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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_histogram.wasp
Title produced by softwareHistogram
Date of computationSun, 06 Aug 2017 12:13:51 +0200
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/Aug/06/t1502014496zi328lictb99f10.htm/, Retrieved Sat, 11 May 2024 05:59:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306945, Retrieved Sat, 11 May 2024 05:59:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [omzet nietjesmacine] [2017-08-06 10:13:51] [ff90ea2d7baa48124a9630d5b785d73f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
€ 133 448,00
€ 132 951,00
€ 132 447,00
€ 131 404,00
€ 141 722,00
€ 141 176,00
€ 133 448,00
€ 128 310,00
€ 128 807,00
€ 128 807,00
€ 129 360,00
€ 130 354,00
€ 131 901,00
€ 131 901,00
€ 130 907,00
€ 128 310,00
€ 141 722,00
€ 143 766,00
€ 140 679,00
€ 133 448,00
€ 136 542,00
€ 131 901,00
€ 133 994,00
€ 134 995,00
€ 136 038,00
€ 133 448,00
€ 133 994,00
€ 130 354,00
€ 141 722,00
€ 145 313,00
€ 142 226,00
€ 136 542,00
€ 142 723,00
€ 136 038,00
€ 142 226,00
€ 141 722,00
€ 143 269,00
€ 137 585,00
€ 143 766,00
€ 143 269,00
€ 152 544,00
€ 150 451,00
€ 142 226,00
€ 138 082,00
€ 143 766,00
€ 136 038,00
€ 141 722,00
€ 142 723,00
€ 144 816,00
€ 140 182,00
€ 142 723,00
€ 144 270,00
€ 149 954,00
€ 145 313,00
€ 139 132,00
€ 132 447,00
€ 138 635,00
€ 121 625,00
€ 129 857,00
€ 134 491,00
€ 139 132,00
€ 132 447,00
€ 132 447,00
€ 132 447,00
€ 136 038,00
€ 130 907,00
€ 124 173,00
€ 118 538,00
€ 122 626,00
€ 106 666,00
€ 116 445,00
€ 122 129,00
€ 123 172,00
€ 117 488,00
€ 117 985,00
€ 116 445,00
€ 121 625,00
€ 117 985,00
€ 110 810,00
€ 105 623,00
€ 114 394,00
€ 95 347,00
€ 107 716,00
€ 113 351,00
€ 113 351,00
€ 106 666,00
€ 100 485,00
€ 99 988,00
€ 105 623,00
€ 100 485,00
€ 90 713,00
€ 83 979,00
€ 91 210,00
€ 74 207,00
€ 89 663,00
€ 97 888,00
€ 100 485,00
€ 94 801,00
€ 87 619,00
€ 92 757,00
€ 94 801,00
€ 93 254,00
€ 77 791,00
€ 70 616,00
€ 75 747,00
€ 60 291,00
€ 76 251,00
€ 81 935,00
€ 86 569,00
€ 78 841,00
€ 71 610,00
€ 75 747,00
€ 77 791,00
€ 73 703,00
€ 58 247,00
€ 51 513,00
€ 57 694,00
€ 40 691,00
€ 59 241,00
€ 70 616,00

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306945&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]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=306945&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306945&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 time0 seconds
R ServerBig Analytics Cloud Computing Center


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 12 ; 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 {
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')
}