Hundreds of engaging, class-tested statistics exercises (and detailed solutions) that test student understanding of the material. Many are educational in their own rightfor example, baseball managers who played professional ball were often catche...Daugiau...
Hundreds of engaging, class-tested statistics exercises (and detailed solutions) that test student understanding of the material. Many are educational in their own rightfor example, baseball managers who played professional ball were often catche...Daugiau...
Students cultivate learning techniques in school that emphasize procedural problem solving and rote memorization. This leads to efficient problem solving for familiar problems. However, conducting novel research is an exercise in creative problem...Daugiau...
Exercises in Statistical Reasoning is a collection of exercises designed to strengthen creative problem-solving skills. The exercises are designed to encourage readers to understand the key points of a problem while seeking knowledge, rather than se...Daugiau...
Nonparametric statistical methods minimise the number of assumptions that need to be made about the distribution of data being analysed, unlike classical parametric methods. As such, they are an essential part of a statisticians armoury and this...Daugiau...
A Hands-On Way to Learning Data Analysis Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence...Daugiau...
Providing a flexible framework for data analysis and model building, this text focuses on the statistical methods and models that can help predict the expected value of an outcome, dependent, or response variable. It offers a sound introduction to...Daugiau...
This textbook, based on the authors course on causal inference at UC Berkeley taught over the past seven years, only requires basic knowledge of probability theory, statistical inference, and linear and logistic regressions. It assumes minimal kn...Daugiau...
Substantial update from the first edition based on experience of teaching from the book for nearly a decade. Organized as before, but two new substantive topics in the advanced topics chapter group testing and splines. Computing has been complet...Daugiau...
Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background. The author draws on twenty years o...Daugiau...
Data Science students and practitioners want to find a forecast that works and dont want to be constrained to a single forecasting strategy, Time Series for Data Science: Analysis and Forecasting discusses techniques of ensembl...Daugiau...
Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes. ...Daugiau...
The purpose of applying mathematical theory to the theory of statistical inference is to make it simpler and more elegant. Theory of Statistical Inference is concerned with the development of a type of optimization theory which can b...Daugiau...
For advanced undergraduate or non-major graduate students in Advanced Statistical Modeling or Regression II and courses in Generalized Linear Models, Longitudinal Data Analysis, Correlated Data, Multilevel Models. Material on R at the end of each cha...Daugiau...
Like the De Groot winning first edition, the second edition of Principles of Uncertainty is an accessible, comprehensive guide to the theory of Bayesian Statistics written in an appealing, inviting style, and packed with interesting examples....Daugiau...
This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that ar...Daugiau...
This book presents an introduction to linear modeling using the generalized linear mixed model (GLMM) as an overarchingconceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear mode...Daugiau...
This thoroughly updated and expanded second edition covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and...Daugiau...
This book is based on detailed notes prepared by Professor Hall for his signature class on large sample theory in parametric models, which he taught at the University of Rochester for many years, most recently in 2012. It provides a rigorous but a...Daugiau...
