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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 computationTue, 23 Feb 2016 14:15:26 +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/2016/Feb/23/t1456236975u9huhp9ohrhurbf.htm/, Retrieved Sun, 05 May 2024 23:13:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=292522, Retrieved Sun, 05 May 2024 23:13:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact127

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2016-02-09 17:41:50] [74be16979710d4c4e7c6647856088456]
- RMP   [Histogram] [Frequentietabel: ...] [2016-02-09 17:51:04] [74be16979710d4c4e7c6647856088456]
- R  D    [Histogram] [] [2016-02-15 23:43:07] [74be16979710d4c4e7c6647856088456]
- R  D        [Histogram] [] [2016-02-23 14:15:26] [73c24565f080d314e595da727a2003f4] [Current]
- RMP           [Kernel Density Estimation] [] [2016-02-23 14:42:17] [eb84577074ca016fc26a8bcabc390030]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
89.72
89.95
90.19
90.23
90.32
90.86
90.99
90.98
91.22
91.42
91.55
91.67
92.30
92.92
93.10
93.23
93.36
93.42
93.58
93.68
94.02
94.29
94.54
94.64
96.70
96.83
97.07
97.11
97.42
97.44
97.67
97.84
98.17
98.31
98.42
98.44
98.89
99.26
99.59
99.82
99.95
99.99
100.28
100.38
100.46
100.52
100.43
100.44
101.33
101.43
101.41
101.53
101.58
101.73
102.12
101.86
101.93
101.86
101.92
102.02

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'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 & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=292522&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=292522&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=292522&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[88,90[8920.0333330.0333330.016667
[90,92[91100.1666670.20.083333
[92,94[9380.1333330.3333330.066667
[94,96[9540.0666670.40.033333
[96,98[9780.1333330.5333330.066667
[98,100[99100.1666670.70.083333
[100,102[101160.2666670.9666670.133333
[102,104]10320.03333310.016667

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[88,90[ & 89 & 2 & 0.033333 & 0.033333 & 0.016667 \tabularnewline
[90,92[ & 91 & 10 & 0.166667 & 0.2 & 0.083333 \tabularnewline
[92,94[ & 93 & 8 & 0.133333 & 0.333333 & 0.066667 \tabularnewline
[94,96[ & 95 & 4 & 0.066667 & 0.4 & 0.033333 \tabularnewline
[96,98[ & 97 & 8 & 0.133333 & 0.533333 & 0.066667 \tabularnewline
[98,100[ & 99 & 10 & 0.166667 & 0.7 & 0.083333 \tabularnewline
[100,102[ & 101 & 16 & 0.266667 & 0.966667 & 0.133333 \tabularnewline
[102,104] & 103 & 2 & 0.033333 & 1 & 0.016667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=292522&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][88,90[[/C][C]89[/C][C]2[/C][C]0.033333[/C][C]0.033333[/C][C]0.016667[/C][/ROW]
[ROW][C][90,92[[/C][C]91[/C][C]10[/C][C]0.166667[/C][C]0.2[/C][C]0.083333[/C][/ROW]
[ROW][C][92,94[[/C][C]93[/C][C]8[/C][C]0.133333[/C][C]0.333333[/C][C]0.066667[/C][/ROW]
[ROW][C][94,96[[/C][C]95[/C][C]4[/C][C]0.066667[/C][C]0.4[/C][C]0.033333[/C][/ROW]
[ROW][C][96,98[[/C][C]97[/C][C]8[/C][C]0.133333[/C][C]0.533333[/C][C]0.066667[/C][/ROW]
[ROW][C][98,100[[/C][C]99[/C][C]10[/C][C]0.166667[/C][C]0.7[/C][C]0.083333[/C][/ROW]
[ROW][C][100,102[[/C][C]101[/C][C]16[/C][C]0.266667[/C][C]0.966667[/C][C]0.133333[/C][/ROW]
[ROW][C][102,104][/C][C]103[/C][C]2[/C][C]0.033333[/C][C]1[/C][C]0.016667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=292522&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=292522&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
[88,90[8920.0333330.0333330.016667
[90,92[91100.1666670.20.083333
[92,94[9380.1333330.3333330.066667
[94,96[9540.0666670.40.033333
[96,98[9780.1333330.5333330.066667
[98,100[99100.1666670.70.083333
[100,102[101160.2666670.9666670.133333
[102,104]10320.03333310.016667


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 {
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,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')
}