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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 computationMon, 04 Oct 2010 16:48:50 +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/2010/Oct/04/t128621087054yp03o27ie3q9l.htm/, Retrieved Sun, 28 Apr 2024 17:30:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=80661, Retrieved Sun, 28 Apr 2024 17:30:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Histogram] [Bad example of Hi...] [2010-09-25 09:28:23] [b98453cac15ba1066b407e146608df68]
F   PD    [Histogram] [Histogram - Time ...] [2010-10-04 16:48:50] [6e647d331a8f33aa61a2d78ef323178e] [Current]
-   P       [Histogram] [Task 3 - solution] [2010-10-08 11:37:43] [9894f466352df31a128e82ec8d720241]
Feedback Forum
2010-10-07 16:22:17 [Jolin Verept] [reply] 
Oplossing 1 werd niet gevonden. De oplossing wordt hier bekomen door de intervallen aan te passen naar 30. Door het vergroten van de intervallen kunnen we preciezer zien wat de normale tijd is. We kunnen zien dat het interval [200, 300[ de hoogste frequentie heeft, namelijk 54. We kunnen besluiten dat de normale tijd die nodig is om de enquête te vervullen 250 seconden (midpoint) bedraagt.

De link van oplossing 1 vind je hier: http://www.freestatistics.org/blog/index.php?v=date/2010/Oct/02/t1286015497pvxe0pmg6w2f124.htm/

Oplossing 2 werd niet uitgevoerd. We kunnen in de frequentietabel zien dat er uitschieters aanwezig zijn. Deze uitschieters beïnvloeden echter het resultaat en daarom is het nodig deze uitschieters te verwijderen. We kunnen dit aan de hand van het invoeren van de code x<-x[x<700[ in 'R Code' of door manueel de uitschieters te verwijderen uit de gegevensverzameling / data. Deze methode laat blijken dat de normale tijd voor het vervullen van de enquête 210 seconden bedraagt. Dit kunnen we vaststellen aan het interval [200,220[ die de hoogste frequentie heeft, namelijk 16.

De link van oplossing 2 vind je hieronder:

http://www.freestatistics.org/blog/index.php?v=date/2010/Oct/07/t128644997750zx1oon6cvxhz2.htm/
2010-10-11 16:43:02 [07e9eb4976a13216fde13362eef7fcc8] [reply] 
De Voorgestelde oplossingen van Jolin zijn correct. Al zou ik bij voorbeeld 1 opteren om minder kolommen te gebruiken. Meer groeperen is hier duidelijker omdat je resultaten duidelijk 'blokken' vertonen.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
426.113
383.703
232.444
70.939
226.731
158.047
33.999
37.028
388.3
392.25
180.818
198.296
217.465
275.562
57.47
136.452
213.361
274.482
220.553
236.71
260.642
213.923
169.861
403.064
449.594
406.167
206.893
156.187
257.102
62.156
251.422
171.328
350.089
221.588
4.813
183.186
190.379
223.166
232.669
356.725
109.215
475.834
315.955
8.95
278.741
308.16
207.533
192.797
289.714
293.671
386.688
85.094
131.812
197.549
308.174
86.58
242.205
238.502
187.881
140.321
440.31
421.403
218.761
137.55
262.517
348.821
150.034
64.016
261.596
259.7
171.26
203.077
249.148
211.655
252.64
438.555
239.89
401.915
216.886
184.641
380.155
313.906
366.936
236.302
229.641
235.577
103.898
263.906
241.171
216.548
295.281
193.299
204.386
257.567
136.813
240.755
59.609
213.511
380.531
242.344
250.407
183.613
191.835
266.793
246.542
330.563
403.556
208.108
324.04
308.532
199.297
200.156
262.875
287.069
190.157
199.746
265.777
435.956
72.844
206.771
401.422
216.046
39.047
441.437

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80661&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=80661&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80661&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'George Udny Yule' @ 72.249.76.132






Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[0,50[2550.0403230.0403230.000806
[50,100[7580.0645160.1048390.00129
[100,150[12570.0564520.161290.001129
[150,200[175200.161290.3225810.003226
[200,250[225340.2741940.5967740.005484
[250,300[275200.161290.7580650.003226
[300,350[32580.0645160.8225810.00129
[350,400[37590.0725810.8951610.001452
[400,450[425120.0967740.9919350.001935
[450,500]47510.00806510.000161

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[0,50[ & 25 & 5 & 0.040323 & 0.040323 & 0.000806 \tabularnewline
[50,100[ & 75 & 8 & 0.064516 & 0.104839 & 0.00129 \tabularnewline
[100,150[ & 125 & 7 & 0.056452 & 0.16129 & 0.001129 \tabularnewline
[150,200[ & 175 & 20 & 0.16129 & 0.322581 & 0.003226 \tabularnewline
[200,250[ & 225 & 34 & 0.274194 & 0.596774 & 0.005484 \tabularnewline
[250,300[ & 275 & 20 & 0.16129 & 0.758065 & 0.003226 \tabularnewline
[300,350[ & 325 & 8 & 0.064516 & 0.822581 & 0.00129 \tabularnewline
[350,400[ & 375 & 9 & 0.072581 & 0.895161 & 0.001452 \tabularnewline
[400,450[ & 425 & 12 & 0.096774 & 0.991935 & 0.001935 \tabularnewline
[450,500] & 475 & 1 & 0.008065 & 1 & 0.000161 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80661&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][0,50[[/C][C]25[/C][C]5[/C][C]0.040323[/C][C]0.040323[/C][C]0.000806[/C][/ROW]
[ROW][C][50,100[[/C][C]75[/C][C]8[/C][C]0.064516[/C][C]0.104839[/C][C]0.00129[/C][/ROW]
[ROW][C][100,150[[/C][C]125[/C][C]7[/C][C]0.056452[/C][C]0.16129[/C][C]0.001129[/C][/ROW]
[ROW][C][150,200[[/C][C]175[/C][C]20[/C][C]0.16129[/C][C]0.322581[/C][C]0.003226[/C][/ROW]
[ROW][C][200,250[[/C][C]225[/C][C]34[/C][C]0.274194[/C][C]0.596774[/C][C]0.005484[/C][/ROW]
[ROW][C][250,300[[/C][C]275[/C][C]20[/C][C]0.16129[/C][C]0.758065[/C][C]0.003226[/C][/ROW]
[ROW][C][300,350[[/C][C]325[/C][C]8[/C][C]0.064516[/C][C]0.822581[/C][C]0.00129[/C][/ROW]
[ROW][C][350,400[[/C][C]375[/C][C]9[/C][C]0.072581[/C][C]0.895161[/C][C]0.001452[/C][/ROW]
[ROW][C][400,450[[/C][C]425[/C][C]12[/C][C]0.096774[/C][C]0.991935[/C][C]0.001935[/C][/ROW]
[ROW][C][450,500][/C][C]475[/C][C]1[/C][C]0.008065[/C][C]1[/C][C]0.000161[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=80661&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80661&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
[0,50[2550.0403230.0403230.000806
[50,100[7580.0645160.1048390.00129
[100,150[12570.0564520.161290.001129
[150,200[175200.161290.3225810.003226
[200,250[225340.2741940.5967740.005484
[250,300[275200.161290.7580650.003226
[300,350[32580.0645160.8225810.00129
[350,400[37590.0725810.8951610.001452
[400,450[425120.0967740.9919350.001935
[450,500]47510.00806510.000161


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 18 ; 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 {
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')
}