1st Edition

Introductory Statistics A Conceptual Approach Using R

    520 Pages
    by Routledge

    518 Pages
    by Routledge

      This comprehensive and uniquely organized text is aimed at undergraduate and graduate level statistics courses in education, psychology, and other social sciences. A conceptual approach, built around common issues and problems rather than statistical techniques, allows students to understand the conceptual nature of statistical procedures and to focus more on cases and examples of analysis. Wherever possible, presentations contain explanations of the underlying reasons behind a technique. Importantly, this is one of the first statistics texts in the social sciences using R as the principal statistical package. Key features include the following.

      • Conceptual Focus – The focus throughout is more on conceptual understanding and attainment of statistical literacy and thinking than on learning a set of tools and procedures.
      • Problems and Cases – Chapters and sections open with examples of situations related to the forthcoming issues, and major sections ends with a case study. For example, after the section on describing relationships between variables, there is a worked case that demonstrates the analyses, presents computer output, and leads the student through an interpretation of that output.
      • Continuity of Examples – A master data set containing nearly all of the data used in the book’s examples is introduced at the beginning of the text. This ensures continuity in the examples used across the text.
      • Companion Website – A companion website contains instructions on how to use R, SAS, and SPSS to solve the end-of-chapter exercises and offers additional exercises.
      • Field Tested – The manuscript has been field tested for three years at two leading institutions.

      1. Introduction and Background  II. Descriptive Statistics  2. Describing Quantitative Data with Frequency Distributions  3. Describing Quantitative Data: Summary Statistics  4. Describing Categorical Data: Frequency Distributions, Graphics, and Summary Statistics  5. Describing the Position of a Case within a Set of Scores  6. Describing the Relationship between Two Quantitative Variables: Correlation  7. Describing the Relationship between Two Quantitative Variables: Regression  III. The Fundamentals of Statistical Inference  8. The Essentials of Probability  9. Probability and Sampling Distributions  10. The Normal Distribution  IV. Statistical Inference  11. The Basics of Statistical Inference: Tests of Location  12. Other One-Sample Tests for Location  13. More One-Sample Tests  14. Two-Sample Tests of Location  15. Other Two-Sample Tests: Variability and Relationships  V. K-Sample Tests  16. Tests on Location: Analysis of Variance and Other Selected Procedures  17. Multiple Comparison Procedures  18. Looking Back… and Beyond  Appendix A: Statistical Tables  Appendix B: Getting Started with R  Index


      William B. Ware is Professor at the University of North Carolina at Chapel Hill, USA.

      John M. Ferron is Professor at the University of South Florida, USA.

      Barbara M. Miller is Associate Professor at Elon University, USA.