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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 computationFri, 12 Nov 2010 19:15:41 +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/Nov/12/t1289589339fws92ju5isw2es7.htm/, Retrieved Wed, 15 Jan 2025 08:50:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=94285, Retrieved Wed, 15 Jan 2025 08:50:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact225

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Central Tendency] [Arabica Price in ...] [2008-01-19 12:03:37] [74be16979710d4c4e7c6647856088456]
F RMPD    [Histogram] [WS 6 Distribution...] [2010-11-12 19:15:41] [4d0f7ea43b071af5c75b527ee1ef14c2] [Current]
-           [Histogram] [Workshop 6, Simpl...] [2010-11-13 12:58:48] [3635fb7041b1998c5a1332cf9de22bce]
- R  D        [Histogram] [Simple Linear Reg...] [2010-11-17 00:05:04] [b20a509e36241371274681d9edf773da]
-             [Histogram] [] [2011-11-12 14:40:59] [b1eb71d4db1ceb5d347df987feb4a25e]
- RMP         [Kernel Density Estimation] [] [2011-11-12 14:46:07] [b1eb71d4db1ceb5d347df987feb4a25e]
- RMP         [Mean Plot] [] [2011-11-12 14:52:15] [b1eb71d4db1ceb5d347df987feb4a25e]
- R           [Histogram] [] [2011-11-14 19:17:09] [c505444e07acba7694d29053ca5d114e]
- R           [Histogram] [Tutorial Assignme...] [2011-11-14 20:05:03] [147523945ddfd9cf10d509b57b5cab55]
- RM          [Histogram] [] [2011-11-15 08:37:28] [74be16979710d4c4e7c6647856088456]
-    D        [Histogram] [Workshop 6: Histo...] [2011-11-15 08:42:53] [21b3d52ef28595defb5676e0f3570994]
- RMPD        [Kernel Density Estimation] [Workshop 6: Kerne...] [2011-11-15 08:44:57] [21b3d52ef28595defb5676e0f3570994]
- RMPD        [Mean Plot] [Workshop 6: Plot ...] [2011-11-15 08:54:12] [21b3d52ef28595defb5676e0f3570994]
- RM          [Histogram] [Histrogram ] [2011-11-15 10:40:47] [74be16979710d4c4e7c6647856088456]
- RMP         [Histogram] [] [2011-11-15 15:55:01] [86a47bcc75cd2e0d5b5c9888edc893c2]
- R           [Histogram] [] [2011-11-15 15:55:40] [d6b4d011b409693eac2700c83288e3e7]
-   P         [Histogram] [workshop 6 Lineai...] [2011-11-15 19:23:09] [aa7c7608f809e956d7797134ec926e04]
- RMP         [Kernel Density Estimation] [workshop 6 Lineai...] [2011-11-15 19:30:44] [aa7c7608f809e956d7797134ec926e04]
-   PD        [Histogram] [Workshop 6 Histo] [2011-11-16 00:38:03] [43a0606d8103c0ba382f0586f4417c48]
- RMPD        [Kernel Density Estimation] [Workshop 6 Kernel...] [2011-11-16 00:39:39] [43a0606d8103c0ba382f0586f4417c48]
Feedback Forum
2010-11-21 08:11:05 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
Het histogram is inderdaad een perfect middel om een uitspraak te kunnen doen over de scheefheid van de verdeling.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
255
280.2
299.9
339.2
374.2
393.5
389.2
381.7
375.2
369
357.4
352.1
346.5
342.9
340.3
328.3
322.9
314.3
308.9
294
285.6
281.2
280.3
278.8
274.5
270.4
263.4
259.9
258
262.7
284.7
311.3
322.1
327
331.3
333.3
321.4
327
320
314.7
316.7
314.4
321.3
318.2
307.2
301.3
287.5
277.7
274.4
258.8
253.3
251
248.4
249.5
246.1
244.5
243.6
244
240.8
249.8
248
259.4
260.5
260.8
261.3
259.5
256.6
257.9
256.5
254.2
253.3
253.8
255.5
257.1
257.3
253.2
252.8
252
250.7
252.2
250
251
253.4
251.2
255.6
261.1
258.9
259.9
261.2
264.7
267.1
266.4
267.7
268.6
267.5
268.5
268.5
270.5
270.9
270.1
269.3
269.8
270.1
264.9
263.7
264.8
263.7
255.9
276.2
360.1
380.5
373.7
369.8
366.6
359.3
345.8
326.2
324.5
328.1
327.5
324.4
316.5
310.9
301.5
291.7
290.4
287.4
277.7
281.6
288
276
272.9
283
283.3
276.8
284.5
282.7
281.2
287.4
283.1
284
285.5
289.2
292.5
296.4
305.2
303.9
311.5
316.3
316.7
322.5
317.1
309.8
303.8
290.3
293.7
291.7
296.5
289.1
288.5
293.8
297.7
305.4
302.7
302.5
303
294.5
294.1
294.5
297.1
289.4
292.4
287.9
286.6
280.5
272.4
269.2
270.6
267.3
262.5
266.8
268.8
263.1
261.2
266
262.5
265.2
261.3
253.7
249.2
239.1
236.4
235.2
245.2
246.2
247.7
251.4
253.3
254.8
250
249.3
241.5
243.3
248
253
252.9
251.5
251.6
253.5
259.8
334.1
448
445.8
445
448.2
438.2
439.8
423.4
410.8
408.4
406.7
405.9
402.7
405.1
399.6
386.5
381.4
375.2
357.7
359
355
352.7
344.4
343.8
338
339
333.3
334.4
328.3
330.7
330
331.6
351.2
389.4
410.9
442.8
462.8
466.9
461.7
439.2
430.3
416.1
402.5
397.3
403.3
395.9
387.8
378.6
377.1
370.4
362
350.3
348.2
344.6
343.5
342.8
347.6
346.6
349.5
342.1
342
342.8
339.3
348.2
333.7
334.7
354
367.7
363.3
358.4
353.1
343.1
344.6
344.4
333.9
331.7
324.3
321.2
322.4
321.7
320.5
312.8
309.7
315.6
309.7
304.6
302.5
301.5
298.8
291.3
293.6
294.6
285.9
297.6
301.1
293.8
297.7
292.9
292.1
287.2
288.2
283.8
299.9
292.4
293.3
300.8
293.7
293.1
294.4
292.1
291.9
282.5
277.9
287.5
289.2
285.6
293.2
290.8
283.1
275
287.8
287.8
287.4
284
277.8
277.6
304.9
294
300.9
324
332.9
341.6
333.4
348.2
344.7
344.7
329.3
323.5
323.2
317.4
330.1
329.2
334.9
315.8
315.4
319.6
317.3
313.8
315.8
311.3

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94285&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94285&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94285&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Frequency Table (Histogram)
BinsMidpointAbs. FrequencyRel. FrequencyCumul. Rel. Freq.Density
[220,240[23030.0083330.0083330.000417
[240,260[250580.1611110.1694440.008056
[260,280[270520.1444440.3138890.007222
[280,300[290750.2083330.5222220.010417
[300,320[310430.1194440.6416670.005972
[320,340[330450.1250.7666670.00625
[340,360[350360.10.8666670.005
[360,380[370140.0388890.9055560.001944
[380,400[390110.0305560.9361110.001528
[400,420[410100.0277780.9638890.001389
[420,440[43050.0138890.9777780.000694
[440,460[45050.0138890.9916670.000694
[460,480]47030.00833310.000417

\begin{tabular}{lllllllll}
\hline
Frequency Table (Histogram) \tabularnewline
Bins & Midpoint & Abs. Frequency & Rel. Frequency & Cumul. Rel. Freq. & Density \tabularnewline
[220,240[ & 230 & 3 & 0.008333 & 0.008333 & 0.000417 \tabularnewline
[240,260[ & 250 & 58 & 0.161111 & 0.169444 & 0.008056 \tabularnewline
[260,280[ & 270 & 52 & 0.144444 & 0.313889 & 0.007222 \tabularnewline
[280,300[ & 290 & 75 & 0.208333 & 0.522222 & 0.010417 \tabularnewline
[300,320[ & 310 & 43 & 0.119444 & 0.641667 & 0.005972 \tabularnewline
[320,340[ & 330 & 45 & 0.125 & 0.766667 & 0.00625 \tabularnewline
[340,360[ & 350 & 36 & 0.1 & 0.866667 & 0.005 \tabularnewline
[360,380[ & 370 & 14 & 0.038889 & 0.905556 & 0.001944 \tabularnewline
[380,400[ & 390 & 11 & 0.030556 & 0.936111 & 0.001528 \tabularnewline
[400,420[ & 410 & 10 & 0.027778 & 0.963889 & 0.001389 \tabularnewline
[420,440[ & 430 & 5 & 0.013889 & 0.977778 & 0.000694 \tabularnewline
[440,460[ & 450 & 5 & 0.013889 & 0.991667 & 0.000694 \tabularnewline
[460,480] & 470 & 3 & 0.008333 & 1 & 0.000417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=94285&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][220,240[[/C][C]230[/C][C]3[/C][C]0.008333[/C][C]0.008333[/C][C]0.000417[/C][/ROW]
[ROW][C][240,260[[/C][C]250[/C][C]58[/C][C]0.161111[/C][C]0.169444[/C][C]0.008056[/C][/ROW]
[ROW][C][260,280[[/C][C]270[/C][C]52[/C][C]0.144444[/C][C]0.313889[/C][C]0.007222[/C][/ROW]
[ROW][C][280,300[[/C][C]290[/C][C]75[/C][C]0.208333[/C][C]0.522222[/C][C]0.010417[/C][/ROW]
[ROW][C][300,320[[/C][C]310[/C][C]43[/C][C]0.119444[/C][C]0.641667[/C][C]0.005972[/C][/ROW]
[ROW][C][320,340[[/C][C]330[/C][C]45[/C][C]0.125[/C][C]0.766667[/C][C]0.00625[/C][/ROW]
[ROW][C][340,360[[/C][C]350[/C][C]36[/C][C]0.1[/C][C]0.866667[/C][C]0.005[/C][/ROW]
[ROW][C][360,380[[/C][C]370[/C][C]14[/C][C]0.038889[/C][C]0.905556[/C][C]0.001944[/C][/ROW]
[ROW][C][380,400[[/C][C]390[/C][C]11[/C][C]0.030556[/C][C]0.936111[/C][C]0.001528[/C][/ROW]
[ROW][C][400,420[[/C][C]410[/C][C]10[/C][C]0.027778[/C][C]0.963889[/C][C]0.001389[/C][/ROW]
[ROW][C][420,440[[/C][C]430[/C][C]5[/C][C]0.013889[/C][C]0.977778[/C][C]0.000694[/C][/ROW]
[ROW][C][440,460[[/C][C]450[/C][C]5[/C][C]0.013889[/C][C]0.991667[/C][C]0.000694[/C][/ROW]
[ROW][C][460,480][/C][C]470[/C][C]3[/C][C]0.008333[/C][C]1[/C][C]0.000417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=94285&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=94285&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
[220,240[23030.0083330.0083330.000417
[240,260[250580.1611110.1694440.008056
[260,280[270520.1444440.3138890.007222
[280,300[290750.2083330.5222220.010417
[300,320[310430.1194440.6416670.005972
[320,340[330450.1250.7666670.00625
[340,360[350360.10.8666670.005
[360,380[370140.0388890.9055560.001944
[380,400[390110.0305560.9361110.001528
[400,420[410100.0277780.9638890.001389
[420,440[43050.0138890.9777780.000694
[440,460[45050.0138890.9916670.000694
[460,480]47030.00833310.000417


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 {
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')
}