Author Archives: Durwood Marshall

The widespread availability of MicroSoft Excel has created a less than desirable environment for statistical computing. In my opinion the Excel statistics add-in leaves much to be desired relative to real statistics packages. One solution for extending the usefullness of … Continue reading →

If you want to save time and improve accuracy of your programs, don’t reinvent the wheel, consider using javanumerics. A large variety of statistical and mathematical classes are available. Note, not all options are free.

Often researchers will need access to functionality that isn’t found in commercial statistics packages. This problem varies quite a bit and is meet with specialized solutions by the statistical community. These solutions are often cutting edge, reflecting new statistical research. … Continue reading →

Statistical power calculations are often needed at various stages of planning for establishing sample sizes. Elsewhere on this Blog I mention PiFace as a power calculation tool. However SAS users may find the following three SAS macros of interest. UnifyPow … Continue reading →

Sometimes it is important to reinvent the wheel, and sometimes not. Here is a site with a nice collection of contributed R graphic examples from a variety of R packages. Almost all are supportive of some statistical method for purposes … Continue reading →

This is a broad and rich topic. Applications are found in almost every field. Over the past 30+ years major theoretical contributions from Econometrics, Psychometrics and Statistics have established the topic as a vibrant research area. Most major statistics oriented … Continue reading →

Cluster analysis has been a data mining tool for some time. There are hundreds of cluster algorithms that compete for various statistical notions of performance. All major statistical software packages offer several solutions to address this task. Recently the notion … Continue reading →

Spatial smoothing techniques are often employed to estimate mean trends over some spatial and or time domain. An explosion of new estimation methods in the last 15 or so years have improved upon simple multiple regression and Kriging options often … Continue reading →

A recent article in The International Jorunal of Biostatistics argues for confidence intervals for the mean derived from the use of Bernstein’s inequality. An excellent presentation compares and contrasts the proposed method with standard alternatives. In keeping with the spirit … Continue reading →

Cross-Over Designs have played a major role in applied settings that cuts across so many disciplines. And there is a rich history and literature on the topic. I recently acquired: Design and Analysis of Cross-Over Trials, Second Edition, by Byron … Continue reading →