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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, 13 Aug 2017 12:12:22 +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/13/t1502619237qiadpyk37jkcivi.htm/, Retrieved Thu, 09 May 2024 21:01:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=307157, Retrieved Thu, 09 May 2024 21:01:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [] [2017-08-13 10:12:22] [270a72b021b4bbf70c885af1fd2608d6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14741900
14195900
15014900
12011900
15560900
15287900
16379900
16925900
18836900
16379900
15560900
19382900
16379900
12284900
14468900
10919900
15287900
12557900
16652900
15014900
15833900
17744900
17471900
20747900
15014900
12557900
13922900
10100900
14468900
11192900
15833900
15014900
13376900
19109900
17198900
19655900
14741900
13649900
12284900
10100900
13376900
12011900
16379900
15833900
13649900
18290900
16925900
21839900
17471900
10646900
10646900
10646900
12557900
12557900
16925900
15560900
13922900
17471900
16106900
23204900
18290900
10646900
11192900
9281900
12830900
14741900
18563900
18290900
14741900
17198900
15287900
21839900
16652900
13376900
12011900
9008900
13376900
16106900
18836900
17744900
13103900
18836900
14741900
22658900
18836900
13649900
12557900
8462900
13376900
12830900
19382900
19382900
14741900
19109900
14195900
22112900
18836900
13922900
10646900
7370900
14468900
13922900
18290900
21020900
15560900
17471900
13103900
22658900

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=307157&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=307157&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307157&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
[6000000,8000000[7e+0610.0092590.0092590
[8000000,10000000[9e+0630.0277780.0370370
[10000000,12000000[1.1e+07100.0925930.129630
[12000000,14000000[1.3e+07260.2407410.370370
[14000000,16000000[1.5e+07250.2314810.6018520
[16000000,18000000[1.7e+07190.1759260.7777780
[18000000,20000000[1.9e+07160.1481480.9259260
[20000000,22000000[2.1e+0740.0370370.9629630
[22000000,24000000]2.3e+0740.03703710

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[6000000,8000000[ & 7e+06 & 1 & 0.009259 & 0.009259 & 0 \tabularnewline
[8000000,10000000[ & 9e+06 & 3 & 0.027778 & 0.037037 & 0 \tabularnewline
[10000000,12000000[ & 1.1e+07 & 10 & 0.092593 & 0.12963 & 0 \tabularnewline
[12000000,14000000[ & 1.3e+07 & 26 & 0.240741 & 0.37037 & 0 \tabularnewline
[14000000,16000000[ & 1.5e+07 & 25 & 0.231481 & 0.601852 & 0 \tabularnewline
[16000000,18000000[ & 1.7e+07 & 19 & 0.175926 & 0.777778 & 0 \tabularnewline
[18000000,20000000[ & 1.9e+07 & 16 & 0.148148 & 0.925926 & 0 \tabularnewline
[20000000,22000000[ & 2.1e+07 & 4 & 0.037037 & 0.962963 & 0 \tabularnewline
[22000000,24000000] & 2.3e+07 & 4 & 0.037037 & 1 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=307157&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][6000000,8000000[[/C][C]7e+06[/C][C]1[/C][C]0.009259[/C][C]0.009259[/C][C]0[/C][/ROW]
[ROW][C][8000000,10000000[[/C][C]9e+06[/C][C]3[/C][C]0.027778[/C][C]0.037037[/C][C]0[/C][/ROW]
[ROW][C][10000000,12000000[[/C][C]1.1e+07[/C][C]10[/C][C]0.092593[/C][C]0.12963[/C][C]0[/C][/ROW]
[ROW][C][12000000,14000000[[/C][C]1.3e+07[/C][C]26[/C][C]0.240741[/C][C]0.37037[/C][C]0[/C][/ROW]
[ROW][C][14000000,16000000[[/C][C]1.5e+07[/C][C]25[/C][C]0.231481[/C][C]0.601852[/C][C]0[/C][/ROW]
[ROW][C][16000000,18000000[[/C][C]1.7e+07[/C][C]19[/C][C]0.175926[/C][C]0.777778[/C][C]0[/C][/ROW]
[ROW][C][18000000,20000000[[/C][C]1.9e+07[/C][C]16[/C][C]0.148148[/C][C]0.925926[/C][C]0[/C][/ROW]
[ROW][C][20000000,22000000[[/C][C]2.1e+07[/C][C]4[/C][C]0.037037[/C][C]0.962963[/C][C]0[/C][/ROW]
[ROW][C][22000000,24000000][/C][C]2.3e+07[/C][C]4[/C][C]0.037037[/C][C]1[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=307157&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=307157&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
[6000000,8000000[7e+0610.0092590.0092590
[8000000,10000000[9e+0630.0277780.0370370
[10000000,12000000[1.1e+07100.0925930.129630
[12000000,14000000[1.3e+07260.2407410.370370
[14000000,16000000[1.5e+07250.2314810.6018520
[16000000,18000000[1.7e+07190.1759260.7777780
[18000000,20000000[1.9e+07160.1481480.9259260
[20000000,22000000[2.1e+0740.0370370.9629630
[22000000,24000000]2.3e+0740.03703710


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
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')
}