Calling the lp_solve Linear Program Software from R, S-PLUS and Excel

We present a link that allows R, S-PLUS and Excel to call the functions in the lp_solve system. lp_solve is free software (licensed under the GNU Lesser GPL) that solves linear and mixed integer linear programs of moderate size (on the order of 10,000 variables and 50,000 constraints). R does not include this ability (though two add-on packages offer linear programs without integer variables), while S-PLUS users need to pay extra for the NuOPT library in order to solve these problems. Our link manages the interface between these statistical packages and lp_solve. Excel has a built-in add-in named Solver that is capable of solving mixed integer programs, but only with fewer than 200 variables. This link allows Excel users to handle substantially larger problems at no extra cost. While our primary concern has been the Windows operating system, the package has been tested on some Unix-type systems as well.

An Excel Add-In for Statistical Process Control Charts

Statistical process control (SPC) descibes a widely-used set of approaches used to detect shifts in processes in, for example, manufacturing. Among these are "control charts". Control charts and other SPC techniques have been in use since at least the'50s, and, because they are comparatively unsophisticated, are often used by management or operations personnel without formal statistical training. These personnel will often have experience with the popular spreadsheet program Excel, but may have less training on a mainstream statistical package. Base Excel does not provide the ability to draw control charts directly, although add-ins for that purpose are available for purchase. We present a free add-in for Excel that draws the most common sorts of control charts. It follows the development of the textbook of Montgomery (2005), so it may be well-suited for instructional purposes.

The Smarter Regression add-in for linear and logistic regression in excel

The widely-used Excel spreadsheet program has a linear regression routine, but it has a number of drawbacks: it does not handle categorical predictors; it requires to the user to generate columns for interactions; it cannot compute logistic regressions; and it is limited to 16 predictor columns. We have developed an Excel add-in that does both logistic and linear regression, handles categorical and interaction variables in an obvious way, and removes the 16-column limit. We also support nested model tests and, for linear regression, transformations of the response variable. Although we do not claim that Excel is the proper tool for data analysis, our tool can make both small, quick analyses and introductory statistics courses simpler and more complete.Approved for public release; distribution is unlimited

Calling the lp_solve Linear Program Software from R, S-PLUS and Excel

We present a link that allows R, S-PLUS and Excel to call the functions in the lp_solve system. lp_solve is free software (licensed under the GNU Lesser GPL) that solves linear and mixed integer linear programs of moderate size (on the order of 10,000 variables and 50,000 constraints). R does not include this ability (though two add-on packages offer linear programs without integer variables), while S-PLUS users need to pay extra for the NuOPT library in order to solve these problems. Our link manages the interface between these statistical packages and lp_solve. Excel has a built-in add-in named Solver that is capable of solving mixed integer programs, but only with fewer than 200 variables. This link allows Excel users to handle substantially larger problems at no extra cost. While our primary concern has been the Windows operating system, the package has been tested on some Unix-type systems as well

An Excel Add-In for Statistical Process Control Charts

t shifts in processes in, for example, manufacturing. Among these are control charts".Control charts and other SPC techniques have been in use since at least the 1950s, and,because they are comparatively unsophisticated, are often used by management or op-erations personnel without formal statistical training. These personnel will often haveexperience with the popular spreadsheet program Excel, but may have less training on amainstream statistical package. Base Excel does not provide the ability to draw controlcharts directly, although add-ins for that purpose are available for purchase.We present a free add-in for Excel that draws the most common sorts of control charts.It follows the development of the textbook of Montgomery (2005), so it may be well-suited for instructional purposes

An evaluation of the effectiveness of the crew resource management programme in naval aviation

The US Navy’s Crew Resource Management (CRM) training programme has not been evaluated within the last decade. Reactions were evaluated by analysing 51,570 responses to an item pertaining to CRM that is part of a safety climate survey. A total of 172 responses were obtained on a knowledge test. The attitudes of 553 naval aviators were assessed using an attitudes questionnaire. The CRM mishap rate from 1997 until 2007 was evaluated. It was found that naval aviators appear to think than CRM training is useful, are generally knowledgeable of, and display positive attitudes towards, the concepts addressed in the training. However, there is a lack of evidence to support the view that CRM training is having an effect on the mishap rate. As the next generation of highly automated aircraft becomes part of naval aviation, there is a need to ensure that CRM training evolves to meet this new challenge

Calling the LP Solve linear program software from Excel, S-Plus and R

We present software that allows Excel, S-Plus and R to call the functions in the lp solve system. Lp solve is free software (licensed under the Gnu Lesser GPL) that solves linear and mixed-integer linear programs of moderate size (on the order of 10,000 variables and 50,000 constraints). Since these problems are substantially larger than those that can be handled by Excel's built-in solver, this link will allow Excel users to handle large problems at no extra cost.Approved for public release; distribution is unlimited

An Environment for Creating Interactive Statistical Documents

The spectacular growth and acceptance of theWeb has made it a very attractive medium for interactive documents. Web-based reporting in industry, “live” documents in research, and interactive worksheets in education material are in many ways ideal uses of the Web. These types of documents frequently display dynamic, statistical output both in the form of text and plots. Unfortunately, much of the effort in creating these types of documents has focussed on re-inventing existing statistical software, and often with inferior results. The reason is that systems such as S and SAS cannot be integrated into the reader’s browser. A better approach is to allow the author to create a document using the common authoring tools (e.g. LATEX, MS Word or HTML editors) and to conveniently insert dynamic and interactive components from other languages. The author focuses on the presentation and display of these components, including the usual multi-media elements such as text, images and sounds. She uses HTML form elements and Java components to provide interactive controls with which the reader can manipulate the contents of the document. And finally she performs statistical computations and renders visual displays using the statistical software that is embedded within the reader’s browser. In this presentation, we describe how we have created an environment for interactive statistical documents. It allows the author to use HTML, JavaScript and R to create the content and the interactivity. The reader accesses the interactive and dynamic functionality of the document via a plug-in for Netscape that embeds R within it. The different languages are all reasonably standard tools and each is used for the purposes for which it was designed. This makes it a reasonably straightforward environment in which to quickly and simply create interfaces for various different applications and audiences

Optimal location of Navy recruiters

Naval Research Program, Research & Sponsored Programs Office 699 Dyer Road, Bldg 234 Naval Postgraduate School Monterey, CA, 93943Prepared for the Marine Corps Systems CommandNaval Research ProgramApproved for public release; distribution is unlimited

A scale-independent, noise-resistant dissimilarity for tree-based clustering of mixed data

Clustering techniques divide observations into groups.Current techniques usually rely on measurements of dissimilarities between pairs of observations, between pairs of clusters, and between an observation and a cluster.For numeric variables, these dissimilarity measurements often depend on the scaling of the variables, are changed by monotonic transformations, and do not provide for selection of “important" variables.In our scheme, we fit a set of regression or classification trees with each variable acting in turn as the “response" variable.Points are “close" to one another if they tend to appear in the same leaves of these trees.Trees with poor predictive power are discarded.Therefore, “noise" variables will often appear in none of the trees and have no effect on the clustering. Because our technique uses trees, the dissimilarities are unaffected by linear transformations of the numeric variables and resistant to monotonic ones and to outliers.Categorical variables are included automatically and missing values handled in a natural way.We demonstrate the performance of this technique by using these dissimilarities to cluster some well-known data sets to which noise has been added.Approved for public release; distribution is unlimited