Hi
See the file http://www.mijnbestand.nl/Bestand-6ZPTBYDLBZQI.txt here .
That file contains the results of a study on breathing resistance in
children with asthma and children with cystic fibrosis to investigate wheter
there is a relationship between breathing resistance and length in each of
th
Hi,
So in my example, I can say that the data comes from a moderate normal
distribution because the points more at the right lay straight to a straight
line, then the points at the left. Please a confirmation here.
But what is the information above (that the data is from a normal
distribution) say
Dear Özgür
On Wed, Jun 20, 2012 at 7:37 AM, Özgür Asar wrote:
> Why do you prefer robust methods in the example of Noor and why you need
> exact normality here?
>
The idea is that when you do hypothesis testing to check whether a
given distribution is normal, the results are rarely informative:
>Hi,
>So in my example, I can say that the data comes from a moderate normal
distribution because the points more at the >right lay straight to a
straight line, then the points at the left. Please a confirmation here.
>But what is the information above (that the data is from a normal
distribution)
Dear Liviu ,
Why do you prefer robust methods in the example of Noor and why you need
exact normality here?
Ozgur
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Dear Kjetil,
Simulated point-wise confidence envelopes are available from qqPlot() only for
studentized residuals from linear and generalized linear models. For an
independent sample of observations, the confidence envelopes produced by
qqPlot() are based on the standard errors of the order sta
Or uou can try
library(car)
?qqPlot
use that with argument simulate=TRUE, which will give a simulated
envelope around the curve for
comparison.
Kjetil
On Tue, Jun 19, 2012 at 9:30 AM, Özgür Asar wrote:
> Hi,
>
> Try boxplot for outliers.
>
> To decide whether they influence significantly, try
Hi,
But what are the functions of the outliers on the left and right? Does they
influence the normal distribution?
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On Tue, Jun 19, 2012 at 12:42 PM, Özgür Asar wrote:
> Following a straight line indicates less evidence towards non-normality. But
> QQ-Plot is an exploratory tool.
>
> You can confirm your ideas obtained from the QQ-Plot via noramlity tests
> such as Shapiro-Wilk test.
>
Hmm, some gurus on this
Hi,
Try boxplot for outliers.
To decide whether they influence significantly, try confirmatory normality
tests.
Ozgur
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Hi,
Following a straight line indicates less evidence towards non-normality. But
QQ-Plot is an exploratory tool.
You can confirm your ideas obtained from the QQ-Plot via noramlity tests
such as Shapiro-Wilk test.
See shapiro.test under stats package and nortest package.
Ozgur
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