How should data analysis be taught? How valid are the results? How should one deal with inhomogeneous data? What kinds of computing languages should be used, if used at all? These are but a few of the many challenging questions surrounding the fundamentals of data analysis. Data Analysis: What Can Be Learned from the Past 50 Years explores the historical and philosophical implications inherent in any study of statistical data analysis. This book addresses the needs of researchers who are working with larger, complicated data sets by offering an understanding of the significance of robust data sets, the implementation of software languages, and the use of models.

Rather than focus on specific procedures, this book concentrates on general insights that can be drawn from data analysis research. The author utilizes case studies to explore the impact of technological advances on data analysis techniques and other thought-provoking issues, including:

• Homogeneous, unstructured data

• Statistical pitfalls

• Singular value decomposition

• Nonlinear weighted least squares

• Simulation of stochastic models

• Scatter- and curve-plots

With plentiful examples that showcase best practices for working with challenges in the field, Data Analysis is an excellent supplement for courses on data analysis, robust statistics, data mining, and computational statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for applied statisticians working in the fields of business, engineering, and the life and health sciences.