For courses in Statistical Methods for the Social Sciences . Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes you have no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 6th Edition gives you examples and exercises with a variety of “real data.� It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics–often a stumbling block for students–while avoiding reliance on an overly simplistic recipe-based approach to statistics.

Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department s history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history. "

For courses in introductory statistics. Statistics: The Art and Science of Learning from Data takes a conceptual approach, helping students understand what statistics is about and learning the right questions to ask when analysing data, rather than just memorising procedures. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible, without compromising the necessary rigor. Students will enjoy reading this book, and will stay engaged with its wide variety of real-world data in the examples and exercises. The authors believe that it's important for students to learn and analyse both quantitative and categorical data. As a result, the text pays greater attention to the analysis of proportions than many other introductory statistics texts. Concepts are introduced first with categorical data, and then with quantitative data.

For courses in introductory statistics. The art and science of learning from data Statistics: The Art and Science of Learning from Data takes a conceptual approach,helping students understand what statistics is about and learning the rightquestions to ask when analyzing data, rather than just memorizing procedures.This book takes the ideas that have turned statistics into a central science inmodern life and makes them accessible, without compromising the necessaryrigor. Students will enjoy reading this book, and will stay engaged with itswide variety of real-world data in the examples and exercises.

Shows the elements of statistical science that are highly relevant for students who plan to become data scientists less emphasis on probability theory and methods of probability such as combinatorics, derivations of probability distributions of transformations of random variables (except for explanations of t, chi-squared, and F constructions) Formal statements and proofs of theorems, and decision theory Introduces some modern topics that do not normally appear in "math stat" texts but are especially relevant for data scientists, such as generalized linear models for non-normal responses (e.g., logistic regression) Bayesian and regularized fitting of models (e.g., showing an example using the lasso), classification and clustering, and implementing methods with modern software (R and Python)

Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. This book consists of a set of memoirs, one for each department in the U.S. created by the mid-1960s. The memoirs describe key aspects of the department's history -- its founding, its growth, key people in its development, success stories (such as major research accomplishments) and the occasional failure story, PhD graduates who have had a significant impact, its impact on statistical education, and a summary of where the department stands today and its vision for the future. Read here all about how departments such as at Berkeley, Chicago, Harvard, and Stanford started and how they got to where they are today. The book should also be of interests to scholars in the field of disciplinary history.

Take your first steps into learning statistics, and understand the fascinating science of analysing data. Statistics: The Art and Science of Learning from Data, Global Edition, 5th edition by Agresti, Franklin, and Klingenberg is the ideal introduction to the discipline that will familiarise you with the world of statistics and data analysis. Ideal for students who study introductory courses in statistics, this text takes a conceptual approach and will encourage you to learn how to analyse data the right way by enquiring and searching for the right questions and information rather than just memorising procedures. Enjoyable and accessible, yet informative and without compromising the necessary rigour, this edition will help you engage with the science in modern life, delivering a learning experience that is effective in statistical thinking and practice. Key features include: Greater attention to the analysis of proportions compared to other introductory statistics texts. Introduction to key concepts, presenting the categorical data first, and quantitative data after. A wide variety of real-world data in the examples and exercises New sections and updated content will enhance your learning and understanding. Pearson MyLab (R) Students, if Pearson Pearson MyLab Statistics is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. Pearson MyLab Statistics should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. This title is a Pearson Global Edition. The Editorial team at Pearson has worked closely with educators around the world to include content which is especially relevant to students outside the United States.

A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: * An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods * An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems * Numerous examples that use R software for all text data analyses * More than 400 exercises for readers to practice and extend the theory, methods, and data analysis * A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5th Edition uses examples and exercises with a variety of "real data." It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics-often a stumbling block for students-while avoiding reliance on an overly simplistic recipe-based approach to statistics.