De Gruyter Bootstrapping: An Integrated Approach with Python and Stata

Bittmann explains the fundamentals of bootstrapping mainly for student and practicing social scientists-particularly in sociology, psychology, political science, and econometrics-but also for anyone else who uses statistical analysis. Benefits of the bootstrapping approach, he says, include requiring fewer assumptions about the data, high flexibility, and improved accuracy. Assuming knowledge of statistical concepts and methods at a second-course level, he covers basic statistical concepts, bootstrapping, confidence intervals, hypothesis testing, regression models, and applied programming examples. Annotation ©2021 Ringgold, Inc., Portland, OR (protoview.com) Bootstrapping is a conceptually simple statistical technique to increase the quality of estimates, conduct robustness checks and compute standard errors for virtually any statistic. This book provides an intelligible and compact introduction for students, scientists and practitioners. It not only gives a clear explanation of the underlying concepts but also demonstrates the application of bootstrapping using Python and Stata. Autorid: Felix Bittmann