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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_meanversusmedian.wasp
Title produced by softwareMean versus Median
Date of computationTue, 05 Oct 2010 05:39:16 +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/05/t1286257158wmqw6u9fqhn0qv2.htm/, Retrieved Sun, 28 Apr 2024 17:45:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=81326, Retrieved Sun, 28 Apr 2024 17:45:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Notched Boxplots] [Screen Width and ...] [2010-09-25 10:03:47] [b98453cac15ba1066b407e146608df68]
F RMPD    [Mean versus Median] [opdracht 10] [2010-10-05 05:39:16] [8f110cf3e3846d42560df9b5835185a6] [Current]
Feedback Forum
2010-10-08 16:54:15 [347d11d64cf4ded9ba0714e7297d928b] [reply] 
Fout (0/2).
Als u de link direct compute krijgt u het volgende:
http://www.freestatistics.org/blog/index.php?v=date/2010/Oct/08/t1286556797li7fnrq4ou7wswq.htm/
Hieruit kan u afleiden dat het gemiddelde hoger is dan het mediaan (= omdat de Density plot recht scheef is).
2010-10-09 16:50:08 [1047e32db976ffec0cf8e54ab6985f99] [reply] 
Bij een perfect normaalverdeling zouden mediaan en rekenkundig gemiddelde samenvallen. Er is sprake van een rechts scheve verdeling omdat rekenkundig gemiddelde groter is dan de mediaan. Dit komt door de extreme meetuitslagen. Ze trekken het rekenkundig gemiddelde naar boven.
http://www.freestatistics.org/blog/index.php?v=date/2010/Oct/04/t1286217481qm0sq0deszyhv8a.htm/
2010-10-10 12:09:29 [73b763ab03a59f488b4c9e04fda397bb] [reply] 
http://www.freestatistics.org/blog/date/2010/Oct/03/t1286139053cerb6vhlkzdiyum.htm

Hieruit kan je afleiden dat de mediaan kleiner is dan het gemiddelde. Dit valt te verklaren door de scheve verdeling van de extreme waardes/gegevens naar rechts (rightwards skewness). Deze trekken het gemiddelde omhoog.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1024	768
1024	768
1024	768
1024	768
1024	768
1024	768
1024	768
1024	768
1024	768
1024	768
1024	768
1024	640
1024	768
1024	768
1024	768
1024	768
1024	768
1024	768
1024	768
1024	768
1024	768
1117	698
1120	700
1140	641
1143	857
1152	864
1152	864
1176	735
1176	735
1257	785
1280	800
1280	1024
1280	768
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	768
1280	800
1280	800
1280	800
1280	1024
1280	1024
1280	800
1280	768
1280	800
1280	800
1280	800
1280	800
1280	1024
1280	800
1280	800
1280	800
1280	800
1280	768
1280	800
1280	1024
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	800
1280	1024
1280	800
1280	800
1280	800
1280	1024
1280	800
1280	1024
1280	800
1280	800
1280	800
1280	800
1280	800
1280	1024
1280	1024
1280	800
1280	800
1280	800
1366	768
1366	768
1366	768
1366	768
1366	768
1366	768
1366	768
1366	768
1408	880
1408	880
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1440	900
1503	845
1600	900
1600	1200
1600	900
1600	900
1600	900
1680	1050
1680	1050
1680	1050
1680	1050
1688	949
1760	990
1920	1080
1920	1200
2560	1440
917	550
983	737

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=81326&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=81326&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=81326&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


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
library(Hmisc)
m <- mean(x)
e <- median(x)
bitmap(file='test1.png')
op <- par(mfrow=c(2,1))
mydensity1 <- density(x,kernel='gaussian',na.rm=TRUE)
plot(mydensity1,main='Density Plot - Gaussian Kernel',xlab='Median (0 -> full line) | Mean (0 -> dashed line)',ylab='density')
abline(v=e,lty=1)
abline(v=m,lty=5)
grid()
myseq <- seq(0.01, 0.99, 0.01)
hd <- hdquantile(x, probs = myseq, se = TRUE, na.rm = FALSE, names = TRUE, weights=FALSE)
plot(myseq,hd,col=2,main='Harrell-Davis Quantiles',xlab='quantiles',ylab='Median (0 -> full) | Mean (0 -> dashed)')
abline(h=m,lty=5)
abline(h=e,lty=1)
grid()
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Median versus Mean',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,mean(x))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
a<-table.element(a,median(x))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')