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A unique text that simplifies experimental business design and is dedicated to the R language  Business Experiments with R  offers a guide and explores the fundamentals of experiment business designs. The book fills a gap in the literature with its discussion of business statistics, addressing issues such as small samples, lack of normality, and data confounding. The author—a noted expert on the topic—puts the focus on the A/B tests (and their variants) that are widely used in industry but not typically covered in business statistics textbooks.  The text contains the tools needed to design and analyze two-treatment experiments (i.e., A/B tests) to answer business questions. The author highlights the strategic and technical issues involved in designing experiments that will truly affect organizations. The book then builds on the foundation laid in Part I and expands on multivariable testing. Today’s companies use experiments to solve a broad range of problems, and  Business Experiments with R  is an essential resource for any business student. This important text:  Presents the key ideas that business students need to know about experiments Offers a series of examples, focusing on specific business questions Helps develop the ability to frame ill-defined problems and determine what data and types of analysis provide information about each problem Contains supplementary material, such as data sets available to everyone and an instructor-only companion site featuring lecture slides and an answer key Written for students of general business, marketing, and business analytics,  Business Experiments with R  is an important text that helps to answer business questions by highlighting the strategic and technical issues involved in designing experiments that will truly affect organizations.

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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 <i>Fundamentals of Predictive Analytics with JMP(R)</i> bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. <p> First, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. Second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in JMP . <p> Using JMP 13 and JMP 13 Pro, this book offers the following new and enhanced features in an example-driven format: <p> <ul> <li>an add-in for Microsoft Excel <li>Graph Builder <li>dirty data <li>visualization <li>regression <li>ANOVA <li>logistic regression <li>principal component analysis <li>LASSO <li>elastic net <li>cluster analysis <li>decision trees <li><i>k</i>-nearest neighbors <li>neural networks <li>bootstrap forests <li>boosted trees <li>text mining <li>association rules <li>model comparison</ul> <p> With today’s emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis. <p> This book is part of the SAS Press program.

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