An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Online Statistics: An Interactive Multimedia Course of Study is a resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab.This print edition of the public domain textbook gives the student an opportunity to own a physical copy to help enhance their educational experience. This part I features the book Front Matter, Chapters 1-10, and the full Glossary. Chapters Include:: I. Introduction, II. Graphing Distributions, III. Summarizing Distributions, IV. Describing Bivariate Data, V. Probability, VI. Research Design, VII. Normal Distributions, VIII. Advanced Graphs, IX. Sampling Distributions, and X. Estimation. Online Statistics Education: A Multimedia Course of Study (http: //onlinestatbook.com/). Project Leader: David M. Lane, Rice University.

This handbook aims to facilitate a greater understanding of the OECD statistics and indicators produced and so allow for their more effective use in policy analysis.

Provides a practical approach which helps teachers understand how to interpret student assessments statistically and how to measure and explain the validity and reliability of those assessments. This framework for measuring and interpreting assessment results is a must-have for your professional development library.

This handbook gathers in one volume the major research and scholarship related to multicultural science education that has developed since the field was named and established by Atwater in 1993. Culture is defined in this handbook as an integrated pattern of shared values, beliefs, languages, worldviews, behaviors, artifacts, knowledge, and social and political relationships of a group of people in a particular place or time that the people use to understand or make meaning of their world, each other, and other groups of people and to transmit these to succeeding generations. The research studies include both different kinds of qualitative and quantitative studies. The chapters in this volume reflect differing ideas about culture and its impact on science learning and teaching in different K-14 contexts and policy issues. Research findings about groups that are underrepresented in STEM in the United States, and in other countries related to language issues and indigenous knowledge are included in this volume.