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Engineering Mathematics
Engineering Mathematics

John Bird's approach to mathematics, based on numerous worked examples supported by problems, is ideal for students of a wide range of abilities. Theory is kept to a minimum, with the emphasis firmly placed on problem-solving skills, making this a thoroughly practical introduction to the mathematics engineering students need to...

R for Business Analytics
R for Business Analytics

R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages.  With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of...

Introduction to Engineering Statistics and Six Sigma: Statistical Quality Control and Design of Experiments and Systems
Introduction to Engineering Statistics and Six Sigma: Statistical Quality Control and Design of Experiments and Systems

Many have heard that six sigma methods are necessary to survive, let alone thrive, in today’s competitive markets, but are not really sure what the methods are or how or when to use them.

Introduction to Engineering Statistics and Six Sigma contains precise descriptions of all of the many related methods and details...

Essentials of Probability & Statistics for Engineers & Scientists
Essentials of Probability & Statistics for Engineers & Scientists
This text was designed for a one-semester course that covers the essential topics needed for a fundamental understanding of basic statistics and its applications in the fields of...
Foundations of Machine Learning (Adaptive Computation and Machine Learning series)
Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise...

Engineering Statistics
Engineering Statistics

Montgomery, Runger, and Hubele provide modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process.  All major aspects of engineering statistics are covered, including descriptive statistics, probability and probability distributions, statistical test and...

Linear Mixed-Effects Models Using R: A Step-by-Step Approach (Springer Texts in Statistics)
Linear Mixed-Effects Models Using R: A Step-by-Step Approach (Springer Texts in Statistics)

Linear mixed-effects models (LMMs) are an important class of statistical models that can be used to analyze correlated data. Such data are encountered in a variety of fields including biostatistics, public health, psychometrics, educational measurement, and sociology. This book aims to support a wide range of uses for the models by applied...

Mathematics for Econometrics
Mathematics for Econometrics

This book deals with a number of mathematical topics that are of great importance in the study of classical econometrics. There is a lengthy chapter on matrix algebra, which takes the reader from the most elementary aspects to the partitioned inverses, characteristic roots and vectors, symmetric, and orthogonal and positive (semi) definite...

Smoothing Spline ANOVA Models (Springer Series in Statistics)
Smoothing Spline ANOVA Models (Springer Series in Statistics)

Nonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the ample computing power in today's servers, desktops, and laptops, smoothing methods have been finding their ways into everyday data analysis by practitioners. While scores of...

Nonlinear Computer Modeling of Chemical and Biochemical Data
Nonlinear Computer Modeling of Chemical and Biochemical Data

Assuming only background knowledge of algebra and elementary calculus, and access to a modern personal computer, Nonlinear Computer Modeling of Chemical and Biochemical Data presents the fundamental basis and procedures of data modeling by computer using nonlinear regression analysis. Bypassing the need for intermediary analytical...

Introduction to Data Analysis and Graphical Presentation in Biostatistics with R: Statistics in the Large (SpringerBriefs in Statistics)
Introduction to Data Analysis and Graphical Presentation in Biostatistics with R: Statistics in the Large (SpringerBriefs in Statistics)

Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R. These include tools that are critical to dealing with missing data, which is a pressing scientific issue for those engaged in biostatistics. Readers will be equipped to run analyses and make graphical...

Machine Learning with R
Machine Learning with R

Learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications

Overview

  • Harness the power of R for statistical computing and data science
  • Use R to apply common machine learning algorithms with real-world applications
  • Prepare,...
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