R Data Analysis without Programming

<p>The new edition of this innovative book <i>R Data Analysis without Programming</i> prepares the readers to quickly analyze data and interpret statistical results using R. Professor Gerbing has developed <i>lessR</i> a ground-breaking method in alleviating the challenges of R programming. The <i>lessR</i> extends R removing the need for programming. This edition expands upon the first edition’s introduction to R through <i>lessR</i> which enables the readers to learn how to organize data for analysis read the data into R and generate output without performing numerous functions and programming exercises first. With <i>lessR</i> readers can select the necessary procedure and change the relevant variables with simple function calls. The text reviews and explains basic statistical procedures with the <i>lessR</i> enhancements added to the standard R environment. Using <i>lessR</i> data analysis with R becomes immediately accessible to the novice user and easier to use for the experienced user.</p><p>Highlights along with content new to this edition include:</p><ul> <i> </i><p> </p> <li>Explanation and Interpretation of all data analysis techniques; much more than a computer manual this book shows the reader how to explain and interpret the results.</li> <i> </i><p> </p> <li>Introduces the concepts and commands reviewed in each chapter.</li> <i> </i><p> </p> <li>Clear relaxed writing style more effectively communicates the underlying concepts than more stilted academic writing.</li> <i> </i><p> </p> <li>Extensive margin notes highlight define illustrate and cross-reference the key concepts. When readers encounter a term previously discussed the margin notes identify the page number for the initial introduction.</li> <i> </i><p> </p> <li>Scenarios that highlight the use of a specific analysis followed by the corresponding R/<i>lessR</i> input output and an interpretation of the results.</li> <p><em>Numerous examples of output</em> from psychology business education and other social sciences that demonstrate the analysis and how to interpret results.</p> <i> </i><p> </p> <li>Two data sets are analyzed multiple times in the book provide continuity throughout.</li> <i> </i><p> </p> <li>Comprehensive: A wide range of data analysis techniques are presented throughout the book.</li> <i> </i><p> </p> <li>Integration with machine learning as regression analysis is presented from both the traditional perspective and from the modern machine learning perspective.</li> <i> </i><p> </p> <li>End of chapter <i>problems</i> help readers test their understanding of the concepts.</li> <p> </p> <li>A website at <i>www.lessRstats.com</i> that features the data sets referenced in both standard text and SPSS formats so readers can practice using R/<i>lessR</i> by working through the text examples and worked problems R/<i>lessR</i> videos to help readers better understand the program and more.</li> </ul><p>This book is ideal for graduate and undergraduate courses in statistics beyond the introductory course research methods and/or any data analysis course taught in departments of psychology business education and other social and health sciences; this book is also appreciated by researchers doing data analysis. Prerequisites include basic statistical knowledge though the concepts are explained from the beginning in the book. Previous knowledge of R is not assumed.</p>