Predictive analytics is the art of using historical & current data to make projections about what might happen in the future. Learn more for your business.
For output-error (OE) models and when using instrumental-variable (IV) methods, make sure that your model shows independence of e and u, and pay less attention to the results of the whiteness of e. In this case, the modeling focus is on the dynamics G and not the disturbance properties...
Regression is a simple, common, and highly useful data analysis technique, often colloquially referred to as "fitting a line." In its simplest form, regression fits a straight line between a one variable (feature) and another (label). In more complicated forms, regression can find non-linear...
Cause Analysis If a system module frequently refreshes dmesg logs, the data in the memory for storing kernel logs is inconsistent with that in the cache. In this case, the memory exported from the Black Box contains error data that is not updated in time, which causes parsing failure. OOM ...
Retail is a highly competitive business complicated by the relative novelty of online commerce, and retail profit margins have always been thin, leaving little room for error. Even slight adjustments in product selection and inventory management can greatly reduce stockouts or, at the other end of...
While a vulnerability assessment is usually automated to cover a wide variety of unpatched vulnerabilities, pen testing generally combines automated and manual techniques to help testers delve further into the vulnerabilities and exploit them to gain access to the network in a controlled environment. ...
How you find the standard error depends on what stat you need. For example, the calculation is different for the mean or proportion. When you are asked to find the sample error, you’re probably finding the standard error. That uses the following formula: s/√n. You mi...
Root cause analysis (RCA) is the quality management process by which an organization searches for the root of a problem, issue or incident after it occurs.
Anomaly detection is a very common way that organizations today harness machine learning. We know, of course, that accurate anomaly detection relies on a combination of historical data and ongoing statistical analysis. Importantly, these models are highly dependent on the data quality and sample sizes...
Statistics is so unique because it can go from health outcomes research to marketing analysis to the longevity of a light bulb. It’s a fun field because you really can do so many different things with it. Besa Smith President and Senior Scientist ...