Tag Archives: biostatistical learning

NXG Logic Introduces Game-Changing Statistical Software

Houston, Texas, 2018-Jan-3 — /EPR SOFTWARE NEWS/ —  NXG Logic recently introduced two new Windows-based products, the Explorer package for machine learning and statistical analysis, and the Instructor package for generation of biostatistical learning and teaching materials.

Academic and industrial researchers know full well that in order to be successful in science, you can’t waste time on anything unnecessarily. This mantra includes meetings, grant-writing, publication, preparation of presentation materials, managing experiments in the lab, and analyzing data from experiments. With the ever-decreasing US NIH budget for medical research, in spite of being funded, most grants awarded today receive significant budget cuts which translate to dropping salary and fringe for a lab technician or dropping sub-aims of the research objectives that could potentially provide new insights into disease and establish new leads for future research. Altogether, there is an overwhelming sense of cost reduction (belt-tightening), increased efficiency and increased resource optimization in academic research.

Wasted functionality. In the early days of statistical software development (circa 1970s-1980s), software houses competed by offering more and more statistical tests.  The problem that ensued was that, over time, most of the large vendors like SAS, SPSS, Stata, etc., programmed into their software literally everything they could “get their hands on” — and their current customers are still paying for this unprecedented programming frenzy.  The drawback of this “program everything” focus is that only a fraction of the software developed will ever be used.  In short, most IT departments are likely wasting thousands of dollars per year for statistical software functionality which is never used because of developer over-programming.

Wasted time. There is also a good chance statistical software users are spending too much time to analyze data.   Most packages require running a test for each pair of variables singly, and then manually transposing results (statistics and p-values) into Word, Excel, or PowerPoint presentations.  So the problem is not only related to paying for features that will never be used, but also wasting precious time to create publishable results in grant applications, manuscripts, presentations, and research reports.

New demands. Data analysis has also changed over the last few decades. Demand for software capable of data-driven analyses is now competing with the demand for software providing only hypothesis-driven analyses, the latter of which involve the majority of large statistical software developer houses. The idea of “death of statistics” involving use of probability distributions to define everything is not a new one. In point of fact, most graduate students are now more interested in large-scale deep learning with artificial neural networks, or machine learning as a way of becoming competitive in today’s employment markets.

Novel approach. NXG Logic’s approach to statistical software development started with the realization of what most statistical software packages lacked, namely, the ability to rapidly combine hypothesis test results for multiple variables into a single color-formatted output which could rapidly be pasted into manuscripts and presentations; and the lack of newer more contemporary machine learning and artificial neural network methods.   Original program design concepts incorporated numerous time-saving steps so that the end-user can obtain more informative results faster, while optimizing research resources.  Since the early 2000’s, NXG Logic’s programming efforts focused on development of several fast-formatting technologies which combined output from runs made on multiple variables. These technologies include:

  • FFOSS – Fast Formatted Output for Summary Statistics
  • FFOMT – Fast Formatted Output for Multiple Tests
  • FFORM – Fast Formatted Output for Regression Models
  • FFOA – Fast Formatted Output for Association
  • FFOCD – Fast Formatted Output for Class Discovery
  • FFOCP – Fast Formatted Output for Class Prediction

Using NXG Logic’s Explorer package, researchers can generate demographics tables and tables for hypothesis tests in a fraction of the time currently required by most software packages.  Whether it’s machine learning, cluster analysis, ANOVA, class discovery, class prediction, predictive analytics, or survival analysis, Explorer can produce multi-variable results substantially faster and in a format that is much more informative when compared with most other packages.

The newly introduced NXG Logic Instructor package for learning/teaching biostatistics can substantially shorten the time required for generating statistical teaching materials, including homeworks, quizzes, exams, course packs, grading keys for TAs with worked solutions, etc. The rationale for developing the Instructor package was to reduce the time required for generating high-quality biostatistical teaching materials, including homeworks, quizzes, and examinations which could be randomly generated so that students have different parameters for questions and different simulated datasets. Student dishonesty and cheating is on the rise around the globe, and universities are constantly trying to increase their awareness of it while attempting to thwart its occurrence. By randomly generating quiz and examination questions with different parameters, and randomly generating different datasets for student projects, the Instructor package can be used to help overcome these issues.

Visit www.nxglogic.com for additional information.

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