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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 computationMon, 03 Oct 2011 12:11:34 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Oct/03/t1317658319hkw6hvi1e6kntb2.htm/, Retrieved Wed, 04 Oct 2023 01:14:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=125243, Retrieved Wed, 04 Oct 2023 01:14:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [Workshop 1, task 1] [2011-10-03 16:11:34] [79818163420d1233b8d9d93d595e6c9e] [Current]
- RMPD    [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Taak 1 WS 6] [2011-11-11 16:49:06] [86f7284edee3dbb8ea5c7e2dec87d892]
- RMPD    [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [Taak 1 WS 6] [2011-11-11 16:55:17] [86f7284edee3dbb8ea5c7e2dec87d892]
- RMPD    [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2011-11-11 17:11:18] [86f7284edee3dbb8ea5c7e2dec87d892]
- RMPD    [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2011-11-11 17:13:43] [86f7284edee3dbb8ea5c7e2dec87d892]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'Linuxi686'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'IntelMacOSX10_6_2'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.1'
'WindowsNT6.0'
'IntelMacOSX10_6_2'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'IntelMacOSX10_5_8'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'IntelMacOSX10_5_8'
'IntelMacOSX10_5_8'
'WindowsNT6.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.0'
'IntelMacOSX10_6_2'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT5.1'
'IntelMacOSX10_5_7'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT5.1'
'IntelMacOSX10_6_1'
'WindowsNT6.0'
'WindowsNT5.1'
'IntelMacOSX10.5'
'WindowsNT6.0'
'IntelMacOSX10_6'
'IntelMacOSX10_5_8'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT6.1'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT6.0'
'WindowsNT6.1'
'WindowsNT6.1'
'WindowsNT6.1'
'WindowsNT5.1'
'WindowsNT6.0'
'WindowsNT5.1'
'IntelMacOSX10_6_2'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT5.1'
'WindowsNT5.1'

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 1 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=125243&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=125243&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=125243&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 time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Frequency Table (Categorical Data)
CategoryAbs. FrequencyRel. Frequency
IntelMacOSX10.510.0072
IntelMacOSX10_5_710.0072
IntelMacOSX10_5_840.0288
IntelMacOSX10_610.0072
IntelMacOSX10_6_110.0072
IntelMacOSX10_6_240.0288
Linuxi68610.0072
WindowsNT5.1520.3741
WindowsNT6.0650.4676
WindowsNT6.190.0647

\begin{tabular}{lllllllll}
\hline
Frequency Table (Categorical Data) \tabularnewline
Category & Abs. Frequency & Rel. Frequency \tabularnewline
IntelMacOSX10.5 & 1 & 0.0072 \tabularnewline
IntelMacOSX10_5_7 & 1 & 0.0072 \tabularnewline
IntelMacOSX10_5_8 & 4 & 0.0288 \tabularnewline
IntelMacOSX10_6 & 1 & 0.0072 \tabularnewline
IntelMacOSX10_6_1 & 1 & 0.0072 \tabularnewline
IntelMacOSX10_6_2 & 4 & 0.0288 \tabularnewline
Linuxi686 & 1 & 0.0072 \tabularnewline
WindowsNT5.1 & 52 & 0.3741 \tabularnewline
WindowsNT6.0 & 65 & 0.4676 \tabularnewline
WindowsNT6.1 & 9 & 0.0647 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=125243&T=1

[TABLE]
[ROW][C]Frequency Table (Categorical Data)[/C][/ROW]
[ROW][C]Category[/C][C]Abs. Frequency[/C][C]Rel. Frequency[/C][/ROW]
[ROW][C]IntelMacOSX10.5[/C][C]1[/C][C]0.0072[/C][/ROW]
[ROW][C]IntelMacOSX10_5_7[/C][C]1[/C][C]0.0072[/C][/ROW]
[ROW][C]IntelMacOSX10_5_8[/C][C]4[/C][C]0.0288[/C][/ROW]
[ROW][C]IntelMacOSX10_6[/C][C]1[/C][C]0.0072[/C][/ROW]
[ROW][C]IntelMacOSX10_6_1[/C][C]1[/C][C]0.0072[/C][/ROW]
[ROW][C]IntelMacOSX10_6_2[/C][C]4[/C][C]0.0288[/C][/ROW]
[ROW][C]Linuxi686[/C][C]1[/C][C]0.0072[/C][/ROW]
[ROW][C]WindowsNT5.1[/C][C]52[/C][C]0.3741[/C][/ROW]
[ROW][C]WindowsNT6.0[/C][C]65[/C][C]0.4676[/C][/ROW]
[ROW][C]WindowsNT6.1[/C][C]9[/C][C]0.0647[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=125243&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=125243&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 (Categorical Data)
CategoryAbs. FrequencyRel. Frequency
IntelMacOSX10.510.0072
IntelMacOSX10_5_710.0072
IntelMacOSX10_5_840.0288
IntelMacOSX10_610.0072
IntelMacOSX10_6_110.0072
IntelMacOSX10_6_240.0288
Linuxi68610.0072
WindowsNT5.1520.3741
WindowsNT6.0650.4676
WindowsNT6.190.0647


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):
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 {
plot(mytab <- table(x),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,hyperlink('histogram.htm','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')
}