Greg N. Data Science.
Angela Montanari. Modeling Count Data. Joseph M. Monte Carlo Statistical Methods. Christian Robert. Business Statistics for Competitive Advantage with Excel Cynthia Fraser. Jorge Ribeiro. An Introduction to Statistics with Python. Thomas Haslwanter. Applied Choice Analysis.
David A. Paul D. Artificial Intelligence in Financial Markets. Christian L. Stress Testing and Risk Integration in Banks. Tiziano Bellini. An Introduction to R for Quantitative Economics. Vikram Dayal. Introduction to Computational Genomics. Nello Cristianini. Machine Learning Using R. Karthik Ramasubramanian. Edward W. Cedric Gondro. R: Data Analysis and Visualization. Tony Fischetti. Il Do Ha. Assessing and Improving Prediction and Classification.
Timothy Masters. Statistics for Data Science. James D.
Jim Grayson. Elements of Probability and Statistics. Francesca Biagini. Portfolio Construction and Analytics. Frank J.
Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. cantrodalata.cf: Applied Data Mining for Forecasting Using SAS (): Tim Rey MS, Arthur Kordon PhD, Chip Wells PhD: Books.
Probabilistic Cellular Automata. Pierre-Yves Louis. Linda Herkenhoff. Developing, Validating and Using Internal Ratings. Giacomo De Laurentis. Julie Kezik. Harry M.
Heath Rushing. Matrix Algebra. James E. Madison Pruitt. JMP 14 Scripting Guide. SAS Institute. The Domain Name Game.
Yaseen Essack. Edward C.
Some features on this site might not work correctly. Kennesaw State University. Search KSU.
Statistics Home About. Requirements In order to get the certificate, students must: Complete 12 credit hours of courses listed in the Curriculum, including two core courses and at least two elective courses. Data Mining is an information extraction activity whose goal is to discover hidden facts contained in databases and perform prediction and forecasting through interaction with the data. The process includes data selection, cleaning and coding, using statistical pattern recognition and machine learning techniques, and reporting and visualizing the generated structures.
An Effect Summary report allows you drag and drop terms to see their impact on the model.
Whatever your favored model-building approach, JMP provides a complete set of manual and automated methods, with appropriate diagnostics, to allow you to rapidly build most types of linear models. Specific fitting options focus your attention appropriately; JMP Pro extends the repertoire by adding Mixed Models to correctly handle repeated and spatial measurements and Generalized Regression with regularized or penalized regression techniques like the Elastic Net that help identify X's that may have explanatory power.
JMP Pro also supports quantile regression. JMP lets you easily compare competing models. Multiple responses are handled in an integrated way, and the Profiler makes it simple to compare and contrast the interpretability and results of various fits.
The Profiler also allows you to find settings to optimize your Y's, and Monte Carlo simulations help you assess how variation in the X's will be transmitted into the Y's. The Nonlinear platform allows you to model nonlinear relationships. Nonlinear models use either standard least squares or a custom loss function. JMP provides a library of nonlinear model types needed for bioassay and pharmacokinetic studies, and does not require you to input starting values or auxiliary formulas.