> 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. As for letting the language dictate the indexing, I think this is a bad practice for developing an API. APIs are supposed to be language agnostic and should exhibit the same behavior no matter the implementing language. If we allow the language to dictate the behavior of an API method, its possible the behavior will be different for other languages. I feel these situations should be avoided so the API is portable to a wide array of languages, which I feel is a long-term goal of some of our developers. > 2) Change the name of "BivariateRegression" to "UnivariateRegression" (or > something else) If we're bothering to change its name to make it less confusing, let's call it what it is, SimpleLeastSquaresRegression. If that is too long, then SimpleRegression as least squares is the inferred method when one mentions regression. > 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. As for the static methods on the variance and standard deviation classes, the javadoc should be changed to better explain the source of the mean argument. The comments should indicate the mean is pre-computed using the same values that are going to be used to compute the variation estimate. Any other mean passed in will result in the variation computation to be unreliable. > 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"). "Regression is used to study relationships between measurable variables." [Weisberg, 1985] "Regression analysis is a statistical tool that utilizes the relations between two or more quantitative variables..." [Neter, et al., 1985] 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. Brent Worden --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
