## Free Statistics

of Irreproducible Research!

Author's title
Author*Unverified author*
R Software Modulerwasp_bootstrapplot1.wasp
Title produced by softwareBootstrap Plot - Central Tendency
Date of computationThu, 27 Dec 2012 15:12:14 -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/27/t13566391569sr6z9scjsx6xff.htm/, Retrieved Tue, 31 Jan 2023 07:34:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204785, Retrieved Tue, 31 Jan 2023 07:34:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [Autocorrelatie In...] [2012-11-12 10:59:56] [41982c7b3984978a38ca838fef047984]
- RMPD    [Bootstrap Plot - Central Tendency] [Bootstrap Plot (m...] [2012-12-27 20:12:14] [97ff841fcf87514e420f2e9629cfd808] [Current]
- R P       [Bootstrap Plot - Central Tendency] [Bootstrap Plot (m...] [2012-12-27 20:14:03] [41982c7b3984978a38ca838fef047984]
-   P         [Bootstrap Plot - Central Tendency] [Bootstrap Plot (m...] [2012-12-27 20:16:33] [41982c7b3984978a38ca838fef047984]
- RMPD        [Blocked Bootstrap Plot - Central Tendency] [Bootstrap Plot (g...] [2012-12-27 20:38:33] [41982c7b3984978a38ca838fef047984]
- R  D          [Blocked Bootstrap Plot - Central Tendency] [Bootstrap Plot (g...] [2012-12-27 20:42:04] [41982c7b3984978a38ca838fef047984]
-                 [Blocked Bootstrap Plot - Central Tendency] [Bootstrap Plot (g...] [2012-12-27 20:44:01] [41982c7b3984978a38ca838fef047984]
- RM D            [Variability] [Spreidingsmaten S...] [2012-12-27 20:59:10] [41982c7b3984978a38ca838fef047984]
- RM D            [Standard Deviation Plot] [Spreidingsgrafiek...] [2012-12-27 21:03:45] [41982c7b3984978a38ca838fef047984]
- RM D            [Standard Deviation-Mean Plot] [Spreidings- en ge...] [2012-12-27 21:11:16] [41982c7b3984978a38ca838fef047984]
- RM D            [Variability] [Spreidingsmaten g...] [2012-12-27 21:17:49] [41982c7b3984978a38ca838fef047984]
- RM              [Standard Deviation Plot] [Spreidingsgrafiek...] [2012-12-27 21:20:17] [41982c7b3984978a38ca838fef047984]
- RM              [Standard Deviation-Mean Plot] [Spreidings- en ge...] [2012-12-27 21:24:21] [41982c7b3984978a38ca838fef047984]
- RM D            [Classical Decomposition] [Decompositie Insc...] [2012-12-27 21:30:41] [41982c7b3984978a38ca838fef047984]
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Dataseries X:
20
25
15
15
25
25
25
21
30
25
20
40
13
30
25
20
25
20
25
20
20
15
15
12
20
5
20
15
25
22
20
22
25
20
20
35
30
25
20
20
20
25
25
15
20
35
25
25
30
23
10
22
25
25
22
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20
25
25
22
25
25
25
22
25
12
18
20
20
22
30
25
22
20
50
30
25
20
30
22
25
30
22
25
22
22
25
25
25
20
22
15
20
30
20
25
30
35
22
12
30
15
10
30
9
25
20
20
35
25
35
30
12
25
15
25
25
20
20
6
15
40
20
40
25
25
20
15
15
22
24
22
20
25
25
25
35
40
20
22
22
20
25
25
18
25
20
25
30
20
22
35
22
25
25
25
25
22
23
35
15
25
18
22
25
25
28
30
20
25
25
30
22
30
10
10
25
20
22
25
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15
22
25
25
28
22
30
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20
25
25
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30
50
19
20
28
20
25
35
25
25
15
16
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25
30
20
25
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20
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20
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18
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15
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22
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12
25
30
22
15
22
25
12
18
30
25
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40
24
25
15
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20
25
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20
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30
22
25
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50
19
50
25
35
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18
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30
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8
20
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10
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18
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15
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22
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18
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30
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25
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10
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9
15
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15
20
30
12
15
12
20
15
12
25
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25
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30
20
25
15
15
22
10
15
10
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25
20
20
38
20
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40
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30
25
10
20
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12
15
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15
40
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16
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50
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12
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15
20
25
15
25
50
30
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20
25
12
15
20
20
35
22
15
18
30
22
12
12
20
20
15
25
15
20
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25
18
30
20
25
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25
20
20
25
20
22
15
15
22
20
10
25
20
20
15
12
20
5
20
15
15
25
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25
15
25
22
25
20
18
22
25
35
25
25
25
35
30
22
30
50
15
25
24
20
25
25
25
12
15
22
25
25
25
25
15
20
20
15
35
30
20
22
65
20
25
22
20
25
25
20
25
15
20
12
15
10
25
15
30
35
25
25
25
25
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40
40
25
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40
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35
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18
5
15
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40
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35
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12
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12
9
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15
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18
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20

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 9 seconds R Server 'Gwilym Jenkins' @ jenkins.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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204785&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204785&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204785&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 9 seconds R Server 'Gwilym Jenkins' @ jenkins.wessa.net

 Estimation Results of Bootstrap statistic Q1 Estimate Q3 S.D. IQR mean 23.004 23.166 23.327 0.22605 0.32306 median 22 22 24.5 1.3112 2.5 midrange 27.5 35 35 3.5309 7.5 mode 25 25 25 0 0 mode k.dens 23 25 23.103 2.4966 0.10278

\begin{tabular}{lllllllll}
\hline
Estimation Results of Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 23.004 & 23.166 & 23.327 & 0.22605 & 0.32306 \tabularnewline
median & 22 & 22 & 24.5 & 1.3112 & 2.5 \tabularnewline
midrange & 27.5 & 35 & 35 & 3.5309 & 7.5 \tabularnewline
mode & 25 & 25 & 25 & 0 & 0 \tabularnewline
mode k.dens & 23 & 25 & 23.103 & 2.4966 & 0.10278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204785&T=1

[TABLE]
[ROW][C]Estimation Results of Bootstrap[/C][/ROW]
[ROW][C]statistic[/C][C]Q1[/C][C]Estimate[/C][C]Q3[/C][C]S.D.[/C][C]IQR[/C][/ROW]
[ROW][C]mean[/C][C]23.004[/C][C]23.166[/C][C]23.327[/C][C]0.22605[/C][C]0.32306[/C][/ROW]
[ROW][C]median[/C][C]22[/C][C]22[/C][C]24.5[/C][C]1.3112[/C][C]2.5[/C][/ROW]
[ROW][C]midrange[/C][C]27.5[/C][C]35[/C][C]35[/C][C]3.5309[/C][C]7.5[/C][/ROW]
[ROW][C]mode[/C][C]25[/C][C]25[/C][C]25[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]mode k.dens[/C][C]23[/C][C]25[/C][C]23.103[/C][C]2.4966[/C][C]0.10278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204785&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204785&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Estimation Results of Bootstrap statistic Q1 Estimate Q3 S.D. IQR mean 23.004 23.166 23.327 0.22605 0.32306 median 22 22 24.5 1.3112 2.5 midrange 27.5 35 35 3.5309 7.5 mode 25 25 25 0 0 mode k.dens 23 25 23.103 2.4966 0.10278

par1 <- as.numeric(par1)par2 <- as.numeric(par2)if (par3 == '0') bw <- NULLif (par3 != '0') bw <- as.numeric(par3)if (par1 < 10) par1 = 10if (par1 > 5000) par1 = 5000library(modeest)library(lattice)library(boot)boot.stat <- function(s,i){s.mean <- mean(s[i])s.median <- median(s[i])s.midrange <- (max(s[i]) + min(s[i])) / 2s.mode <- mlv(s[i], method='mfv')$Ms.kernelmode <- mlv(s[i], method='kernel', bw=bw)$Mc(s.mean, s.median, s.midrange, s.mode, s.kernelmode)}(r <- boot(x,boot.stat, R=par1, stype='i'))bitmap(file='plot1.png')plot(r$t[,1],type='p',ylab='simulated values',main='Simulation of Mean')grid()dev.off()bitmap(file='plot2.png')plot(r$t[,2],type='p',ylab='simulated values',main='Simulation of Median')grid()dev.off()bitmap(file='plot3.png')plot(r$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')grid()dev.off()bitmap(file='plot7.png')plot(r$t[,4],type='p',ylab='simulated values',main='Simulation of Mode')grid()dev.off()bitmap(file='plot8.png')plot(r$t[,5],type='p',ylab='simulated values',main='Simulation of Mode of Kernel Density')grid()dev.off()bitmap(file='plot4.png')densityplot(~r$t[,1],col='black',main='Density Plot',xlab='mean')dev.off()bitmap(file='plot5.png')densityplot(~r$t[,2],col='black',main='Density Plot',xlab='median')dev.off()bitmap(file='plot6.png')densityplot(~r$t[,3],col='black',main='Density Plot',xlab='midrange')dev.off()bitmap(file='plot9.png')densityplot(~r$t[,4],col='black',main='Density Plot',xlab='mode')dev.off()bitmap(file='plot10.png')densityplot(~r$t[,5],col='black',main='Density Plot',xlab='mode of kernel dens.')dev.off()z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3],r$t[,4],r$t[,5]))colnames(z) <- list('mean','median','midrange','mode','mode k.dens')bitmap(file='plot11.png')boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')grid()dev.off()load(file='createtable')a<-table.start()a<-table.row.start(a)a<-table.element(a,'Estimation Results of Bootstrap',6,TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'statistic',header=TRUE)a<-table.element(a,'Q1',header=TRUE)a<-table.element(a,'Estimate',header=TRUE)a<-table.element(a,'Q3',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'IQR',header=TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'mean',header=TRUE)q1 <- quantile(r$t[,1],0.25)[[1]]q3 <- quantile(r$t[,1],0.75)[[1]]a<-table.element(a,signif(q1,par2))a<-table.element(a,signif(r$t0[1],par2))a<-table.element(a,signif(q3,par2))a<-table.element( a,signif( sqrt(var(r$t[,1])),par2 ) )a<-table.element(a,signif(q3-q1,par2))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'median',header=TRUE)q1 <- quantile(r$t[,2],0.25)[[1]]q3 <- quantile(r$t[,2],0.75)[[1]]a<-table.element(a,signif(q1,par2))a<-table.element(a,signif(r$t0[2],par2))a<-table.element(a,signif(q3,par2))a<-table.element(a,signif(sqrt(var(r$t[,2])),par2))a<-table.element(a,signif(q3-q1,par2))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'midrange',header=TRUE)q1 <- quantile(r$t[,3],0.25)[[1]]q3 <- quantile(r$t[,3],0.75)[[1]]a<-table.element(a,signif(q1,par2))a<-table.element(a,signif(r$t0[3],par2))a<-table.element(a,signif(q3,par2))a<-table.element(a,signif(sqrt(var(r$t[,3])),par2))a<-table.element(a,signif(q3-q1,par2))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'mode',header=TRUE)q1 <- quantile(r$t[,4],0.25)[[1]]q3 <- quantile(r$t[,4],0.75)[[1]]a<-table.element(a,signif(q1,par2))a<-table.element(a,signif(r$t0[4],par2))a<-table.element(a,signif(q3,par2))a<-table.element(a,signif(sqrt(var(r$t[,4])),par2))a<-table.element(a,signif(q3-q1,par2))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'mode k.dens',header=TRUE)q1 <- quantile(r$t[,5],0.25)[[1]]q3 <- quantile(r$t[,5],0.75)[[1]]a<-table.element(a,signif(q1,par2))a<-table.element(a,signif(r$t0[5],par2))a<-table.element(a,signif(q3,par2))a<-table.element(a,signif(sqrt(var(r\$t[,5])),par2))a<-table.element(a,signif(q3-q1,par2))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable.tab')