Learn how IBM SPSS Statistics can enhance the value that statistical analysis adds to a business, and find out how you can tap into the power of high-performance statistical modeling in your own organization.
Essentially, Monte Carlo simulations predict an outcome not from the actual values of input data (which aren’t known) but from the likely (aka “simulated”) values of that data (based on their probability distributions). These simulations can prove invaluable for assessing risks in many real-world
Although spreadsheets offer a stable, attractive option when working with numbers, they can fall far short when they are applied to enterprise-scale statistical analytics. Weigh the limitations of spreadsheets against the benefits of a sophisticated, enterprise-grade statistical analysis tool for
Open source tools continue to foster non-stop innovation throughout the Insight Economy. So it’s no surprise that open-source languages—most notably, R--have moved to the center of enterprise statistical analytics and data management.
Do you find yourself increasingly having to make decisions amid uncertain conditions? The advanced capabilities offered by IBM SPSS Statistics aim to make Monte Carlo simulation a part of your risk analysis by bringing these two worlds together in a single software solution.
Spreadsheets are excellent tools as far as they go—but how far can they truly go? If you’re pushing your spreadsheet-based solutions beyond their viable limits, then they might be doing more harm than good. Discover what considerations you shouldn’t ignore when using spreadsheets for statistical
Data analytics is no longer an either/or choice. With the integration of IBM SPSS Statistics and R, you can bring together the statistical analysis and data management capabilities that have helped so many data scientists gain insight after insight from their data.
An important ingredient for any successful business is its staff. And yet recent research shows that human resources ranks lowest among front office operations when it comes to using predictive analytics, particularly to recruit and retain the right professionals for the right positions. But that
Smart predictions can spell the difference between whether your company succeeds wildly or falls by the wayside. Get the details on four strategic pillars for smart, proactive business through predictive analytics deployment in a series of new blog perspectives.
Expand the boundaries of your possibility thanks to Apache Spark. Big data analysis is undergoing a paradigm shift powered by Spark, which supercharges the Hadoop ecosystem to help organizations accomplish things that were once thought impossible.
Inaccurate perceptions of predictive analytics are common in the business world. In reality, predictive analytics is straightforward to understand, can leverage existing skillsets in business and IT organizations, and can deliver value in most industries and lines of business. Getting started with
High-quality predictive analytics, statistical modeling and data mining tools are the heart of a well-run modern organization. Organizations of all sizes, in all sectors and geographies, are using these tools to drive evidence-based predictions into the full range of business processes, operations
Overfitting is an unfortunate consequence of top notch data scientists attempting to refine their statistical models. It stems from the tendency to skew data science models by starting with a biased set of project assumptions that drive selection of the wrong variables, the wrong data, the wrong
Data science by itself is an ineffectual civic-governance tool if it lacks strong champions who can wield it to get things done in the legislative, executive and judicial branches. Big data analytics can influence public policy if it helps frame a compelling case in the minds of decision makers for
A data scientist uses machine learning (ML) to find heretofore unknown correlations and other patterns in fresh data. ML is adept at finding both the "known unknowns" and the "unknown unknowns" through the power of supervised learning and unsupervised learning methodologies, respectively.