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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 computationWed, 12 Nov 2008 07:47:17 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/12/t1226501310nfclrs1gkpd7yle.htm/, Retrieved Wed, 15 May 2024 05:18:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24218, Retrieved Wed, 15 May 2024 05:18:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

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] [Normal distribution] [2007-11-05 10:10:07] [76acd2a07599dda8f6e62381bea67e8b]
F    D  [Maximum-likelihood Fitting - Normal Distribution] [Various EDA Topic...] [2008-11-09 19:06:54] [57850c80fd59ccfb28f882be994e814e]
F    D      [Maximum-likelihood Fitting - Normal Distribution] [Various EDA topic...] [2008-11-12 14:47:17] [ff1f39dba9ec26bf89aa666d9dcb6cc1] [Current]
Feedback Forum
2008-11-24 17:45:56 [5faab2fc6fb120339944528a32d48a04] [reply] 
Het is inderdaad geen goede benadering. Bij het vergelijken van het histogram en de normaalverdelingscurve kunnen we vaststellen dat de gegevens niet normaal verdeeld zijn. Er zijn te veel uitschieters en lage blokken in het histogram.
2008-11-24 20:41:49 [Kevin Vermeiren] [reply] 
De student heeft de opgave niet goed begrepen. Ook hier diende gewerkt te worden met dezelfde Yt uit Q3. Door deze module “Maximum-likelihood Normal Distribution Fitting” toe te passen kunnen we gemakkelijk concluderen of de gegevens al dan niet normaal verdeeld zijn. De berekening maakt een schatting van het gemiddelde en de standaardfout die het best past bij de verdeling van de gegevens. De figuur die dan bekomen wordt, geeft een histogram van Yt weer met een normaalverdeling die het beste overeenkomt met het histogram. Visueel kan dan vastgesteld worden of het histogram al dan niet een goede benadering is van de vorm van de normaalverdeling.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
90,7
94,3
104,6
111,1
110,8
107,2
99,0
99,0
91,0
96,2
96,9
96,2
100,1
99,0
115,4
106,9
107,1
99,3
99,2
108,3
105,6
99,5
107,4
93,1
88,1
110,7
113,1
99,6
93,6
98,6
99,6
114,3
107,8
101,2
112,5
100,5
93,9
116,2
112,0
106,4
95,7
96,0
95,8
103,0
102,2
98,4
111,4
86,6
91,3
107,9
101,8
104,4
93,4
100,1
98,5
112,9
101,4
107,1
110,8
90,3
95,5
111,4
113,0
107,5
95,9
106,3
105,2
117,2
106,9
108,2
113,0
97,2
99,9
108,1
118,1
109,1
93,3
112,1
111,8
112,5
116,3
110,3
117,1
103,4
96,2

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24218&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24218&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24218&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'George Udny Yule' @ 72.249.76.132






ParameterEstimated ValueStandard Deviation
mean103.5352941176470.842110885173045
standard deviation7.763878743823150.595462317416866

\begin{tabular}{lllllllll}
\hline
Parameter & Estimated Value & Standard Deviation \tabularnewline
mean & 103.535294117647 & 0.842110885173045 \tabularnewline
standard deviation & 7.76387874382315 & 0.595462317416866 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24218&T=1

[TABLE]
[ROW][C]Parameter[/C][C]Estimated Value[/C][C]Standard Deviation[/C][/ROW]
[ROW][C]mean[/C][C]103.535294117647[/C][C]0.842110885173045[/C][/ROW]
[ROW][C]standard deviation[/C][C]7.76387874382315[/C][C]0.595462317416866[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24218&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24218&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
mean103.5352941176470.842110885173045
standard deviation7.763878743823150.595462317416866


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')