x27;)$p. < .05))
--
David
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of Joris Meys
Sent: Thursday, May 06, 2010
h.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111
-Original Message-
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
project.org] On Behalf Of Joris Meys
Sent: Thursday, May 06, 2010 9:57 AM
To: Dimitri Liakhovitski
Cc: r-help@r-project
--
> David
>
>
>
>> --
>> Gregory (Greg) L. Snow Ph.D.
>> Statistical Data Center
>> Intermountain Healthcare
>> greg.s...@imail.org
>> 801.408.8111
>>
>>
>> -Original Message-----
>>> From: r-help-boun...@r-project.o
06, 2010 9:57 AM
> To: Dimitri Liakhovitski
> Cc: r-help@r-project.org; level
> Subject: Re: [R] T-test & for loop
>
> Hehe,
>
> those homeworks. If you can explain this code to the professor, you
> surely
> passed.
>
> n <-1
> obs.rej.rate <-
PS : level, you might want to consider stopping to spam the help-list.
You're not making yourself popular by asking -in one day- 3 questions that
can be solved by using Google and reading the introductions given on the R
homepage.
On Thu, May 6, 2010 at 5:57 PM, Joris Meys wrote:
> Hehe,
>
> tho
Hehe,
those homeworks. If you can explain this code to the professor, you surely
passed.
n <-1
obs.rej.rate <- sum(sapply(1:n,function(x){t.test(rnorm(10, 0.1,
1),alternative="greater",mu=0,conf.level=0.95)$p.value <0.05}))/n
obs.rej.rate
This could actually be another round of R-golf. Anybo
Sounds like homework (you are not supposed to post homework-related
questions here - read the guidelines). But anyway:
nr.of.rejections=0
for(i in 1:1){
x=rnorm(10, 0.1, 1)
result<-t.test(x,alternative="greater",mu=0,conf.level=0.95)$p.value
if(result<0.05) nr.of.rejections = nr.of
I have been set a question which i understand statistically but my inability
with R is preventing me from finishing it..
My question is that we to calculate the frequency of Type 1 errors
starting with x = rnorm(10, 0.1, 1)
then doing a t-test seeing whether you reject the null hypothesis (Ho
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