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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_fitdistrnorm.wasp
Title produced by softwareMaximum-likelihood Fitting - Normal Distribution
Date of computationThu, 13 Nov 2008 13:29:43 -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/13/t12266083222e7p0mku90aoyk0.htm/, Retrieved Mon, 20 May 2024 11:40:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24823, Retrieved Mon, 20 May 2024 11:40:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Box-Cox Linearity Plot] [question 3 box-co...] [2008-11-12 15:13:33] [31c9f333c18b3396ccf9d2485dd39c8a]
F RM D  [Box-Cox Normality Plot] [Vincent Dolhain T...] [2008-11-13 20:20:28] [17bef6922a2795858ae28bf8ba596537]
F RMPD      [Maximum-likelihood Fitting - Normal Distribution] [Taak 2 Part 1 Oef5] [2008-11-13 20:29:43] [dcb9dbe132bac62365bf3d43fe342148] [Current]
Feedback Forum
2008-11-20 16:03:27 [Marie-Lien Loos] [reply] 
Het is inderdaad redelijk normaal verdeeld
2008-11-24 20:57:37 [Marlies Polfliet] [reply] 
De berekeningen zijn correct, en het lijkt er inderdaad op dat de student terecht heeft geconcludeerd dat de data normaal verdeeld is.

Theorie uit het elektronische handboek (aanvullend):
Maximum likelihood estimation begins with the mathematical expression known as a likelihood function of the sample data. Loosely speaking, the likelihood of a set of data is the probability of obtaining that particular set of data given the chosen probability model. This expression contains the unknown parameters. Those values of the parameter that maximize the sample likelihood are known as the maximum likelihood estimates. The reliability chapter contains some examples of the likelihood functions for a few of the commonly used distributions in reliability analysis.
2008-11-24 21:26:36 [Erik Geysen] [reply] 
De student heeft hier juist gehandeld. Het klopt dat de gegevens hier vrij normaal verdeeld zijn.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109.86
108.68
113.38
117.12
116.23
114.75
115.81
115.86
117.8
117.11
116.31
118.38
121.57
121.65
124.2
126.12
128.6
128.16
130.12
135.83
138.05
134.99
132.38
128.94
128.12
127.84
132.43
134.13
134.78
133.13
129.08
134.48
132.86
134.08
134.54
134.51
135.97
136.09
139.14
135.63
136.55
138.83
138.84
135.37
132.22
134.75
135.98
136.06
138.05
139.59
140.58
139.81
140.77
140.96
143.59
142.7
145.11
146.7
148.53
148.99
149.65
151.11
154.82
156.56
157.6
155.24
160.68
163.22
164.55
166.76
159.05
159.82
164.95
162.89

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24823&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24823&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24823&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






ParameterEstimated ValueStandard Deviation
mean136.4809459459461.65412704273821
standard deviation14.22933885464541.16964444886424

\begin{tabular}{lllllllll}
\hline
Parameter & Estimated Value & Standard Deviation \tabularnewline
mean & 136.480945945946 & 1.65412704273821 \tabularnewline
standard deviation & 14.2293388546454 & 1.16964444886424 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24823&T=1

[TABLE]
[ROW][C]Parameter[/C][C]Estimated Value[/C][C]Standard Deviation[/C][/ROW]
[ROW][C]mean[/C][C]136.480945945946[/C][C]1.65412704273821[/C][/ROW]
[ROW][C]standard deviation[/C][C]14.2293388546454[/C][C]1.16964444886424[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24823&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24823&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
mean136.4809459459461.65412704273821
standard deviation14.22933885464541.16964444886424


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')