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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_bootstrapplot.wasp
Title produced by softwareBlocked Bootstrap Plot - Central Tendency
Date of computationFri, 22 Nov 2013 06:43:50 -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/2013/Nov/22/t1385120688idmznv4corvz4c9.htm/, Retrieved Mon, 29 Apr 2024 17:16:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=227505, Retrieved Mon, 29 Apr 2024 17:16:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords750 simulations
Estimated Impact93

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Blocked Bootstrap Plot - Central Tendency] [Wekelijkse verkoc...] [2013-11-22 11:43:50] [74a92a9d3a2c9c03f2186ea574174bd1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
221.102
220.892
225.537
219.334
216.126
224.573
213.991
221.865
215.382
213.962
217.009
318.110
219.662
212.650
209.307
210.541
210.609
208.910
207.541
207.699
205.005
205.747
203.644
229.937
214.446
210.194
206.535
216.524
198.243
208.274
207.493
215.525
207.562
213.355
209.048
220.497
214.563
211.571
216.385
211.496
209.683
206.304
213.925
204.829
205.729
200.296
207.960
207.729
208.327
207.794
213.700
213.136
210.354
202.205
220.425
210.038
205.201
197.441
200.021
203.165
201.699
201.003
232.308
211.412
209.661
213.587
193.611
192.543
196.040
191.407
189.726
191.272
187.924
198.213
199.352
199.289
195.475
198.045
197.615
189.015
189.668
189.120
194.168
192.304
185.913
197.599
186.085
190.566
187.054
193.222
189.856
190.608
190.588
186.773
183.510
180.106
180.150
178.412
182.353
203.805
186.054
184.290
187.483
187.111
189.561
184.439
182.985
183.828
184.036
183.214
183.464
173.718
180.210
171.252
172.705
174.006
172.043
169.445
169.449
177.073
170.799
171.648
172.220
165.795
167.466
165.528
162.851
165.864
162.094
162.385
164.293
165.983
159.680
161.739
159.302
167.795
164.242
159.743
160.887
163.844
161.172
159.330
155.570
156.749
155.012
163.419
153.630
154.535
151.543
152.955
150.166
151.416
150.332
152.196
153.422
147.435

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=227505&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=227505&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=227505&T=0

As an alternative you can also use a QR Code:  
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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean187.966088141026191.728019230769195.5954166666675.554739003626947.62932852564103
median188.4695192.8825199.32058.4149085001009810.851
midrange191.237232.7725232.772520.94546566692641.5355

\begin{tabular}{lllllllll}
\hline
Estimation Results of Blocked Bootstrap \tabularnewline
statistic & Q1 & Estimate & Q3 & S.D. & IQR \tabularnewline
mean & 187.966088141026 & 191.728019230769 & 195.595416666667 & 5.55473900362694 & 7.62932852564103 \tabularnewline
median & 188.4695 & 192.8825 & 199.3205 & 8.41490850010098 & 10.851 \tabularnewline
midrange & 191.237 & 232.7725 & 232.7725 & 20.945465666926 & 41.5355 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=227505&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]187.966088141026[/C][C]191.728019230769[/C][C]195.595416666667[/C][C]5.55473900362694[/C][C]7.62932852564103[/C][/ROW]
[ROW][C]median[/C][C]188.4695[/C][C]192.8825[/C][C]199.3205[/C][C]8.41490850010098[/C][C]10.851[/C][/ROW]
[ROW][C]midrange[/C][C]191.237[/C][C]232.7725[/C][C]232.7725[/C][C]20.945465666926[/C][C]41.5355[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=227505&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=227505&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Estimation Results of Blocked Bootstrap
statisticQ1EstimateQ3S.D.IQR
mean187.966088141026191.728019230769195.5954166666675.554739003626947.62932852564103
median188.4695192.8825199.32058.4149085001009810.851
midrange191.237232.7725232.772520.94546566692641.5355


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 750 ; par2 = 12 ;
Parameters (R input):
par1 = 750 ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- '12'
par1 <- '200'
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)
s.midrange <- (max(s) + min(s)) / 2
c(s.mean, s.median, s.midrange)
}
(r <- tsboot(x, boot.stat, R=par1, l=12, sim='fixed'))
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$t[,3],type='p',ylab='simulated values',main='Simulation of Midrange')
grid()
dev.off()
bitmap(file='plot4.png')
densityplot(~r$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()
z <- data.frame(cbind(r$t[,1],r$t[,2],r$t[,3]))
colnames(z) <- list('mean','median','midrange')
bitmap(file='plot7.png')
boxplot(z,notch=TRUE,ylab='simulated values',main='Bootstrap Simulation - Central Tendency')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimation Results of Blocked Bootstrap',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'statistic',header=TRUE)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,'Estimate',header=TRUE)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'IQR',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
q1 <- quantile(r$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$t[,2])))
a<-table.element(a,q3-q1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'midrange',header=TRUE)
q1 <- quantile(r$t[,3],0.25)[[1]]
q3 <- quantile(r$t[,3],0.75)[[1]]
a<-table.element(a,q1)
a<-table.element(a,r$t0[3])
a<-table.element(a,q3)
a<-table.element(a,sqrt(var(r$t[,3])))
a<-table.element(a,q3-q1)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')