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.
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
Watson Analytics is not only revolutionary but also unlike any other analytics solution. At the event, which is called: Analytics for All: Empowering Everyone to Know, customers will share how they're improving their business with Watson Analytics.
Predictive product and customer profitability analysis techniques get a significant boost from big data and analytics. So much so that the lines blur between the phases of the traditional "Plan, Do, Study, Act" cycle outlined by Dr. Edwards Deming.
Financial analytics software transforms the chief financial officer role from number cruncher to strategic business partner. This is accomplished with faster, more accurate forecasting, simplified and less conflict-laden planning and quicker decision making with real-time, trusted financial metrics.
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.