
So .. 

are you trying to figure out whether your data hasa substantial number of outliers that call into question the adequacy of the normal distro fro your data?

 

If this is the case, note that you cannot individually check the values (as you are doing) without taking into account of the "Bonferoni" fallacy i.e. small p-values will be found with a respectable frequency as the size of the dataset grows (C Robert discusses this in a preprint in arxiv see http://arxiv.org/PS_cache/arxiv/pdf/1002/1002.2080v1.pdf ) So even though you could check each individual point for normality, testing the whole dataset requires that you apply a Bonferoni correction to your z.tests or use outlier.test from package "car" to reduce the amount of code you have to write.

 

Regards, 

Christos
 
> Date: Fri, 16 Apr 2010 19:11:19 +0300
> From: attenka@utu.fi
> To: r-help@r-project.org
> Subject: [R] Is it ok to apply the z.test this way?
> 
> Dear R-users,
> 
> I want to check if certain values are from random distribution, that includes values between 0-1. So, it is not really normal even though shapiro.test says it is highly normal... Can I do something like this and think that the values given are right. z.test is from package TeachingDemos. 
> -------------------------------------------------------------------------------
> SelectedVals=c()
> for(i in seq(0,1,by=0.001))
> {
> if((z.test(i, mu=mean(Distribution), stdev=sd(Distribution))$p.value)<=0.05) SelectedVals=c(SelectedVals,i)
> }
> 
> -------------------------------------------------------------------------------
> I have marked the border values given by this script to the histogram of the original random distribution:
> 
> http://www.ag.fimug.fi/~Atte/62Hist100410.pdf
> 
> Atte Tenkanen
> University of Turku, Finland
> Department of Musicology
> +35823335278
> http://users.utu.fi/attenka/
> 
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
 		 	   		  
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