## Free Statistics

of Irreproducible Research!

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, 11 Sep 2024 07:08:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=94285, Retrieved Wed, 11 Sep 2024 07:08:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact214
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
Dataseries X:
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


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 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:

The GUIDs for individual cells are displayed in the table below:

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk

 Frequency Table (Histogram) Bins Midpoint Abs. Frequency Rel. Frequency Cumul. Rel. Freq. Density [220,240[ 230 3 0.008333 0.008333 0.000417 [240,260[ 250 58 0.161111 0.169444 0.008056 [260,280[ 270 52 0.144444 0.313889 0.007222 [280,300[ 290 75 0.208333 0.522222 0.010417 [300,320[ 310 43 0.119444 0.641667 0.005972 [320,340[ 330 45 0.125 0.766667 0.00625 [340,360[ 350 36 0.1 0.866667 0.005 [360,380[ 370 14 0.038889 0.905556 0.001944 [380,400[ 390 11 0.030556 0.936111 0.001528 [400,420[ 410 10 0.027778 0.963889 0.001389 [420,440[ 430 5 0.013889 0.977778 0.000694 [440,460[ 450 5 0.013889 0.991667 0.000694 [460,480] 470 3 0.008333 1 0.000417

\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:

The GUIDs for individual cells are displayed in the table below:

 Frequency Table (Histogram) Bins Midpoint Abs. Frequency Rel. Frequency Cumul. Rel. Freq. Density [220,240[ 230 3 0.008333 0.008333 0.000417 [240,260[ 250 58 0.161111 0.169444 0.008056 [260,280[ 270 52 0.144444 0.313889 0.007222 [280,300[ 290 75 0.208333 0.522222 0.010417 [300,320[ 310 43 0.119444 0.641667 0.005972 [320,340[ 330 45 0.125 0.766667 0.00625 [340,360[ 350 36 0.1 0.866667 0.005 [360,380[ 370 14 0.038889 0.905556 0.001944 [380,400[ 390 11 0.030556 0.936111 0.001528 [400,420[ 410 10 0.027778 0.963889 0.001389 [420,440[ 430 5 0.013889 0.977778 0.000694 [440,460[ 450 5 0.013889 0.991667 0.000694 [460,480] 470 3 0.008333 1 0.000417

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 <- TRUEif (par3 == 'FALSE') par3 <- FALSEif (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 <- 3if (par1 > 50) par1 <- 50myhist<-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])) {myhistn <- 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 <- 0if (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='')elsedum <- paste(mybracket,myhist$breaks[i],sep='')dum <- paste(dum,myhist$breaks[i+1],sep=',')if (i==mynumrows)dum <- paste(dum,']',sep='')elsedum <- 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$counts[i]/ncrf <- crf + rfa<-table.element(a,round(rf,6))a<-table.element(a,round(crf,6))a<-table.element(a,round(myhist$density[i],6))a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable.tab')} else {mytabreltab <- 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')}