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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 computationTue, 19 Dec 2017 00:57:15 +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/2017/Dec/19/t15136415727egibelkojrk6oa.htm/, Retrieved Wed, 15 May 2024 14:03:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310261, Retrieved Wed, 15 May 2024 14:03:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Histogram] [Frequentietabel +...] [2017-12-18 23:57:15] [eea26683481d1970ffe2162bfa111b12] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
'KY '
'CA '
'MT '
'OK '
'CT '
'IL '
'KS '
'LA '
'NJ '
'NJ '
'TX '
'IA '
'WY '
'WY '
'TX '
'NE '
'WV '
'NJ '
'TX '
'NY '
'MS '
'TX '
'TX '
'WV '
'IA '
'AL '
'TX '
'TX '
'UT '
'IA '
'PA '
'TX '
'AR '
'AR '
'KS '
'CO '
'CO '
'MT '
'IN '
'MO '
'MO '
'IL '
'TX '
'NE '
'CT '
'CO '
'CO '
'WA '
'CA '
'TX '
'OR '
'OR '
'WA '
'WA '
'OH '
'OH '
'AL '
'KS '
'AZ '
'TN '
'SC '
'TX '
'AR '
'ND '
'ND '
'OK '
'LA '
'NE '
'IN '
'NE '
'MN '
'IL '
'FL '
'FL '
'TX '
'TX '
'AL '
'CA '
'CO '
'KS '
'IL '
'IL '
'GA '
'GA '
'KS '
'IL '
'IL '
'IN '
'TN '
'TX '
'TX '
'NE '
'NE '
'AK '
'CA '
'CA '
'TX '
'NY '
'NY '
'NY '
'IL '
'IL '
'IL '
'NY '
'ID '
'TX '
'TX '
'IL '
'OR '
'FL '
'IA '
'TX '
'TX '
'NM '
'KY '
'KY '
'WV '
'WV '
'GA '
'OH '

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=310261&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=310261&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310261&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 (Categorical Data)
CategoryAbs. FrequencyRel. Frequency
TX200.1667
IL110.0917
NE60.05
CA50.0417
CO50.0417
KS50.0417
NY50.0417
IA40.0333
WV40.0333
AL30.025
AR30.025
FL30.025
GA30.025
IN30.025
KY30.025
NJ30.025
OH30.025
OR30.025
WA30.025
CT20.0167
LA20.0167
MO20.0167
MT20.0167
ND20.0167
OK20.0167
TN20.0167
WY20.0167
AK10.0083
AZ10.0083
ID10.0083
MN10.0083
MS10.0083
NM10.0083
PA10.0083
SC10.0083
UT10.0083

\begin{tabular}{lllllllll}
\hline
Frequency Table (Categorical Data) \tabularnewline
Category & Abs. Frequency & Rel. Frequency \tabularnewline
TX & 20 & 0.1667 \tabularnewline
IL & 11 & 0.0917 \tabularnewline
NE & 6 & 0.05 \tabularnewline
CA & 5 & 0.0417 \tabularnewline
CO & 5 & 0.0417 \tabularnewline
KS & 5 & 0.0417 \tabularnewline
NY & 5 & 0.0417 \tabularnewline
IA & 4 & 0.0333 \tabularnewline
WV & 4 & 0.0333 \tabularnewline
AL & 3 & 0.025 \tabularnewline
AR & 3 & 0.025 \tabularnewline
FL & 3 & 0.025 \tabularnewline
GA & 3 & 0.025 \tabularnewline
IN & 3 & 0.025 \tabularnewline
KY & 3 & 0.025 \tabularnewline
NJ & 3 & 0.025 \tabularnewline
OH & 3 & 0.025 \tabularnewline
OR & 3 & 0.025 \tabularnewline
WA & 3 & 0.025 \tabularnewline
CT & 2 & 0.0167 \tabularnewline
LA & 2 & 0.0167 \tabularnewline
MO & 2 & 0.0167 \tabularnewline
MT & 2 & 0.0167 \tabularnewline
ND & 2 & 0.0167 \tabularnewline
OK & 2 & 0.0167 \tabularnewline
TN & 2 & 0.0167 \tabularnewline
WY & 2 & 0.0167 \tabularnewline
AK & 1 & 0.0083 \tabularnewline
AZ & 1 & 0.0083 \tabularnewline
ID & 1 & 0.0083 \tabularnewline
MN & 1 & 0.0083 \tabularnewline
MS & 1 & 0.0083 \tabularnewline
NM & 1 & 0.0083 \tabularnewline
PA & 1 & 0.0083 \tabularnewline
SC & 1 & 0.0083 \tabularnewline
UT & 1 & 0.0083 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310261&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]TX[/C][C]20[/C][C]0.1667[/C][/ROW]
[ROW][C]IL[/C][C]11[/C][C]0.0917[/C][/ROW]
[ROW][C]NE[/C][C]6[/C][C]0.05[/C][/ROW]
[ROW][C]CA[/C][C]5[/C][C]0.0417[/C][/ROW]
[ROW][C]CO[/C][C]5[/C][C]0.0417[/C][/ROW]
[ROW][C]KS[/C][C]5[/C][C]0.0417[/C][/ROW]
[ROW][C]NY[/C][C]5[/C][C]0.0417[/C][/ROW]
[ROW][C]IA[/C][C]4[/C][C]0.0333[/C][/ROW]
[ROW][C]WV[/C][C]4[/C][C]0.0333[/C][/ROW]
[ROW][C]AL[/C][C]3[/C][C]0.025[/C][/ROW]
[ROW][C]AR[/C][C]3[/C][C]0.025[/C][/ROW]
[ROW][C]FL[/C][C]3[/C][C]0.025[/C][/ROW]
[ROW][C]GA[/C][C]3[/C][C]0.025[/C][/ROW]
[ROW][C]IN[/C][C]3[/C][C]0.025[/C][/ROW]
[ROW][C]KY[/C][C]3[/C][C]0.025[/C][/ROW]
[ROW][C]NJ[/C][C]3[/C][C]0.025[/C][/ROW]
[ROW][C]OH[/C][C]3[/C][C]0.025[/C][/ROW]
[ROW][C]OR[/C][C]3[/C][C]0.025[/C][/ROW]
[ROW][C]WA[/C][C]3[/C][C]0.025[/C][/ROW]
[ROW][C]CT[/C][C]2[/C][C]0.0167[/C][/ROW]
[ROW][C]LA[/C][C]2[/C][C]0.0167[/C][/ROW]
[ROW][C]MO[/C][C]2[/C][C]0.0167[/C][/ROW]
[ROW][C]MT[/C][C]2[/C][C]0.0167[/C][/ROW]
[ROW][C]ND[/C][C]2[/C][C]0.0167[/C][/ROW]
[ROW][C]OK[/C][C]2[/C][C]0.0167[/C][/ROW]
[ROW][C]TN[/C][C]2[/C][C]0.0167[/C][/ROW]
[ROW][C]WY[/C][C]2[/C][C]0.0167[/C][/ROW]
[ROW][C]AK[/C][C]1[/C][C]0.0083[/C][/ROW]
[ROW][C]AZ[/C][C]1[/C][C]0.0083[/C][/ROW]
[ROW][C]ID[/C][C]1[/C][C]0.0083[/C][/ROW]
[ROW][C]MN[/C][C]1[/C][C]0.0083[/C][/ROW]
[ROW][C]MS[/C][C]1[/C][C]0.0083[/C][/ROW]
[ROW][C]NM[/C][C]1[/C][C]0.0083[/C][/ROW]
[ROW][C]PA[/C][C]1[/C][C]0.0083[/C][/ROW]
[ROW][C]SC[/C][C]1[/C][C]0.0083[/C][/ROW]
[ROW][C]UT[/C][C]1[/C][C]0.0083[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310261&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310261&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
TX200.1667
IL110.0917
NE60.05
CA50.0417
CO50.0417
KS50.0417
NY50.0417
IA40.0333
WV40.0333
AL30.025
AR30.025
FL30.025
GA30.025
IN30.025
KY30.025
NJ30.025
OH30.025
OR30.025
WA30.025
CT20.0167
LA20.0167
MO20.0167
MT20.0167
ND20.0167
OK20.0167
TN20.0167
WY20.0167
AK10.0083
AZ10.0083
ID10.0083
MN10.0083
MS10.0083
NM10.0083
PA10.0083
SC10.0083
UT10.0083


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,'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')
}