# Moving from IBM SPSS to R and RStudio : a statistics companion

Material type: TextPublication details: Los Angeles : SAGE, ©2022Description: xvi, 293 p. : col. ill. ; 23 cmISBN:- 9781071817001

- 005.5 TOK-M

Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|

Books | IIITD General Stacks | Computer Science and Engineering | 005.5 TOK-M (Browse shelf(Opens below)) | Available | 012948 |

Chapter 1. Introduction to R Chapter 2. Preparing to Use R and Rstudio Chapter 3. R Terms, Concepts, and Command Structure Chapter 4. Introduction to Rstudio Chapter 5. Conducting Rstudio Sessions: A Detailed Example Chapter 6. Conducting Rstudio Sessions: A Brief Example Chapter 7. Conducting Statistical Analyses Using This Book: A Detailed Example Chapter 8. Conducting Statistical Analyses Using This Book: A Brief Example Chapter 9. Working With Data Frames and Variables in R Chapter 10. Conducting Statistical Analyses Using SPSS Syntax

"Are you a researcher or instructor who has been wanting to learn R and RStudio®, but you don't know where to begin? Do you want to be able to perform all the same functions you use in IBM® SPSS® in R? Is your license to IBM® SPSS® expiring, or are you looking to provide your students guidance to a freely-available statistical software program? Moving from IBM® SPSS® to R and RStudio ®: A Statistics Companion is a concise and easy-to-read guide for users who want to know learn how to perform statistical calculations in R. Brief chapters start with a step-by-step introduction to R and RStudio, offering basic installation information and a summary of the differences. Subsequent chapters walk through differences between SPSS and R, in terms of data files, concepts, and structure. Detailed examples provide walk-throughs for different types of data conversions and transformations and their equivalent in R. Helpful and comprehensive appendices provide tables of each statistical transformation in R with its equivalent in SPSS and show what, if any, differences in assumptions factor to into each function. Statistical tests from t-tests to ANOVA through three-factor ANOVA and multiple regression and chi-square are covered in detail, showing each step in the process for both programs. By focusing just on R and eschewing detailed conversations about statistics, this brief guide gives adept SPSS users just the information they need to transition their data analyses from SPSS to R"--

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