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Author*The author of this computation has been verified*
R Software Modulerwasp_bootstrapplot.wasp
Title produced by softwareBlocked Bootstrap Plot - Central Tendency
Date of computationMon, 15 Nov 2010 18:00:07 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/15/t1289844025svhj61po7tjonqi.htm/, Retrieved Fri, 26 Apr 2024 07:09:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=94967, Retrieved Fri, 26 Apr 2024 07:09:40 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact216
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD  [Blocked Bootstrap Plot - Central Tendency] [Colombia Coffee] [2008-01-07 10:26:26] [74be16979710d4c4e7c6647856088456]
-  M D    [Blocked Bootstrap Plot - Central Tendency] [Workshop 6 Boxplo...] [2010-11-07 12:50:07] [247f085ab5b7724f755ad01dc754a3e8]
-    D        [Blocked Bootstrap Plot - Central Tendency] [Boxplots] [2010-11-15 18:00:07] [9d72585f2b7b60ae977d4816136e1c95] [Current]
-    D          [Blocked Bootstrap Plot - Central Tendency] [Paper lineair reg...] [2010-11-30 16:58:09] [247f085ab5b7724f755ad01dc754a3e8]
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Dataseries X:
17681170,13
19858875,65
16997477,78
16969881,42
18908869,11
15692144,17
15159951,13
15806842,33
16007123,26
16059123,25
16189383,57
12522497,94
14733828,17
15686348,94
13779681,49
14423755,53
15290621,44
14308336,46
13855616,12
14384486,00
15638580,70
19711553,27
20359793,22
16141449,72
20056915,06
20605542,58
19325754,03
20547653,75
19211178,55
19009453,59
18746813,27
16471529,53
18957217,20
20515191,95
18374420,60
16192909,22
18147463,68
19301440,71
18344657,85
17183629,01
19629987,60
17167191,42
17428458,32
16016524,60
18466459,42
18406552,23
18174068,44
14851949,20
16260733,42
18329610,38
18003781,65
15903762,33
19554176,37
16554237,93
16198892,67
16571771,60
17535166,38
16198106,13
17487530,67
13768040,14




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94967&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94967&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94967&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean16825676.129458317161102.704666717547518.7697083546674.146573132721842.640250001
median16334168.2162517082334.617681170.13828709.2135811171347001.91375000

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 16825676.1294583 & 17161102.7046667 & 17547518.7697083 & 546674.146573132 & 721842.640250001 \tabularnewline
median & 16334168.21625 & 17082334.6 & 17681170.13 & 828709.213581117 & 1347001.91375000 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94967&T=1

[TABLE]
[ROW][C]Estimation Results of Blocked 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]16825676.1294583[/C][C]17161102.7046667[/C][C]17547518.7697083[/C][C]546674.146573132[/C][C]721842.640250001[/C][/ROW]
[ROW][C]median[/C][C]16334168.21625[/C][C]17082334.6[/C][C]17681170.13[/C][C]828709.213581117[/C][C]1347001.91375000[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94967&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94967&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 Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean16825676.129458317161102.704666717547518.7697083546674.146573132721842.640250001
median16334168.2162517082334.617681170.13828709.2135811171347001.91375000







95% Confidence Intervals
MeanMedian
Lower Bound17153831.283072717071966.7025551
Upper Bound17256449.694260717262416.1374449

\begin{tabular}{lllllllll}
\hline
95% Confidence Intervals \tabularnewline
 & Mean & Median \tabularnewline
Lower Bound & 17153831.2830727 & 17071966.7025551 \tabularnewline
Upper Bound & 17256449.6942607 & 17262416.1374449 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94967&T=2

[TABLE]
[ROW][C]95% Confidence Intervals[/C][/ROW]
[ROW][C][/C][C]Mean[/C][C]Median[/C][/ROW]
[ROW][C]Lower Bound[/C][C]17153831.2830727[/C][C]17071966.7025551[/C][/ROW]
[ROW][C]Upper Bound[/C][C]17256449.6942607[/C][C]17262416.1374449[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94967&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94967&T=2

As an alternative you can also use a QR Code:  

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

95% Confidence Intervals
MeanMedian
Lower Bound17153831.283072717071966.7025551
Upper Bound17256449.694260717262416.1374449



Parameters (Session):
par1 = 500 ; par2 = 12 ;
Parameters (R input):
par1 = 500 ; par2 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
if (par1 < 10) par1 = 10
if (par1 > 5000) par1 = 5000
if (par2 < 3) par2 = 3
if (par2 > length(x)) par2 = length(x)
library(lattice)
library(boot)
boot.stat <- function(s)
{
s.mean <- mean(s)
s.median <- median(s)
c(s.mean, s.median)
}
(r <- tsboot(x, boot.stat, R=par1, l=12, sim='fixed'))
z <- data.frame(cbind(r$t[,1],r$t[,2]))
colnames(z) <- list('mean','median')
bitmap(file='plot7.png')
b <- boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
b
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Blocked 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,q1)
a<-table.element(a,r$t0[1])
a<-table.element(a,q3)
a<-table.element(a,sqrt(var(r$t[,1])))
a<-table.element(a,q3-q1)
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,q1)
a<-table.element(a,r$t0[2])
a<-table.element(a,q3)
a<-table.element(a,sqrt(var(r$t[,2])))
a<-table.element(a,q3-q1)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')

a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'95% Confidence Intervals',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Mean',1,TRUE)
a<-table.element(a,'Median',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Lower Bound',1,TRUE)
a<-table.element(a,b$conf[1,1])
a<-table.element(a,b$conf[1,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Upper Bound',1,TRUE)
a<-table.element(a,b$conf[2,1])
a<-table.element(a,b$conf[2,2])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')