Fundamentals of Predictive Analytics With JMP. Questo articolo non è disponibile.Fundamentals of Predictive Analytics With JMP

Fundamentals of Predictive Analytics with JMP bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining/predictive analytics. This book provides the technical knowledge and problem-solving skills needed to perform real data multivariate analysis. Utilizing JMP 10 and JMP Pro, this book offers new and enhanced resources, including an add-in to Microsoft Excel, Graph Builder, and data mining capabilities. Written for students in undergraduate and graduate statistics courses, this book first teaches students to recognize when it is appropriate to use the tool, to understand what variables and data are required, and to know what the results might be. Second, it teaches them how to interpret the results, followed by step-by-step instructions on how and where to perform and evaluate the analysis in JMP. With the new emphasis on business intelligence, business analytics and predictive analytics, this book is invaluable to everyone who needs to expand their knowledge of statistics and apply real problem-solving analysis.SAS Products and Releases: JMP: 10.0.2, 10.0.1, 10.0 Operating Systems: All

Ron Klimberg is a professor at the Haub School of Business at Saint Joseph's University in Philadelphia, PA. Before joining the faculty in 1997, he was a professor at Boston University, an operations research analyst for the Food and Drug Administration, and a consultant. His primary research interests lie in the areas of multiple criteria decision making, DEA, facility location, data visualization, and data mining. Ron was the 2007 recipient of the Tengelmann Award for his excellence in scholarship, teaching, and research. He received his PhD from The Johns Hopkins University. B. D. McCullough is a professor at the LeBow College of Business at Drexel University in Philadelphia, PA. Prior to joining Drexel, he was a senior economist at the Federal Communications Commission and an assistant professor at Fordham University. His research fields include applied econometrics and time series, accuracy of statistical and econometrics software, replicability of research, and data mining. He received his PhD from the University of Texas at Austin.