Using Applied Econometrics with SAS: Modeling Demand, Supply, and Risk, you will quickly master SAS applications for implementing and estimating standard models in the field of econometrics.
Using Applied Econometrics with SAS: Modeling Demand, Supply, and Risk, you will quickly master SAS applications for implementing and estimating standard models in the field of econometrics.
Analysis of Clinical Trials Using SAS(R): A Practical Guide, Second Edition bridges the gap between modern statistical methodology and real-world clinical trial applications.
When it comes to business intelligence and analytical capabilities, SAS Visual Analytics is the premier solution for data discovery, visualization, and reporting.
When it comes to business intelligence and analytical capabilities, SAS Visual Analytics is the premier solution for data discovery, visualization, and reporting.
Explore biostatistics using JMP(R) in this refreshing introductionPresented in an easy-to-understand way, Introduction to Biostatistics with JMP introduces undergraduate students in the biological sciences to the most commonly used (and misused) statistical methods that they will need to analyze their experimental data using JMP.
This book provides hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP.
Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics.
Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics.
