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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 computationSun, 03 Oct 2010 16:33:24 +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/03/t1286123793ltoner6o7oerom8.htm/, Retrieved Fri, 03 May 2024 10:31:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=80331, Retrieved Fri, 03 May 2024 10:31:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Histogram] [Task 3: Histogram] [2010-10-03 16:33:24] [ee4a783fb13f41eb2e9bc8a0c4f26279] [Current]
Feedback Forum
2010-10-08 14:41:59 [347d11d64cf4ded9ba0714e7297d928b] [reply] 
Correct(2/2).
De student heeft een correcte methode gebruikt om deze oplossing op te lossen. Een andere methode was om het aantal bins (interval) te veranderen naar 30 of een regel toe te voegen aan de R Code 'x <- x[x<700]' dat ook alle andere waarden weg filtert.
2010-10-09 07:43:43 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
De door de student gekozen methode om deze opgave op te lossen, leidt inderdaad tot een correct histogram en een accurate conclusie.

Zoals reeds aangehaald, werden er inderdaad nog twee andere methoden aangereikt waarvan de snelst inderdaad het wijzigen van de broncode (R-code) naar x <- x[x<700] is; dit kan men doen op basis van de vaststellingen uit het orginele histogram en de opgestelde stem- and- leaf plot.

Een tweede methode is het vergroten van het aantal klassen van het originele histogram. Op deze manier vindt men de outliers en deze kan men dan manueel verwijderen.
2010-10-10 13:03:25 [Nicky Vanwalleghem] [reply] 
De oplossing van de student is correct. Men zou nog een tweede oplossing kunnen gebruiken, namelijk: x <- x[x<700] in de r-code invullen.
2010-10-12 15:45:27 [Jasper Ledeganck] [reply] 
De oplossing van de student is correct. Indien je aan de R-code de formule: x <- x[x<700] toevoegt op de eerste lijn, geeft het Histogram de kolommen weer waarbij alle x-waarden kleiner dan 700 verwerkt zijn. Op deze manier hebben de outliers geen invloed meer op het resultaat. Andere mogelijkheid is het aantal staven van het Histogram te vergroten naar bv. 30, waardoor de staven veel gedetailleerdere resultaten geven.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4.813
8.95
33.999
37.028
39.047
57.47
59.609
62.156
64.016
70.939
72.844
85.094
86.58
103.898
109.215
131.812
136.452
136.813
137.55
140.321
150.034
156.187
158.047
169.861
171.26
171.328
180.818
183.186
183.613
184.641
187.881
190.157
190.379
191.835
192.797
193.299
197.549
198.296
199.297
199.746
200.156
203.077
204.386
206.771
206.893
207.533
208.108
211.655
213.361
213.511
213.923
216.046
216.548
216.886
217.465
218.761
220.553
221.588
223.166
226.731
229.641
232.444
232.669
235.577
236.302
236.71
238.502
239.89
240.755
241.171
242.205
242.344
246.542
249.148
250.407
251.422
252.64
257.102
257.567
259.7
260.642
261.596
262.517
262.875
263.906
265.777
266.793
274.482
275.562
278.741
287.069
289.714
293.671
295.281
308.16
308.174
308.532
313.906
315.955
324.04
330.563
348.821
350.089
356.725
366.936
380.155
380.531
383.703
386.688
388.3
392.25
401.422
401.915
403.064
403.556
406.167
421.403
426.113
435.956
438.555
440.31
441.437
449.594
475.834
506.652
556.277
601.162
611.281
645.285
653.641
662.883
694.87
699.645
756.46
947.293
1030.944
1305.923
2763.544
4202.361

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80331&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=80331&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80331&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[0,50[2530.0229010.0229010.000458
[50,100[7580.0610690.0839690.001221
[100,150[12570.0534350.1374050.001069
[150,200[175200.1526720.2900760.003053
[200,250[225340.2595420.5496180.005191
[250,300[275200.1526720.702290.003053
[300,350[32580.0610690.7633590.001221
[350,400[37590.0687020.8320610.001374
[400,450[425120.0916030.9236640.001832
[450,500[47510.0076340.9312980.000153
[500,550[52510.0076340.9389310.000153
[550,600[57510.0076340.9465650.000153
[600,650[62530.0229010.9694660.000458
[650,700]67540.03053410.000611

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[0,50[ & 25 & 3 & 0.022901 & 0.022901 & 0.000458 \tabularnewline
[50,100[ & 75 & 8 & 0.061069 & 0.083969 & 0.001221 \tabularnewline
[100,150[ & 125 & 7 & 0.053435 & 0.137405 & 0.001069 \tabularnewline
[150,200[ & 175 & 20 & 0.152672 & 0.290076 & 0.003053 \tabularnewline
[200,250[ & 225 & 34 & 0.259542 & 0.549618 & 0.005191 \tabularnewline
[250,300[ & 275 & 20 & 0.152672 & 0.70229 & 0.003053 \tabularnewline
[300,350[ & 325 & 8 & 0.061069 & 0.763359 & 0.001221 \tabularnewline
[350,400[ & 375 & 9 & 0.068702 & 0.832061 & 0.001374 \tabularnewline
[400,450[ & 425 & 12 & 0.091603 & 0.923664 & 0.001832 \tabularnewline
[450,500[ & 475 & 1 & 0.007634 & 0.931298 & 0.000153 \tabularnewline
[500,550[ & 525 & 1 & 0.007634 & 0.938931 & 0.000153 \tabularnewline
[550,600[ & 575 & 1 & 0.007634 & 0.946565 & 0.000153 \tabularnewline
[600,650[ & 625 & 3 & 0.022901 & 0.969466 & 0.000458 \tabularnewline
[650,700] & 675 & 4 & 0.030534 & 1 & 0.000611 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80331&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]3[/C][C]0.022901[/C][C]0.022901[/C][C]0.000458[/C][/ROW]
[ROW][C][50,100[[/C][C]75[/C][C]8[/C][C]0.061069[/C][C]0.083969[/C][C]0.001221[/C][/ROW]
[ROW][C][100,150[[/C][C]125[/C][C]7[/C][C]0.053435[/C][C]0.137405[/C][C]0.001069[/C][/ROW]
[ROW][C][150,200[[/C][C]175[/C][C]20[/C][C]0.152672[/C][C]0.290076[/C][C]0.003053[/C][/ROW]
[ROW][C][200,250[[/C][C]225[/C][C]34[/C][C]0.259542[/C][C]0.549618[/C][C]0.005191[/C][/ROW]
[ROW][C][250,300[[/C][C]275[/C][C]20[/C][C]0.152672[/C][C]0.70229[/C][C]0.003053[/C][/ROW]
[ROW][C][300,350[[/C][C]325[/C][C]8[/C][C]0.061069[/C][C]0.763359[/C][C]0.001221[/C][/ROW]
[ROW][C][350,400[[/C][C]375[/C][C]9[/C][C]0.068702[/C][C]0.832061[/C][C]0.001374[/C][/ROW]
[ROW][C][400,450[[/C][C]425[/C][C]12[/C][C]0.091603[/C][C]0.923664[/C][C]0.001832[/C][/ROW]
[ROW][C][450,500[[/C][C]475[/C][C]1[/C][C]0.007634[/C][C]0.931298[/C][C]0.000153[/C][/ROW]
[ROW][C][500,550[[/C][C]525[/C][C]1[/C][C]0.007634[/C][C]0.938931[/C][C]0.000153[/C][/ROW]
[ROW][C][550,600[[/C][C]575[/C][C]1[/C][C]0.007634[/C][C]0.946565[/C][C]0.000153[/C][/ROW]
[ROW][C][600,650[[/C][C]625[/C][C]3[/C][C]0.022901[/C][C]0.969466[/C][C]0.000458[/C][/ROW]
[ROW][C][650,700][/C][C]675[/C][C]4[/C][C]0.030534[/C][C]1[/C][C]0.000611[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=80331&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80331&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[2530.0229010.0229010.000458
[50,100[7580.0610690.0839690.001221
[100,150[12570.0534350.1374050.001069
[150,200[175200.1526720.2900760.003053
[200,250[225340.2595420.5496180.005191
[250,300[275200.1526720.702290.003053
[300,350[32580.0610690.7633590.001221
[350,400[37590.0687020.8320610.001374
[400,450[425120.0916030.9236640.001832
[450,500[47510.0076340.9312980.000153
[500,550[52510.0076340.9389310.000153
[550,600[57510.0076340.9465650.000153
[600,650[62530.0229010.9694660.000458
[650,700]67540.03053410.000611


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 14 ; par2 = grey ; par3 = FALSE ; par4 = Unknown ;
Parameters (R input):
par1 = 14 ; 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')
}