Beginning Statistics

Author: Liam Foster
Publisher: SAGE
ISBN: 1473909228
Size: 11.53 MB
Format: PDF, ePub, Mobi
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In the Second Edition of this bestselling textbook, the authors use real-world examples to introduce basic principles in statistics with no prior knowledge or experience assumed. With an emphasis on describing concepts, showing through example and illustrating points with graphs and displays, this book will provide readers with a step-by-step introduction to using statistics. Chapters address the following questions: Why bother learning statistics in the first place and are they relevant to real life? How do I make sensible tables and informative graphs? What are descriptive and inferential statistics and how are they used? What are regression and correlation anyway?

Beginning Statistics

Author: Liam Foster
Publisher: SAGE
ISBN: 147390921X
Size: 64.28 MB
Format: PDF, ePub, Mobi
View: 6769
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In the Second Edition of this bestselling textbook, the authors use real-world examples to introduce basic principles in statistics with no prior knowledge or experience assumed. With an emphasis on describing concepts, showing through example and illustrating points with graphs and displays, this book will provide readers with a step-by-step introduction to using statistics. Chapters address the following questions: Why bother learning statistics in the first place and are they relevant to real life? How do I make sensible tables and informative graphs? What are descriptive and inferential statistics and how are they used? What are regression and correlation anyway?

Beginning Statistics

Author: Ian Diamond
Publisher: SAGE
ISBN: 1446223485
Size: 60.84 MB
Format: PDF, Docs
View: 3398
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`The clarity, simplicity and use of many practical examples makes this book very useful, primarily for under- and postgraduate students' - Journal of Biosocial Science With an emphasis on description, examples, graphs and displays rather than statistical formulae, this book is the ideal introductory guide for students across the social sciences. It shows how all students can understand the basic ideas of statistics at a level appropriate with being a good social scientist. The authors explain the right ways to present data, how to describe a set of data using summary statistics and how to infer what is going on in a population when all you have to go on is the sample. The book uses small data sets to help students understand the basic principles, and no mathematics or statistical background is assumed.

Quantitative Social Science

Author: Kosuke Imai
Publisher: Princeton University Press
ISBN: 1400885256
Size: 65.49 MB
Format: PDF, ePub
View: 4345
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Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics Includes more than forty data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides

Exploring Data

Author: Catherine Marsh
Publisher: Polity
ISBN: 0745622836
Size: 15.10 MB
Format: PDF, ePub
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The updated edition of this classic text introduces a range of techniques for exploring quantitative data. Beginning with an emphasis on descriptive statistics and graphical approaches, it moves on in later chapters to simple strategies for examining the associations between variables using inferential statistics such as chi squared. The book has been substantially revised to include the most recent approaches to data analysis, and includes step–by–step instructions on using SPSS. All these techniques are illustrated with intriguing real examples, drawn from important social research over the past three decades, designed to illuminate significant sociological and political debates. The book shows how students can use quantitative data to answer various questions: Is it true that the rich are getting richer and the poor are getting poorer? Are crime rates really going down, and how can we tell? How much alcohol do men and women really drink in an average week? Which country in Europe has the highest average working hours? Readers are encouraged to explore data for themselves, and are carefully guided through the opportunities and pitfalls of using statistical packages, as well as the numerous data sources readily available online. Suitable for those with no previous experience of quantitative data analysis, the second edition of Exploring Data will be invaluable to students across the social sciences. Visit the accompanying website at www.politybooks.com/exploringdata for more materials.

Introduction To Statistics With Spss For Social Science

Author: Gareth Norris
Publisher: Routledge
ISBN: 1317861833
Size: 36.84 MB
Format: PDF
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This is a complete guide to statistics and SPSS for social science students. Statistics with SPSS for Social Science provides a step-by-step explanation of all the important statistical concepts, tests and procedures. It is also a guide to getting started with SPSS, and includes screenshots to illustrate explanations. With examples specific to social sciences, this text is essential for any student in this area.

Big Data And Social Science

Author: Ian Foster
Publisher: CRC Press
ISBN: 1498751431
Size: 11.52 MB
Format: PDF
View: 6987
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Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website.

An Introduction To Statistical Learning

Author: Gareth James
Publisher: Springer Science & Business Media
ISBN: 1461471389
Size: 10.57 MB
Format: PDF, ePub, Docs
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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 to marketing to 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, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

Computational And Mathematical Modeling In The Social Sciences

Author: Scott de Marchi
Publisher: Cambridge University Press
ISBN: 113944686X
Size: 46.34 MB
Format: PDF
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Mathematical models in the social sciences have become increasingly sophisticated and widespread in the last decade. This period has also seen many critiques, most lamenting the sacrifices incurred in pursuit of mathematical rigor. If, as critics argue, our ability to understand the world has not improved during the mathematization of the social sciences, we might want to adopt a different paradigm. This book examines the three main fields of mathematical modeling - game theory, statistics, and computational methods - and proposes a new framework for modeling. Unlike previous treatments which view each field separately, the treatment provides a framework that spans and incorporates the different methodological approaches. The goal is to arrive at a new vision of modeling that allows researchers to solve more complex problems in the social sciences. Additionally, a special emphasis is placed upon the role of computational modeling in the social sciences.

Introduction To Applied Bayesian Statistics And Estimation For Social Scientists

Author: Scott M. Lynch
Publisher: Springer Science & Business Media
ISBN: 0387712658
Size: 39.70 MB
Format: PDF, ePub, Mobi
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This book outlines Bayesian statistical analysis in great detail, from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research - including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models - and it thoroughly develops each real-data example in painstaking detail.