Statistical Analysis of Empirical Data

Scott A. Pardo, Ph.D., is a professional statistician, having worked in a wide variety of industrial contexts, including the U.S. Army Information Systems Command, satellite systems engineering, pharmaceutical development, and medical devices. He is the author of Empirical Modeling and Data Analysis for Engineers and Applied Scientists (Springer 2016). He is a Six Sigma Master Black Belt, an Accredited Professional Statistician (PStat™), and holds a Ph.D. in Industrial and Systems Engineering from the University of Southern California.

• Chapter 1: Fundamentals.- Chapter 2: Sample Statistics are NOT Parameters.- Chapter 3: Confidence.- Chapter 4: Multiplicity and Multiple Comparisons.- Chapter 5: Power and the Myth of Sample Size Determination.- Chapter 6: Regression and Model Fitting with Collinearity.- Chapter 7: Overparameterization.- Chapter 8: Ignoring Error Control Factors and Experimental Design.- Chapter 9: Generalized Linear Models.- Chapter 10: Mixed Models and Variance Components.- Chapter 11: Models, Models Everywhere...Model Selection.- Chapter 12: Bayesian Analyses.- Chapter 13: The Acceptance Sampling Game.- Chapter 14: Nonparametric Statistics - A Strange Name.- Chapter 15: Autocorrelated Data and Dynamic Systems.- Chapter 16: Multivariate Analysis and Classification.- Chapter 17: Time-to-Event: Survival and Life Testing.- Index.