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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, 04 Oct 2011 06:18:15 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Oct/04/t1317723595j79aeo8uqy3exyl.htm/, Retrieved Thu, 09 May 2024 10:11:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=125742, Retrieved Thu, 09 May 2024 10:11:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Mean Plot] [workshop 3] [2007-10-26 12:14:28] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F RM D  [Kendall tau Correlation Matrix] [] [2008-11-06 23:58:35] [13aa3097ce898f117c0eb9b0e4ce3070]
- RMPD    [Notched Boxplots] [notched boxplot] [2011-10-04 08:02:01] [e32f7fcc4522d286f7101d32ccf9e2fd]
- RMPD        [Histogram] [median] [2011-10-04 10:18:15] [05d3841c0e91f0207133db830e88168b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13328
12873
14000
13477
14237
13674
13529
14058
12975
14326
14008
16193
14483
14011
15057
14884
15414
14440
14900
15074
14442
15307
14938
17193
15528
14765
15838
15723
16150
15486
15986
15983
15692
16490
15686
18897
16316
15636
17163
16534
16518
16375
16290
16352
15943
16362
16393
19051
16747
16320
17910
16961
17480
17049
16879
17473
16998
17307
17418
20169
17871
17226
19062
17804
19100
18522
18060
18869
18127
18871
18890
21263
19547
18450
20254
19240
20216
19420
19415
20018
18652
19978
19509
21971

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'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=125742&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=125742&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=125742&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'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[12000,13000[1250020.023810.023812.4e-05
[13000,14000[1350040.0476190.0714294.8e-05
[14000,15000[14500130.1547620.226190.000155
[15000,16000[15500140.1666670.3928570.000167
[16000,17000[16500160.1904760.5833330.00019
[17000,18000[17500110.1309520.7142860.000131
[18000,19000[1850090.1071430.8214290.000107
[19000,20000[1950090.1071430.9285710.000107
[20000,21000[2050040.0476190.976194.8e-05
[21000,22000]2150020.0238112.4e-05

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[12000,13000[ & 12500 & 2 & 0.02381 & 0.02381 & 2.4e-05 \tabularnewline
[13000,14000[ & 13500 & 4 & 0.047619 & 0.071429 & 4.8e-05 \tabularnewline
[14000,15000[ & 14500 & 13 & 0.154762 & 0.22619 & 0.000155 \tabularnewline
[15000,16000[ & 15500 & 14 & 0.166667 & 0.392857 & 0.000167 \tabularnewline
[16000,17000[ & 16500 & 16 & 0.190476 & 0.583333 & 0.00019 \tabularnewline
[17000,18000[ & 17500 & 11 & 0.130952 & 0.714286 & 0.000131 \tabularnewline
[18000,19000[ & 18500 & 9 & 0.107143 & 0.821429 & 0.000107 \tabularnewline
[19000,20000[ & 19500 & 9 & 0.107143 & 0.928571 & 0.000107 \tabularnewline
[20000,21000[ & 20500 & 4 & 0.047619 & 0.97619 & 4.8e-05 \tabularnewline
[21000,22000] & 21500 & 2 & 0.02381 & 1 & 2.4e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=125742&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][12000,13000[[/C][C]12500[/C][C]2[/C][C]0.02381[/C][C]0.02381[/C][C]2.4e-05[/C][/ROW]
[ROW][C][13000,14000[[/C][C]13500[/C][C]4[/C][C]0.047619[/C][C]0.071429[/C][C]4.8e-05[/C][/ROW]
[ROW][C][14000,15000[[/C][C]14500[/C][C]13[/C][C]0.154762[/C][C]0.22619[/C][C]0.000155[/C][/ROW]
[ROW][C][15000,16000[[/C][C]15500[/C][C]14[/C][C]0.166667[/C][C]0.392857[/C][C]0.000167[/C][/ROW]
[ROW][C][16000,17000[[/C][C]16500[/C][C]16[/C][C]0.190476[/C][C]0.583333[/C][C]0.00019[/C][/ROW]
[ROW][C][17000,18000[[/C][C]17500[/C][C]11[/C][C]0.130952[/C][C]0.714286[/C][C]0.000131[/C][/ROW]
[ROW][C][18000,19000[[/C][C]18500[/C][C]9[/C][C]0.107143[/C][C]0.821429[/C][C]0.000107[/C][/ROW]
[ROW][C][19000,20000[[/C][C]19500[/C][C]9[/C][C]0.107143[/C][C]0.928571[/C][C]0.000107[/C][/ROW]
[ROW][C][20000,21000[[/C][C]20500[/C][C]4[/C][C]0.047619[/C][C]0.97619[/C][C]4.8e-05[/C][/ROW]
[ROW][C][21000,22000][/C][C]21500[/C][C]2[/C][C]0.02381[/C][C]1[/C][C]2.4e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=125742&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=125742&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
[12000,13000[1250020.023810.023812.4e-05
[13000,14000[1350040.0476190.0714294.8e-05
[14000,15000[14500130.1547620.226190.000155
[15000,16000[15500140.1666670.3928570.000167
[16000,17000[16500160.1904760.5833330.00019
[17000,18000[17500110.1309520.7142860.000131
[18000,19000[1850090.1071430.8214290.000107
[19000,20000[1950090.1071430.9285710.000107
[20000,21000[2050040.0476190.976194.8e-05
[21000,22000]2150020.0238112.4e-05


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 80 ; par3 = 1e-08 ;
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 {
plot(mytab <- table(x),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')
}