This book offers readers the methods that are necessary to apply the power of calculus to analyze real problems. While most calculus textbooks focus on formula-based calculus, this book explains how to do the analysis of calculus, rates of change, and accumulation from data. The author’s introductory approach prepares students with the techniques to handle numerically-based problems in more advanced classes or in real-world applications. This self-contained book uses the computer algebra s...

Problem Solving is essential to solve real-world problems. Advanced Problem Solving with Maple: A First Course applies the mathematical modeling process by formulating, building, solving, analyzing, and criticizing mathematical models. It is intended for a course introducing students to mathematical topics they will revisit within their further studies.The authors present mathematical modeling and problem-solving topics using Maple as the computer algebra ...

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through...

Confusing Textbooks? Missed Lectures? Not Enough Time?Fortunately for you, there's Schaum's Outlines. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test y...

Financial modellingTheory, Implementation and Practice with MATLAB SourceJörg Kienitz and Daniel WetterauFinancial Modelling - Theory, Implementation and Practice with MATLAB Source is a unique combination of quantitative techniques, the application to financial problems and programming using Matlab. The book enables the reader to model, design and implement a wide range of financial models for derivatives pricing and asset allocatio...

This new color edition of Braun and Murdoch's bestselling textbook integrates use of the RStudio platform and adds discussion of newer graphics systems, extensive exploration of Markov chain Monte Carlo, expert advice on common error messages, motivating applications of matrix decompositions, and numerous new examples and exercises. This is the only introduction needed to start programming in R, the computing standard for analyzing data. Co-written by an R core team member and an establish...

This is the only introduction you'll need to start programming in R, the open-source language that is free to download, and lets you adapt the source code for your own requirements. Co-written by one of the R Core Development Team, and by an established R author, this book comes with real R code that complies with the standards of the language. Unlike other introductory books on the ground-breaking R system, this book emphasizes programming, including the principles that apply to most comp...

Books on information theory tend to fall into one of two extreme categories. There are large academic textbooks that cover the subject with great depth and rigor. Probably the best known of these is the book by Cover and Thomas. At the other extreme are the popular books such as the ones by Pierce and Gleick. They provide a very superficial introduction to the subject, enough to engage in cocktail party conversation but little else. This book attempts to bridge these two extremes.T...

Probability and Statistics for Data Science: Math + R + Data covers "math stat"—distributions, expected value, estimation etc.—but takes the phrase "Data Science" in the title quite seriously:* Real datasets are used extensively.* All data analysis is supported by R coding.* Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks....

An introduction to probability at the undergraduate levelChance and randomness are encountered on a daily basis. Authored by a highly qualified professor in the field, Probability: With Applications and R delves into the theories and applications essential to obtaining a thorough understanding of probability.With real-life examples and thoughtful exercises from fields as diverse as biology, computer science, cryptology, ecology, public health, and sports, the book ...

A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applicationsMore and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides t...

This book continues where DOE Simplified leaves off in Chapter 8 with an introduction to "Response Surface Methods [RSM] for Optimization." It presents this advanced tool for design of experiments (DOE) in a way that anyone with a minimum of technical training can understand and appreciate. Unlike any other book of its kind, RSM Simplified keeps formulas to a minimum—making liberal use of figures, charts, graphs and checklists. It also offers many relevant examples, amusing and fun do-it-y...