## Free Statistics

of Irreproducible Research!

Author's title
Author*Unverified author*
R Software Modulerwasp_fitdistrnorm.wasp
Title produced by softwareML Fitting and QQ Plot- Normal Distribution
Date of computationTue, 21 Apr 2020 17:18:48 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Apr/21/t1587484285dxyox4lugqr5gov.htm/, Retrieved Sat, 15 May 2021 23:59:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319139, Retrieved Sat, 15 May 2021 23:59:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact46
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ML Fitting and QQ Plot- Normal Distribution] [] [2020-04-21 15:18:48] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

Post a new message
Dataseries X:
$62,897,210.20$63,174,413.18
$67,201,224.66$68,973,979.92
$52,190,577.71$61,079,762.12
$60,602,326.05$80,045,781.80
$63,348,002.01$70,379,096.07
$57,579,682.80$59,434,016.55
$58,259,927.53$60,806,054.54
$66,296,678.07$66,601,187.53
$67,694,923.94$65,798,390.32
$62,111,206.30$66,977,264.43
$59,111,920.01$53,488,848.05
$53,985,223.72$62,766,088.16
$58,102,681.96$68,715,070.51
$66,952,517.50$66,529,466.40
$71,513,797.44$67,518,599.51
$70,881,629.67$74,068,747.43
$73,348,701.17$74,418,791.76
$83,778,135.12$82,054,671.92
$83,909,739.64$79,179,168.58
$74,697,490.36$76,996,320.79
$77,924,713.88$77,257,423.85
$91,579,952.10$95,295,918.81
$93,023,164.41$92,592,734.65
$95,612,377.01$97,493,588.08
$95,460,766.25$97,589,841.71
$116,455,733.38$123,758,776.11


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 0 seconds R Server Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319139&T=0

[TABLE]
[ROW]
 Summary of computational transaction[/C][/ROW] [ROW] Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW] Raw Output[/C] view raw output of R engine [/C][/ROW] [ROW] Computing time[/C] 0 seconds[/C][/ROW] [ROW] R Server[/C] Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319139&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319139&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 0 seconds R Server Big Analytics Cloud Computing Center

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)library(car)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 properlyr <- fitdistr(x,'normal')print(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$estimate[2])^2),min(x),max(x),add=T)dev.off()bitmap(file='test3.png')qqPlot(x,dist='norm',main='QQ plot (Normal) with confidence intervals')grid()dev.off()load(file='createtable')a<-table.start()a<-table.row.start(a)a<-table.element(a,'Parameter',1,TRUE)a<-table.element(a,'Estimated Value',1,TRUE)a<-table.element(a,'Standard Deviation',1,TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'mean',header=TRUE)a<-table.element(a,r$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')