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From Kim van der Linde <>
Subject Re: [MATH] Summary proposed changes
Date Wed, 01 Sep 2004 14:51:36 GMT

Brent Worden wrote:
>>1) Change the RealMatrix getEntry, getRow, getColumn methods to use
>>0-based indexing.
> Looking at the implementation, I believe the current indexing is
> satisfactory and I can't think of where using it with native arrays would be
> overly burdensome or confusing.

Well, so you think I requested this out of pure filosophical reasons. I 
am running into problems with it, that's why. But maybe I should just do 
it differently, and make a derived class from it and distribute that 
with the classes I am making.....

> APIs are supposed to be language agnostic

I think API's should be logical, and desinged such that they minimise 

> let's call
> it what it is, SimpleLeastSquaresRegression.  If that is too long, then
> SimpleRegression

Fine with me.

>>3) Change Variance to be configurable to generate the population
> statistic.
> Since population variance and sample variance are different statistics, they
> should be different classes as that is the design we have chosen.

I disagree, but in that case I will follow the same way on these classes 
as mentioned for the Matrix classes.

>>4) Combine the univariate and multivariate packages, since it is confusing
>>to separate statistics that focus on one variable and sometimes the word
>>"univariate" is used in the context of multivariate techniques (e.g.
>>"Univariate Anova").

> Both these statements indicate regression is a technique that involves more
> than one variable.  Therefore, regression in general is a multivariate
> technique.  The case where there is only one predictor is immaterial as
> there are two variable quantities.  Would one call a model with one
> predictor variable and two response variables a univariate technique?  I
> wouldn't and I doubt if anyone else would.  The path we have chosen, by
> placing procedures dealing with one variable in the univariate package and
> all other procedures dealing with more than one variable is satisfactory and
> makes for a good discriminant.

See my response, this is not what I proposed. Anyway, common 
interpretation (even among my collegues who do nothing else that complex 
multivariate analyses) is that the one independent, one dependent 
regressions are univariate regressions, although they can see the logic 
as there are two variables.

But in that sense, the TTest should be within the multivariate package 
too. Both simple regression and t-tests are in the end simplified 
versions of the GLM using only one dependent and one independent variable.

Anyway, I thank you all for the help and I will just make derived 
classes were I need a different implementation as provided by this package.



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