Many probability books are written by mathematicians and have the built-in bias that the reader is assumed to be a mathematician coming to the material for its beauty. This textbook is geared towards beginning graduate students from a variety of disciplines whose primary focus is not necessarily mathematics for its own sake. Instead,...

This is the first book to show the capabilities of Microsoft Excel in teaching marketing statistics effectively. It is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical marketing problems. If understanding statistics isn’t your strongest suit, you are not especially...

Researchers and professionals in all walks of life need to use the many tools offered by the statistical world, but...

Fatal Exit: The Automotive Black Box Debate cuts through thirty years of political wrangling and institutional biases to provide an argument for the Motor Vehicle Event Data Recorder (MVEDR). This automotive equivalent...

Now it's easy to perform many of the most common statistical techniques when you use the SAS Enterprise Guide point-and-click interface to access the power of SAS. Emphasizing the practical aspects of the analysis, this example-rich guide shows you how to conduct a wide range of statistical analyses without any SAS programming required. One or...

This book integrates key tools and processes into a comprehensive program for developing more robust and reliable technology-based products. Drawing on their extensive product development experience, the authors present a complete process for ensuring product performance throughout the entire lifecycle, from understanding...

Bioinformatics is an emerging field in which statistical and computational techniques are used extensively to analyze and interpret biological data obtained from high-throughput genomic technologies. Genomic technologies allow us to monitor thousands of biological processes going on inside living organisms in one snapshot, and are...

• Harness the ability to build algorithms for unsupervised data using deep learning concepts with R
• Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models
• Build models relating to neural...