need to know some basic functionality features of R-Proj

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need to know some basic functionality features of R-Proj

Mohammed Asifulla - CTD , Chennai
Hi,

I am new-comer to statistics and R-Project. I would like to know if these
features can be attained in R-Project.Please help.

1)  beta 1 and Beta 2, or gamma one and gamma two for skewness and kurtosis,
respectively, including standard errors and tests for significance (relative
to values for a Gaussian distribution).
2)  linear correlation
3)  quadratic regression
4)  polynomial regression
5)  moving averages
6)  chi-square for a two-by two table and for an n by m contingency table
7)  moving averages - with various (e.g. exponential) weighting
8)  cubic splines (smoothing, not interpolating)
9)  other types of splines, e.g. 'linear' splines
10) erfc-1  inverse error function complement (i.e. tables of integrals of
the normal (Gaussian) curve, or mathematical approximations)
11) erfc    error function complement
12) Table of significant values for t test at P < 0.01 one sided or two
sided - or polynomial approximation
13) Table of significance levels for chi square test
14) Table of significance levels for F distribution  as arising in ANOVA
15) Confidence limits for binomial variables; possibly for multinomial
variables

Thanks and Regards
-Asif

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Re: need to know some basic functionality features of R-Proj

Sean Davis



On 1/3/06 6:46 AM, "Mohammed Asifulla - CTD , Chennai" <[hidden email]>
wrote:

> Hi,
>
> I am new-comer to statistics and R-Project. I would like to know if these
> features can be attained in R-Project.Please help.
>
> 1)  beta 1 and Beta 2, or gamma one and gamma two for skewness and kurtosis,
> respectively, including standard errors and tests for significance (relative
> to values for a Gaussian distribution).
> 2)  linear correlation
> 3)  quadratic regression
> 4)  polynomial regression
> 5)  moving averages
> 6)  chi-square for a two-by two table and for an n by m contingency table
> 7)  moving averages - with various (e.g. exponential) weighting
> 8)  cubic splines (smoothing, not interpolating)
> 9)  other types of splines, e.g. 'linear' splines
> 10) erfc-1  inverse error function complement (i.e. tables of integrals of
> the normal (Gaussian) curve, or mathematical approximations)
> 11) erfc    error function complement
> 12) Table of significant values for t test at P < 0.01 one sided or two
> sided - or polynomial approximation
> 13) Table of significance levels for chi square test
> 14) Table of significance levels for F distribution  as arising in ANOVA
> 15) Confidence limits for binomial variables; possibly for multinomial
> variables

Asif,

It is highly likely that all these can be attained using R.  I think most
(if not all) of those on your list can be done with existing packages; for
those that can't, R is also a full-featured programming language, so you can
write functions to do what you like.  I would suggest starting with the
Introduction to R manual to learn what R can do.  It can be obtained via the
"Manuals" link at the left side of the R home page:

http://www.r-project.org

Also, if you are posting to the email list, it is quite helpful to read the
posting guide, available as a link at the bottom of all emails from this
list.

Sean

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