{"product_id":"reasoning-with-data-9781462530267","title":"Reasoning with Data","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eEngaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, teaching notes, and slide decks.\u003cbr\u003e\u003cbr\u003e Pedagogical Features\u003cbr\u003e *Playful, conversational style and gra\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\"\u003ci\u003eReasoning with Data\u003c\/i\u003e takes a careful and principled approach to guiding readers gracefully from the traditional moorings of frequentist statistics into Bayesian analyses and the functionality and frontiers of the R platform. Stanton provides a range of clear explanations, examples, and practice exercises, fueled by his unbounded enthusiasm and rock-solid expertise. This book is an indispensable resource for undergraduate and graduate students across disciplines--as well as researchers--who want to extend their thinking and their research into where the future is headed.\"--Frederick L. Oswald, PhD, Department of Psychology, Rice University\u003cbr\u003e\u003cbr\u003e \"Offering an up-to-date and refreshing approach, this is a highly useful guide to the statistics our students will be using today, including Bayesian reasoning. Rather than providing an array of equations to memorize, the emphasis is on building conceptual knowledge. The equations that are provided are essential for understanding how to reason with statistics. I plan to use this book as as the text for the first in the series of statistical courses required for our doctoral students in education. It also would be appropriate for advanced undergraduates or anyone who wants to begin to use R. The book has a good focus on Bayesian inference, which is not covered consistently in stats courses, but is critical for the kinds of complex data we use in education and psychology.\"--Carol McDonald Connor, PhD, Chancellor's Professor, School of Education, University of California, Irvine\u003cbr\u003e\u003cbr\u003e \"What do R and traditional and Bayesian statistics have in common? They allow us to answer questions that are important for science and practice. Stanton has produced a wonderful book that will be useful for students as well as established scholars.\"--Herman Aguinis, PhD, Avram Tucker Distinguished Scholar and Professor of Management, George Washington University School of Business\u003cbr\u003e\u003cbr\u003e \"This may be an uncommon thing to say about a book on statistics, but \u003ci\u003eReasoning with Data\u003c\/i\u003e is enjoyable and entertaining--really! Stanton takes the reader on an experiential hands-on tour of random sampling, statistical inference, and drawing conclusions from numerical results. The concreteness of the presentation and examples will make it easy for the reader to intuitively grasp the fundamental concepts. The book is very timely because both Bayesian inference and R are becoming 'must-have' tools for social and behavioral scientists. At the same time, Stanton provides a solid grounding in the historical approach of null hypothesis significance testing, including both its strengths and weaknesses. This text should have a very wide audience, and would be appropriate as an upper-level undergraduate or entry-level graduate statistics text in any of the social sciences.\"--Emily A. Butler, PhD, Family Studies and Human Development, University of Arizona -Written with students and scholars in mind, this text is informative, reader-friendly, and, yes, enjoyable….Stanton emphasizes concepts, not formulas, and promotes hands-on examples. His timely introduction and coverage of the open-source R programming language for statistical data analysis is another strength of this text….This volume will be an invaluable addition to both undergraduate and graduate collections. Highly recommended. Upper-division undergraduates through faculty and professionals.--Choice Reviews, 8\/1\/2018\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eIntroduction\u003cbr\u003e Getting Started\u003cbr\u003e 1. Statistical Vocabulary\u003cbr\u003e Descriptive Statistics\u003cbr\u003e Measures of Central Tendency\u003cbr\u003e Measures of Dispersion\u003cbr\u003e Distributions and Their Shapes\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 2. Reasoning with Probability\u003cbr\u003e Outcome Tables\u003cbr\u003e Contingency Tables\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 3. Probabilities in the Long Run\u003cbr\u003e Sampling\u003cbr\u003e Repetitious Sampling with R\u003cbr\u003e Using Sampling Distributions and Quantiles to Think about Probabilities\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 4. Introducing the Logic of Inference Using Confidence Intervals\u003cbr\u003e Exploring the Variability of Sample Means with Repetitious Sampling\u003cbr\u003e Our First Inferential Test: The Confidence Interval\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 5. Bayesian and Traditional Hypothesis Testing\u003cbr\u003e The Null Hypothesis Significance Test\u003cbr\u003e Replication and the NHST\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 6. Comparing Groups and Analyzing Experiments\u003cbr\u003e Frequentist Approach to ANOVA\u003cbr\u003e Bayesian Approach to ANOVA\u003cbr\u003e Finding an Effect\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 7. Associations between Variables\u003cbr\u003e Inferential Reasoning about Correlation\u003cbr\u003e Null Hypothesis Testing on the Correlation\u003cbr\u003e Bayesian Tests on the Correlation Coefficient\u003cbr\u003e Categorical Associations\u003cbr\u003e Exploring the Chi-Square Distribution with a Simulation\u003cbr\u003e The Chi-Square Test with Real Data\u003cbr\u003e Bayesian Approach to Chi-Square Test\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 8. Linear Multiple Regression\u003cbr\u003e Bayesian Approach to Linear Regression\u003cbr\u003e A Linear Regression Model with Real Data\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 9. Interactions in ANOVA and Regression\u003cbr\u003e Interactions in ANOVA\u003cbr\u003e Interactions in Multiple Regression\u003cbr\u003e Bayesian Analysis of Regression Interactions\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 10. Logistic Regression\u003cbr\u003e A Logistic Regression Model with Real Data\u003cbr\u003e Bayesian Estimation of Logistic Regression\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 11. Analyzing Change over Time\u003cbr\u003e Repeated Measures Analysis\u003cbr\u003e Time-Series Analysis\u003cbr\u003e Exploring a Time Series with Real Data\u003cbr\u003e Finding Change Points in Time Series\u003cbr\u003e Probabilities in Change-Point Analysis\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 12. Dealing with Too Many Variables\u003cbr\u003e Internal Consistency Reliability\u003cbr\u003e Rotation\u003cbr\u003e Conclusion\u003cbr\u003e Exercises\u003cbr\u003e 13. All Together Now\u003cbr\u003e The Big Picture\u003cbr\u003e Appendix A. Getting Started with R\u003cbr\u003e Running R and Typing Commands\u003cbr\u003e Installing Packages\u003cbr\u003e Quitting, Saving, and Restoring\u003cbr\u003e Conclusion\u003cbr\u003e Appendix B. Working with Data Sets in R\u003cbr\u003e Data Frames in R\u003cbr\u003e Reading Data Frames from External Files\u003cbr\u003e Appendix C. Using dplyr with Data Frames\u003cbr\u003e References\u003cbr\u003e Index\u003c\/p\u003e","brand":"Guilford Publications","offers":[{"title":"Default Title","offer_id":49408639533399,"sku":"9781462530267","price":999.99,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/bookcurl.com\/products\/reasoning-with-data-9781462530267","provider":"Book Curl","version":"1.0","type":"link"}