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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_fitdistrnorm.wasp
Title produced by softwareMaximum-likelihood Fitting - Normal Distribution
Date of computationTue, 19 Oct 2010 15:35:19 +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/19/t1287502441ewh0sdmlplce7kh.htm/, Retrieved Sun, 28 Apr 2024 21:27:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=86653, Retrieved Sun, 28 Apr 2024 21:27:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Maximum-likelihood Fitting - Normal Distribution] [Intrinsic Motivat...] [2010-10-12 11:57:21] [b98453cac15ba1066b407e146608df68]
F   PD    [Maximum-likelihood Fitting - Normal Distribution] [] [2010-10-19 15:35:19] [6b31f806e9ccc1f74a26091056f791cb] [Current]
Feedback Forum
2010-10-23 09:26:00 [] [reply] 
We kunnen spreken van een normaal verdeling. Je ziet aan de linkerkant dat er wel wat te veel frequenties zijn en rechts wat te weinig, maar het valt al bij al wel mee. Indien we een andere klassenverdeling zouden gebruiken, zou het al beter passen.
2010-10-23 10:59:56 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
Men kan hier vaststellen dat het histogram dat werd opgesteld op basis van de werkelijke gegevens dezelfde vorm aanneemt dan de voorstelling van hoe de normaalverdeling van deze gegevens er zou uitzien (de volle lijn). Men kan dus concluderen dat de gegevens een normaalverdeling vormen. De uitschieters die men ziet, zouden inderdaad eventueel weggewerkt kunnen worden door de klassenverdeling op een andere manier te doen.
2010-10-26 09:38:31 [] [reply] 
Aan de linker kant zijn er enkele uitschieters te zien. Voor de rest is het histogram normaal verdeeld. Toch zullen we dit moeten controleren met de Tukey Lambda verdeling om met zekerheid te kunnen spreken over een normaalverdeling.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=86653&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=86653&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=86653&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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






ParameterEstimated ValueStandard Deviation
mean18.53703703703700.34767269970282
standard deviation4.425151024679370.245841723593298

\begin{tabular}{lllllllll}
\hline
Parameter & Estimated Value & Standard Deviation \tabularnewline
mean & 18.5370370370370 & 0.34767269970282 \tabularnewline
standard deviation & 4.42515102467937 & 0.245841723593298 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=86653&T=1

[TABLE]
[ROW][C]Parameter[/C][C]Estimated Value[/C][C]Standard Deviation[/C][/ROW]
[ROW][C]mean[/C][C]18.5370370370370[/C][C]0.34767269970282[/C][/ROW]
[ROW][C]standard deviation[/C][C]4.42515102467937[/C][C]0.245841723593298[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=86653&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=86653&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:

ParameterEstimated ValueStandard Deviation
mean18.53703703703700.34767269970282
standard deviation4.425151024679370.245841723593298


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 8 ; par2 = 0 ;
Parameters (R input):
par1 = 8 ; par2 = 0 ;
R code (references can be found in the software module):
library(MASS)
par1 <- as.numeric(par1)
if (par2 == '0') par2 = 'Sturges' else par2 <- as.numeric(par2)
x <- as.ts(x) #otherwise the fitdistr function does not work properly
r <- fitdistr(x,'normal')
r
bitmap(file='test1.png')
myhist<-hist(x,col=par1,breaks=par2,main=main,ylab=ylab,xlab=xlab,freq=F)
curve(1/(r$estimate[2]*sqrt(2*pi))*exp(-1/2*((x-r$estimate[1])/r$estimate[2])^2),min(x),max(x),add=T)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Parameter',1,TRUE)
a<-table.element(a,'Estimated Value',1,TRUE)
a<-table.element(a,'Standard Deviation',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,r$estimate[1])
a<-table.element(a,r$sd[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'standard deviation',header=TRUE)
a<-table.element(a,r$estimate[2])
a<-table.element(a,r$sd[2])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')