{"product_id":"statistical-inference-via-data-science-a-moderndive-into-r-and-the-tidyverse-paperback","title":"Statistical Inference via Data Science: A ModernDive into R and the Tidyverse - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eChester Ismay\u003c\/b\u003e (Author), \u003cb\u003eAlbert Y. Kim\u003c\/b\u003e (Author), \u003cb\u003eArturo Valdivia\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eStatistical Inference via Data Science: A ModernDive into R and the Tidyverse, Second Edition\u003c\/b\u003e offers a comprehensive guide to learning statistical inference with data science tools widely used in industry, academia, and government. The first part of this book introduces the \u003cstrong\u003etidyverse\u003c\/strong\u003e suite of R packages, including \u003cstrong\u003eggplot2 \u003c\/strong\u003efor data visualization and \u003cb\u003edplyr \u003c\/b\u003efor data wrangling. The second part introduces data modeling via simple and multiple linear regression. The third part presents statistical inference using simulation-based methods within a general framework implemented in R via the \u003cb\u003einfer \u003c\/b\u003epackage, a suitable complement to the \u003cstrong\u003etidyverse.\u003c\/strong\u003e By working with these methods, readers can implement effective exploratory data analyses, conduct statistical modeling with data, and carry out statistical inference via confidence intervals and hypothesis testing. All of these tasks are performed by strongly emphasizing data visualization.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey Features in the Second Edition: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003e \u003cb\u003eMinimal Prerequisites\u003c\/b\u003e: No prior calculus or coding experience is needed, making the content accessible to a wide audience.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eReal-World Data\u003c\/b\u003e: Learn with real-world datasets, including all domestic flights leaving New York City in 2023, the Gapminder project, FiveThirtyEight.com data, and new datasets on health, global development, music, coffee quality, and geyser eruptions.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eSimulation-Based Inference\u003c\/b\u003e: Statistical inference through simulation-based methods.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eExpanded Theoretical Discussions\u003c\/b\u003e: Includes deeper coverage of theory-based approaches, their connection with simulation-based approaches, and a presentation of intuitive and formal aspects of these methods.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eEnhanced Use of the infer Package\u003c\/b\u003e: Leverages the \u003cstrong\u003einfer\u003c\/strong\u003e package for \"tidy\" and transparent statistical inference, enabling readers to construct confidence intervals and conduct hypothesis tests through multiple linear regression and beyond.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eDynamic Online Resources\u003c\/b\u003e: All code and output are embedded in the text, with additional interactive exercises, discussions, and solutions available online.\u003c\/li\u003e \u003cli\u003e \u003cb\u003eBroadened Applications\u003c\/b\u003e: Suitable for undergraduate and graduate courses, including statistics, data science, and courses emphasizing reproducible research.\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eThe first edition of the book has been used in so many different ways--for courses in statistical inference, statistical programming, business analytics, and data science for social policy, and by professionals in many other means. Ideal for those new to statistics or looking to deepen their knowledge, this edition provides a clear entry point into data science and modern statistical methods.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eChester Ismay\u003c\/b\u003e is Vice President of Data and Automation at MATE Seminars and is a freelance data science consultant and instructor. He also teaches in the Center for Executive and Professional Education at Portland State University. He completed his PhD in statistics from Arizona State University in 2013. He has previously worked in various roles, including as an actuary at Scottsdale Insurance Company (now Nationwide E\u0026amp;S\/Specialty) and at Ripon College, Reed College, and Pacific University. He has experience working in online education and was previously a Data Science Evangelist at DataRobot, where he led data science, machine learning, and data engineering in-person and virtual workshops for DataRobot University. In addition to his work for *ModernDive*, he contributed as the initial developer of the `infer` R package and is the author and maintainer of the `thesisdown` R package.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAlbert Y. Kim\u003c\/b\u003e is an Associate Professor of Statistical \u0026amp; Data Sciences at Smith College in Northampton, MA, USA. He completed his PhD in statistics at the University of Washington in 2011. Previously he worked in the Search Ads Metrics Team at Google Inc.\\ as well as at Reed, Middlebury, and Amherst Colleges. In addition to his work for *ModernDive*, he is a co-author of the `resampledata` and `SpatialEpi` R packages. Both Dr. Kim and Dr. Ismay, along with Jennifer Chunn, are co-authors of the `fivethirtyeight` package of code and datasets published by the data journalism website FiveThirtyEight.com.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eArturo Valdivia\u003c\/b\u003e is a Senior Lecturer in the Department of Statistics at Indiana University, Bloomington. He earned his PhD in Statistics from Arizona State University in 2013. His research interests focus on statistical education, exploring innovative approaches to help students grasp complex ideas with clarity. Over his career, he has taught a wide range of statistics courses, from introductory to advanced levels, to more than 1,800 undergraduate students and over 900 graduate students pursuing master's and Ph.D. programs in statistics, data science, and other disciplines. In recognition of his teaching excellence, he received Indiana University's Trustees Teaching Award in 2023.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 456\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.99 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e May 02, 2025\u003c\/div\u003e\n            ","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":51887992111392,"sku":"9781032708379","price":149.02,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0974\/9764\/5344\/files\/jkhtfM2-qh9781032708379.webp?v=1781908764","url":"https:\/\/ebocreations.com\/products\/statistical-inference-via-data-science-a-moderndive-into-r-and-the-tidyverse-paperback","provider":"The E-Book Oasis LLC","version":"1.0","type":"link"}