Today’s economy, led by advances in changing economic cycles and communication, relies more on data science. Big data techniques, a significant component of data science and business intelligence, are utilized to harness vast amounts of data quickly for analysis. Increasingly, the volume and availability of granular data, coupled with highly specific and powerful analytical tools such as R and SAS drive organizations toward making more accurate predictions with the prospect of increasing sales and generating organizational efficiencies. These predictions help enable efficient supply chains, driving down costs for producers and leading to more expedient delivery of products and services for consumers.
The R squared value, called the coefficient of determination, determines how well the data points fit on a regression equation. More specifically, the R squared value is a measure of how the independent variables in a regression equation explain the variables of the dependent variable. The value of R squared can change based on the inclusion or removal of variables in the regression model. R squared values are typically used as a measure of the effectiveness of a model. Hence, a high R squared value (anything above 55%), can be an indicator of a capable model. Continue reading The implication of R-Squared
Its easy for large companies to spend large budget dollars on multi million dollar data initiatives, thereby taking risks that could yield significant rewards. Small to medium size companies dont always have the luxury of a large budget to build their analytics group or solution, and in many cases dont have the time, or patience, to wait for a solution to yield results. Large companies, while having the luxury of a budget, suffer from inertia, sometimes preventing progress.