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Statistics Using R - An Integrative Approach (2nd Revised edition): Sharon Lawner Weinberg, Daphna Harel, Sarah Knapp Abramowitz Statistics Using R - An Integrative Approach (2nd Revised edition)
Sharon Lawner Weinberg, Daphna Harel, Sarah Knapp Abramowitz
R2,034 Discovery Miles 20 340 Ships in 9 - 15 working days

Statistics Using R introduces the most up-to-date approaches to R programming alongside an introduction to applied statistics using real data in the behavioral, social, and health sciences. It is uniquely focused on the importance of data management as an underlying and key principle of data analysis. It includes an online R tutorial for learning the basics of R, as well as two R files for each chapter, one in Base R code and the other in tidyverse R code, that were used to generate all figures, tables, and analyses for that chapter. These files are intended as models to be adapted and used by readers in conducting their own research. Additional teaching and learning aids include solutions to all end-of-chapter exercises and PowerPoint slides to highlight the important take-aways of each chapter. This textbook is appropriate for both undergraduate and graduate students in social sciences, applied statistics, and research methods.

Statistics Using Stata - An Integrative Approach (3rd Revised edition): Sharon Lawner Weinberg, Sarah Knapp Abramowitz, Daphna... Statistics Using Stata - An Integrative Approach (3rd Revised edition)
Sharon Lawner Weinberg, Sarah Knapp Abramowitz, Daphna Harel
R2,035 Discovery Miles 20 350 Ships in 9 - 15 working days

Statistics Using Stata uses a highly accessible and lively writing style to seamlessly integrate the learning of the latest version of Stata (17) with an introduction to applied statistics using real data in the behavioral, social, and health sciences. The text is comprehensive in its content coverage and is suitable at undergraduate and graduate levels. It requires knowledge of basic algebra, but no prior coding experience. It is uniquely focused on the importance of data management as an underlying and key principle of data analysis. It includes a .do-file for each chapter, that was used to generate all figures, tables, and analyses for that chapter. These files are intended as models to be adapted and used by readers in conducting their own research. Additional teaching and learning aids include solutions to all end-of-chapter exercises and PowerPoint slides to highlight the important take-aways of each chapter.

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