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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, 17 Dec 2016 12:20:48 +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/2016/Dec/17/t14819736746vc98ghfv0znr6j.htm/, Retrieved Fri, 01 Nov 2024 03:32:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300702, Retrieved Fri, 01 Nov 2024 03:32:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact65

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [N170 histogram en...] [2016-12-17 11:20:48] [63af9ed1c5670c0e3049894fd77d93e0] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5610
3530
2370
11610
4630
8760
4130
5400
5630
7050
5620
5510
4240
3620
4220
3480
3400
4830
3060
8030
8480
7270
10030
7810
6470
5150
4580
2640
2180
6250
4310
6160
8560
6250
8940
8040
6290
2630
4760
3820
2350
2420
4780
6120
4290
5540
6120
5110
4800
2670
5120
2370
3280
4090
2250
2520
3670
6440
5490
2000
2130
1210
6770
2380
2380
3760
3860
4590
4580
8030
5880
1770
5440
4090
3360
1240
1890
3390
2980
5030
3720
3530
1620
2290
2050
2070
2760
1500
1850
2760
3360
2120
3970
3760
3630
1850
3160
2700
1080
1150
1230
2180
2310
2940
4370
4750
7810
2880

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=300702&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=300702&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300702&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






Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[1000,2000[1500110.1018520.1018520.000102
[2000,3000[2500260.2407410.3425930.000241
[3000,4000[3500190.1759260.5185190.000176
[4000,5000[4500170.1574070.6759260.000157
[5000,6000[5500130.120370.7962960.00012
[6000,7000[650090.0833330.879638.3e-05
[7000,8000[750040.0370370.9166673.7e-05
[8000,9000[850070.0648150.9814816.5e-05
[9000,10000[9500000.9814810
[10000,11000[1050010.0092590.9907419e-06
[11000,12000]1150010.00925919e-06

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[1000,2000[ & 1500 & 11 & 0.101852 & 0.101852 & 0.000102 \tabularnewline
[2000,3000[ & 2500 & 26 & 0.240741 & 0.342593 & 0.000241 \tabularnewline
[3000,4000[ & 3500 & 19 & 0.175926 & 0.518519 & 0.000176 \tabularnewline
[4000,5000[ & 4500 & 17 & 0.157407 & 0.675926 & 0.000157 \tabularnewline
[5000,6000[ & 5500 & 13 & 0.12037 & 0.796296 & 0.00012 \tabularnewline
[6000,7000[ & 6500 & 9 & 0.083333 & 0.87963 & 8.3e-05 \tabularnewline
[7000,8000[ & 7500 & 4 & 0.037037 & 0.916667 & 3.7e-05 \tabularnewline
[8000,9000[ & 8500 & 7 & 0.064815 & 0.981481 & 6.5e-05 \tabularnewline
[9000,10000[ & 9500 & 0 & 0 & 0.981481 & 0 \tabularnewline
[10000,11000[ & 10500 & 1 & 0.009259 & 0.990741 & 9e-06 \tabularnewline
[11000,12000] & 11500 & 1 & 0.009259 & 1 & 9e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300702&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][1000,2000[[/C][C]1500[/C][C]11[/C][C]0.101852[/C][C]0.101852[/C][C]0.000102[/C][/ROW]
[ROW][C][2000,3000[[/C][C]2500[/C][C]26[/C][C]0.240741[/C][C]0.342593[/C][C]0.000241[/C][/ROW]
[ROW][C][3000,4000[[/C][C]3500[/C][C]19[/C][C]0.175926[/C][C]0.518519[/C][C]0.000176[/C][/ROW]
[ROW][C][4000,5000[[/C][C]4500[/C][C]17[/C][C]0.157407[/C][C]0.675926[/C][C]0.000157[/C][/ROW]
[ROW][C][5000,6000[[/C][C]5500[/C][C]13[/C][C]0.12037[/C][C]0.796296[/C][C]0.00012[/C][/ROW]
[ROW][C][6000,7000[[/C][C]6500[/C][C]9[/C][C]0.083333[/C][C]0.87963[/C][C]8.3e-05[/C][/ROW]
[ROW][C][7000,8000[[/C][C]7500[/C][C]4[/C][C]0.037037[/C][C]0.916667[/C][C]3.7e-05[/C][/ROW]
[ROW][C][8000,9000[[/C][C]8500[/C][C]7[/C][C]0.064815[/C][C]0.981481[/C][C]6.5e-05[/C][/ROW]
[ROW][C][9000,10000[[/C][C]9500[/C][C]0[/C][C]0[/C][C]0.981481[/C][C]0[/C][/ROW]
[ROW][C][10000,11000[[/C][C]10500[/C][C]1[/C][C]0.009259[/C][C]0.990741[/C][C]9e-06[/C][/ROW]
[ROW][C][11000,12000][/C][C]11500[/C][C]1[/C][C]0.009259[/C][C]1[/C][C]9e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300702&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300702&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
[1000,2000[1500110.1018520.1018520.000102
[2000,3000[2500260.2407410.3425930.000241
[3000,4000[3500190.1759260.5185190.000176
[4000,5000[4500170.1574070.6759260.000157
[5000,6000[5500130.120370.7962960.00012
[6000,7000[650090.0833330.879638.3e-05
[7000,8000[750040.0370370.9166673.7e-05
[8000,9000[850070.0648150.9814816.5e-05
[9000,10000[9500000.9814810
[10000,11000[1050010.0092590.9907419e-06
[11000,12000]1150010.00925919e-06


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
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,'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')
}