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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationSat, 09 Dec 2017 11:21:06 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/09/t15128150880pvlullvgb0so4v.htm/, Retrieved Mon, 13 May 2024 22:50:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308838, Retrieved Mon, 13 May 2024 22:50:36 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
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Estimated Impact85

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [histogram aantal ...] [2017-12-09 10:21:06] [1fcbe9b3e47b52dd478864c188c3f957] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
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Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308838&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308838&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308838&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center






Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[0,0.2[0.125930.9893170.9893174.946585
[0.2,0.4[0.3000.9893170
[0.4,0.6[0.5000.9893170
[0.6,0.8[0.7000.9893170
[0.8,1[0.9000.9893170
[1,1.2[1.1250.0095380.9988550.047692
[1.2,1.4[1.3000.9988550
[1.4,1.6[1.5000.9988550
[1.6,1.8[1.7000.9988550
[1.8,2[1.9000.9988550
[2,2.2[2.120.0007630.9996180.003815
[2.2,2.4[2.3000.9996180
[2.4,2.6[2.5000.9996180
[2.6,2.8[2.7000.9996180
[2.8,3]2.910.00038210.001908

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[0,0.2[ & 0.1 & 2593 & 0.989317 & 0.989317 & 4.946585 \tabularnewline
[0.2,0.4[ & 0.3 & 0 & 0 & 0.989317 & 0 \tabularnewline
[0.4,0.6[ & 0.5 & 0 & 0 & 0.989317 & 0 \tabularnewline
[0.6,0.8[ & 0.7 & 0 & 0 & 0.989317 & 0 \tabularnewline
[0.8,1[ & 0.9 & 0 & 0 & 0.989317 & 0 \tabularnewline
[1,1.2[ & 1.1 & 25 & 0.009538 & 0.998855 & 0.047692 \tabularnewline
[1.2,1.4[ & 1.3 & 0 & 0 & 0.998855 & 0 \tabularnewline
[1.4,1.6[ & 1.5 & 0 & 0 & 0.998855 & 0 \tabularnewline
[1.6,1.8[ & 1.7 & 0 & 0 & 0.998855 & 0 \tabularnewline
[1.8,2[ & 1.9 & 0 & 0 & 0.998855 & 0 \tabularnewline
[2,2.2[ & 2.1 & 2 & 0.000763 & 0.999618 & 0.003815 \tabularnewline
[2.2,2.4[ & 2.3 & 0 & 0 & 0.999618 & 0 \tabularnewline
[2.4,2.6[ & 2.5 & 0 & 0 & 0.999618 & 0 \tabularnewline
[2.6,2.8[ & 2.7 & 0 & 0 & 0.999618 & 0 \tabularnewline
[2.8,3] & 2.9 & 1 & 0.000382 & 1 & 0.001908 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308838&T=1

[TABLE]
[ROW][C]Frequency Table (Histogram)[/C][/ROW]
[ROW][C]Bins[/C][C]Midpoint[/C][C]Abs. Frequency[/C][C]Rel. Frequency[/C][C]Cumul. Rel. Freq.[/C][C]Density[/C][/ROW]
[ROW][C][0,0.2[[/C][C]0.1[/C][C]2593[/C][C]0.989317[/C][C]0.989317[/C][C]4.946585[/C][/ROW]
[ROW][C][0.2,0.4[[/C][C]0.3[/C][C]0[/C][C]0[/C][C]0.989317[/C][C]0[/C][/ROW]
[ROW][C][0.4,0.6[[/C][C]0.5[/C][C]0[/C][C]0[/C][C]0.989317[/C][C]0[/C][/ROW]
[ROW][C][0.6,0.8[[/C][C]0.7[/C][C]0[/C][C]0[/C][C]0.989317[/C][C]0[/C][/ROW]
[ROW][C][0.8,1[[/C][C]0.9[/C][C]0[/C][C]0[/C][C]0.989317[/C][C]0[/C][/ROW]
[ROW][C][1,1.2[[/C][C]1.1[/C][C]25[/C][C]0.009538[/C][C]0.998855[/C][C]0.047692[/C][/ROW]
[ROW][C][1.2,1.4[[/C][C]1.3[/C][C]0[/C][C]0[/C][C]0.998855[/C][C]0[/C][/ROW]
[ROW][C][1.4,1.6[[/C][C]1.5[/C][C]0[/C][C]0[/C][C]0.998855[/C][C]0[/C][/ROW]
[ROW][C][1.6,1.8[[/C][C]1.7[/C][C]0[/C][C]0[/C][C]0.998855[/C][C]0[/C][/ROW]
[ROW][C][1.8,2[[/C][C]1.9[/C][C]0[/C][C]0[/C][C]0.998855[/C][C]0[/C][/ROW]
[ROW][C][2,2.2[[/C][C]2.1[/C][C]2[/C][C]0.000763[/C][C]0.999618[/C][C]0.003815[/C][/ROW]
[ROW][C][2.2,2.4[[/C][C]2.3[/C][C]0[/C][C]0[/C][C]0.999618[/C][C]0[/C][/ROW]
[ROW][C][2.4,2.6[[/C][C]2.5[/C][C]0[/C][C]0[/C][C]0.999618[/C][C]0[/C][/ROW]
[ROW][C][2.6,2.8[[/C][C]2.7[/C][C]0[/C][C]0[/C][C]0.999618[/C][C]0[/C][/ROW]
[ROW][C][2.8,3][/C][C]2.9[/C][C]1[/C][C]0.000382[/C][C]1[/C][C]0.001908[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308838&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308838&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:

Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[0,0.2[0.125930.9893170.9893174.946585
[0.2,0.4[0.3000.9893170
[0.4,0.6[0.5000.9893170
[0.6,0.8[0.7000.9893170
[0.8,1[0.9000.9893170
[1,1.2[1.1250.0095380.9988550.047692
[1.2,1.4[1.3000.9988550
[1.4,1.6[1.5000.9988550
[1.6,1.8[1.7000.9988550
[1.8,2[1.9000.9988550
[2,2.2[2.120.0007630.9996180.003815
[2.2,2.4[2.3000.9996180
[2.4,2.6[2.5000.9996180
[2.6,2.8[2.7000.9996180
[2.8,3]2.910.00038210.001908


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par2 = grey ; par3 = FALSE ; par4 = Unknown ;
Parameters (R input):
par1 = ; par2 = grey ; par3 = FALSE ; par4 = Unknown ;
R code (references can be found in the software module):
par4 <- 'Unknown'
par3 <- 'FALSE'
par2 <- 'grey'
par1 <- ''
par1 <- as.numeric(par1)
if (par3 == 'TRUE') par3 <- TRUE
if (par3 == 'FALSE') par3 <- FALSE
if (par4 == 'Unknown') par1 <- as.numeric(par1)
if (par4 == 'Interval/Ratio') par1 <- as.numeric(par1)
if (par4 == '3-point Likert') par1 <- c(1:3 - 0.5, 3.5)
if (par4 == '4-point Likert') par1 <- c(1:4 - 0.5, 4.5)
if (par4 == '5-point Likert') par1 <- c(1:5 - 0.5, 5.5)
if (par4 == '6-point Likert') par1 <- c(1:6 - 0.5, 6.5)
if (par4 == '7-point Likert') par1 <- c(1:7 - 0.5, 7.5)
if (par4 == '8-point Likert') par1 <- c(1:8 - 0.5, 8.5)
if (par4 == '9-point Likert') par1 <- c(1:9 - 0.5, 9.5)
if (par4 == '10-point Likert') par1 <- c(1:10 - 0.5, 10.5)
bitmap(file='test1.png')
if(is.numeric(x[1])) {
if (is.na(par1)) {
myhist<-hist(x,col=par2,main=main,xlab=xlab,right=par3)
} else {
if (par1 < 0) par1 <- 3
if (par1 > 50) par1 <- 50
myhist<-hist(x,breaks=par1,col=par2,main=main,xlab=xlab,right=par3)
}
} else {
barplot(mytab <- sort(table(x),T),col=par2,main='Frequency Plot',xlab=xlab,ylab='Absolute Frequency')
}
dev.off()
if(is.numeric(x[1])) {
myhist
n <- length(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Frequency Table (Histogram)',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Bins',header=TRUE)
a<-table.element(a,'Midpoint',header=TRUE)
a<-table.element(a,'Abs. Frequency',header=TRUE)
a<-table.element(a,'Rel. Frequency',header=TRUE)
a<-table.element(a,'Cumul. Rel. Freq.',header=TRUE)
a<-table.element(a,'Density',header=TRUE)
a<-table.row.end(a)
crf <- 0
if (par3 == FALSE) mybracket <- '[' else mybracket <- ']'
mynumrows <- (length(myhist$breaks)-1)
for (i in 1:mynumrows) {
a<-table.row.start(a)
if (i == 1)
dum <- paste('[',myhist$breaks[i],sep='')
else
dum <- paste(mybracket,myhist$breaks[i],sep='')
dum <- paste(dum,myhist$breaks[i+1],sep=',')
if (i==mynumrows)
dum <- paste(dum,']',sep='')
else
dum <- paste(dum,mybracket,sep='')
a<-table.element(a,dum,header=TRUE)
a<-table.element(a,myhist$mids[i])
a<-table.element(a,myhist$counts[i])
rf <- myhist$counts[i]/n
crf <- crf + rf
a<-table.element(a,round(rf,6))
a<-table.element(a,round(crf,6))
a<-table.element(a,round(myhist$density[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
} else {
mytab
reltab <- mytab / sum(mytab)
n <- length(mytab)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Frequency Table (Categorical Data)',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Category',header=TRUE)
a<-table.element(a,'Abs. Frequency',header=TRUE)
a<-table.element(a,'Rel. Frequency',header=TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,labels(mytab)$x[i],header=TRUE)
a<-table.element(a,mytab[i])
a<-table.element(a,round(reltab[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
}