745762712
14421.305084745762710.694915254237288
14511.59090909090909-0.590909090909091
14611.30508474576271-0.305084745762712
14721.305084745762710.694915254237288
14821.590909090909090.409090909090909
14921.305084745762710.694915254237288
15021.305084745762710.694915254237288
15111.30508474576271-0.305084745762712
15211.30508474576271-0.305084745762712
15311.59090909090909-0.590909090909091
15421.305084745762710.694915254237288
15521.590909090909090.409090909090909
15621.305084745762710.694915254237288
15711.30508474576271-0.305084745762712
15811.30508474576271-0.305084745762712
15911.30508474576271-0.305084745762712
16011.30508474576271-0.305084745762712
16121.305084745762710.694915254237288
16211.30508474576271-0.305084745762712

\begin{tabular}{lllllllll}
\hline
Actuals, Predictions, and Residuals \tabularnewline
# & Actuals & Forecasts & Residuals \tabularnewline
1 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
2 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
3 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
4 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
5 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
6 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
7 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
8 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
9 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
10 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
11 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
12 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
13 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
14 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
15 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
16 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
17 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
18 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
19 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
20 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
21 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
22 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
23 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
24 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
25 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
26 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
27 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
28 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
29 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
30 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
31 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
32 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
33 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
34 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
35 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
36 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
37 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
38 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
39 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
40 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
41 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
42 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
43 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
44 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
45 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
46 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
47 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
48 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
49 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
50 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
51 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
52 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
53 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
54 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
55 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
56 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
57 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
58 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
59 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
60 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
61 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
62 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
63 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
64 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
65 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
66 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
67 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
68 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
69 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
70 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
71 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
72 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
73 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
74 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
75 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
76 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
77 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
78 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
79 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
80 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
81 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
82 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
83 & 0 & 1.30508474576271 & -1.30508474576271 \tabularnewline
84 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
85 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
86 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
87 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
88 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
89 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
90 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
91 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
92 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
93 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
94 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
95 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
96 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
97 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
98 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
99 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
100 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
101 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
102 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
103 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
104 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
105 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
106 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
107 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
108 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
109 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
110 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
111 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
112 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
113 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
114 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
115 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
116 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
117 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
118 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
119 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
120 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
121 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
122 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
123 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
124 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
125 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
126 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
127 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
128 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
129 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
130 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
131 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
132 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
133 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
134 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
135 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
136 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
137 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
138 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
139 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
140 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
141 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
142 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
143 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
144 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
145 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
146 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
147 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
148 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
149 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
150 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
151 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
152 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
153 & 1 & 1.59090909090909 & -0.590909090909091 \tabularnewline
154 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
155 & 2 & 1.59090909090909 & 0.409090909090909 \tabularnewline
156 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
157 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
158 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
159 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
160 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
161 & 2 & 1.30508474576271 & 0.694915254237288 \tabularnewline
162 & 1 & 1.30508474576271 & -0.305084745762712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197411&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]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]1.30508474576271[/C][C]-1.30508474576271[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]149[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.59090909090909[/C][C]-0.590909090909091[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.59090909090909[/C][C]0.409090909090909[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[ROW][C]161[/C][C]2[/C][C]1.30508474576271[/C][C]0.694915254237288[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]1.30508474576271[/C][C]-0.305084745762712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197411&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197411&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
111.30508474576271-0.305084745762712
211.30508474576271-0.305084745762712
311.30508474576271-0.305084745762712
421.305084745762710.694915254237288
511.30508474576271-0.305084745762712
611.59090909090909-0.590909090909091
721.305084745762710.694915254237288
811.30508474576271-0.305084745762712
911.30508474576271-0.305084745762712
1011.30508474576271-0.305084745762712
1121.305084745762710.694915254237288
1221.305084745762710.694915254237288
1311.30508474576271-0.305084745762712
1411.30508474576271-0.305084745762712
1511.59090909090909-0.590909090909091
1621.305084745762710.694915254237288
1711.30508474576271-0.305084745762712
1821.590909090909090.409090909090909
1921.590909090909090.409090909090909
2011.30508474576271-0.305084745762712
2121.305084745762710.694915254237288
2211.30508474576271-0.305084745762712
2311.30508474576271-0.305084745762712
2421.305084745762710.694915254237288
2511.30508474576271-0.305084745762712
2611.59090909090909-0.590909090909091
2711.59090909090909-0.590909090909091
2811.30508474576271-0.305084745762712
2921.305084745762710.694915254237288
3011.30508474576271-0.305084745762712
3121.590909090909090.409090909090909
3211.30508474576271-0.305084745762712
3311.30508474576271-0.305084745762712
3421.305084745762710.694915254237288
3521.305084745762710.694915254237288
3621.305084745762710.694915254237288
3711.30508474576271-0.305084745762712
3821.305084745762710.694915254237288
3921.590909090909090.409090909090909
4011.59090909090909-0.590909090909091
4111.30508474576271-0.305084745762712
4211.30508474576271-0.305084745762712
4311.59090909090909-0.590909090909091
4421.590909090909090.409090909090909
4521.590909090909090.409090909090909
4611.30508474576271-0.305084745762712
4721.305084745762710.694915254237288
4821.305084745762710.694915254237288
4911.30508474576271-0.305084745762712
5021.305084745762710.694915254237288
5111.30508474576271-0.305084745762712
5211.30508474576271-0.305084745762712
5311.30508474576271-0.305084745762712
5411.30508474576271-0.305084745762712
5521.590909090909090.409090909090909
5611.30508474576271-0.305084745762712
5711.30508474576271-0.305084745762712
5811.30508474576271-0.305084745762712
5921.305084745762710.694915254237288
6021.590909090909090.409090909090909
6111.30508474576271-0.305084745762712
6211.30508474576271-0.305084745762712
6311.59090909090909-0.590909090909091
6421.590909090909090.409090909090909
6511.30508474576271-0.305084745762712
6621.590909090909090.409090909090909
6721.590909090909090.409090909090909
6821.305084745762710.694915254237288
6911.30508474576271-0.305084745762712
7011.30508474576271-0.305084745762712
7111.59090909090909-0.590909090909091
7221.590909090909090.409090909090909
7321.305084745762710.694915254237288
7411.30508474576271-0.305084745762712
7511.59090909090909-0.590909090909091
7611.30508474576271-0.305084745762712
7711.30508474576271-0.305084745762712
7821.590909090909090.409090909090909
7921.590909090909090.409090909090909
8021.305084745762710.694915254237288
8121.590909090909090.409090909090909
8211.30508474576271-0.305084745762712
8301.30508474576271-1.30508474576271
8411.30508474576271-0.305084745762712
8521.305084745762710.694915254237288
8621.590909090909090.409090909090909
8711.30508474576271-0.305084745762712
8811.30508474576271-0.305084745762712
8911.30508474576271-0.305084745762712
9011.30508474576271-0.305084745762712
9111.30508474576271-0.305084745762712
9221.590909090909090.409090909090909
9311.30508474576271-0.305084745762712
9411.30508474576271-0.305084745762712
9511.30508474576271-0.305084745762712
9611.30508474576271-0.305084745762712
9721.305084745762710.694915254237288
9811.30508474576271-0.305084745762712
9921.305084745762710.694915254237288
10021.590909090909090.409090909090909
10111.30508474576271-0.305084745762712
10221.590909090909090.409090909090909
10311.30508474576271-0.305084745762712
10411.59090909090909-0.590909090909091
10521.305084745762710.694915254237288
10611.30508474576271-0.305084745762712
10721.305084745762710.694915254237288
10811.30508474576271-0.305084745762712
10911.30508474576271-0.305084745762712
11011.59090909090909-0.590909090909091
11111.30508474576271-0.305084745762712
11211.30508474576271-0.305084745762712
11311.30508474576271-0.305084745762712
11411.30508474576271-0.305084745762712
11511.59090909090909-0.590909090909091
11621.305084745762710.694915254237288
11711.30508474576271-0.305084745762712
11811.30508474576271-0.305084745762712
11921.590909090909090.409090909090909
12011.30508474576271-0.305084745762712
12111.30508474576271-0.305084745762712
12211.59090909090909-0.590909090909091
12311.30508474576271-0.305084745762712
12411.30508474576271-0.305084745762712
12511.59090909090909-0.590909090909091
12621.305084745762710.694915254237288
12711.59090909090909-0.590909090909091
12811.30508474576271-0.305084745762712
12921.305084745762710.694915254237288
13021.590909090909090.409090909090909
13111.30508474576271-0.305084745762712
13221.590909090909090.409090909090909
13321.305084745762710.694915254237288
13421.305084745762710.694915254237288
13521.590909090909090.409090909090909
13611.30508474576271-0.305084745762712
13711.59090909090909-0.590909090909091
13821.305084745762710.694915254237288
13921.590909090909090.409090909090909
14011.30508474576271-0.305084745762712
14111.30508474576271-0.305084745762712
14211.30508474576271-0.305084745762712
14311.30508474576271-0.305084745762712
14421.305084745762710.694915254237288
14511.59090909090909-0.590909090909091
14611.30508474576271-0.305084745762712
14721.305084745762710.694915254237288
14821.590909090909090.409090909090909
14921.305084745762710.694915254237288
15021.305084745762710.694915254237288
15111.30508474576271-0.305084745762712
15211.30508474576271-0.305084745762712
15311.59090909090909-0.590909090909091
15421.305084745762710.694915254237288
15521.590909090909090.409090909090909
15621.305084745762710.694915254237288
15711.30508474576271-0.305084745762712
15811.30508474576271-0.305084745762712
15911.30508474576271-0.305084745762712
16011.30508474576271-0.305084745762712
16121.305084745762710.694915254237288
16211.30508474576271-0.305084745762712



Parameters (Session):
par1 = 1 ; par2 = none ; par4 = no ;
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')
}