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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 08:50:16 -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/t12265050590vkypsuifpmnqfe.htm/, Retrieved Mon, 20 May 2024 04:10:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24255, Retrieved Mon, 20 May 2024 04:10:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Maximum-likelihood Fitting - Normal Distribution] [Various EDA Topics ] [2008-11-12 15:50:16] [e7b1048c2c3a353441b9143db4404b91] [Current]
-    D    [Maximum-likelihood Fitting - Normal Distribution] [Paper maximum lik...] [2008-12-19 17:54:18] [1640119c345fbfa2091dc1243f79f7a6]
Feedback Forum
2008-11-16 17:10:23 [006ad2c49b6a7c2ad6ab685cfc1dae56] [reply] 
Goed geïnterpreteerd maar wel wat te weinig uitleg. Met deze grafiek wordt er nagegaan hoe de gegevens verdeeld zijn. Deze verdeling wordt dan vergeleken met een normaalverdeling. De lijn op de grafiek stelt de normaalverdeling voor.
2008-11-20 11:33:40 [Jasmine Hendrikx] [reply] 
Eigen evaluatie:
Er is een redelijk goede conclusie gegeven. Het is inderdaad zo dat de normaalverdeling een vrij goede benadering lijkt van Yt. De lijn kent, zoals vermeld, ongeveer hetzelfde verloop als het histogram. Hier zou nog bij vermeld kunnen worden dat deze methode een schatting maakt van het gemiddelde en de standaardafwijking die het best past bij de verdeling van de gegevens. In de grafiek kun je dan ook de geschatte normaalverdeling zien die het dichtst bij het histogram aanleunt. Het histogram geeft dus de verdeling van de gegevens weer en de lijn stelt de normaalverdeling voor.
2008-12-05 16:41:30 [a2386b643d711541400692649981f2dc] [reply] 
Kort maar goed antwoord. Je had nog kunnen vermelden dat de grafiek de (bijna) typische 'klok'vorm had, wat wijst op een normaalverdeling.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
78,4
114,6
113,3
117,0
99,6
99,4
101,9
115,2
108,5
113,8
121,0
92,2
90,2
101,5
126,6
93,9
89,8
93,4
101,5
110,4
105,9
108,4
113,9
86,1
69,4
101,2
100,5
98,0
106,6
90,1
96,9
125,9
112,0
100,0
123,9
79,8
83,4
113,6
112,9
104,0
109,9
99,0
106,3
128,9
111,1
102,9
130,0
87,0
87,5
117,6
103,4
110,8
112,6
102,5
112,4
135,6
105,1
127,7
137,0
91,0
90,5
122,4
123,3
124,3
120,0
118,1
119,0
142,7
123,6
129,6
151,6
110,4
99,2
130,5
136,2
129,7
128,0
121,6
135,8
143,8
147,5
136,2
156,6
123,3
100,4

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=24255&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=24255&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24255&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
mean111.7329411764711.89312315012811
standard deviation17.45373304573651.33864021707683

\begin{tabular}{lllllllll}
\hline
Parameter & Estimated Value & Standard Deviation \tabularnewline
mean & 111.732941176471 & 1.89312315012811 \tabularnewline
standard deviation & 17.4537330457365 & 1.33864021707683 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24255&T=1

[TABLE]
[ROW][C]Parameter[/C][C]Estimated Value[/C][C]Standard Deviation[/C][/ROW]
[ROW][C]mean[/C][C]111.732941176471[/C][C]1.89312315012811[/C][/ROW]
[ROW][C]standard deviation[/C][C]17.4537330457365[/C][C]1.33864021707683[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24255&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24255&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
mean111.7329411764711.89312315012811
standard deviation17.45373304573651.33864021707683


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')