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BackgroundWe live in an era of explosive data generation that will continue to grow and involve all industries. One of the results of this explosion is the need for newer and more efficient data analytics procedures. Traditionally, data analytics required a substantial background in statistics and computer science. In 2015, International Business Machines Corporation (IBM) released the IBM Watson Analytics (IBMWA) software that delivered advanced statistical procedures based on the Statistical Package for the Social Sciences (SPSS). The latest entry of Watson Analytics into the field of analytical software products provides users with enhanced functions that are not available in many existing programs. For example, Watson Analytics automatically analyzes datasets, examines data quality, and determines the optimal statistical approach. Users can request exploratory, predictive, and visual analytics. Using natural language processing (NLP), users are able to submit additional questions for analyses in a quick response format. This analytical package is available free to academic institutions (faculty and students) that plan to use the tools for noncommercial purposes.ObjectiveTo report the features of IBMWA and discuss how this software subjectively and objectively compares to other data mining programs.MethodsThe salient features of the IBMWA program were examined and compared with other common analytical platforms, using validated health datasets.ResultsUsing a validated dataset, IBMWA delivered similar predictions compared with several commercial and open source data mining software applications. The visual analytics generated by IBMWA were similar to results from programs such as Microsoft Excel and Tableau Software. In addition, assistance with data preprocessing and data exploration was an inherent component of the IBMWA application. Sensitivity and specificity were not included in the IBMWA predictive analytics results, nor were odds ratios, confidence intervals, or a confusion matrix.ConclusionsIBMWA is a new alternative for data analytics software that automates descriptive, predictive, and visual analytics. This program is very user-friendly but requires data preprocessing, statistical conceptual understanding, and domain expertise.

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Literature focused on the emerging implementation of STEAM (Science, Technology,

Purpose This study examines the use of inferential statistics, specifically multivariate correlation and regression, as a means of interpreting LCA data. It is believed that these methods provide additional context in understanding data and results, and may serve as a way to present the uncertain results that are inherent to LCA. Methods Nine building envelope combinations were analyzed according to five service life models (N=45). Three environmental indicators were used: global warming potential, atmospheric ecotoxicity, and atmospheric acidification from the Tool for the Reduction and Assessment of Chemical and Other Environmental Impacts assessment method. Multivariate correlation was performed using nine variables, including cumulative life cycle impact, major replacement, major replacement (frequency), minor replacement, major repairs, minor repairs, inspections 1 and 2, and total transportation (N=45, 405 data points). The same data set was used for the regression analysis, although the variables were limited to major replacement, minor replacement, major repair, and minor repair (N=45, 225 data points). SPSS software was used for all statistical calculations. Results and discussion Multivariate correlation analysis showed strong, statistically significant correlations between cumulative life cycle impact and major replacement across all environmental indicators. Similarly, the regression analysis showed strong R 2 values between cumulative life cycle impact and major replacement, such that the influence of all other variables was considerably diminished. Conclusions The use of inferential statistics provides useful information with respect to the strength and statistical significance of correlations between variables as in multivariate correlation, and allows for predictive capacity of impact, as demonstrated through regression analysis. Further studies should be conducted to confirm the added value of these analytical tools.