Discovering Statistics Using R

This textbook provides a technical introduction to quantitative data analysis using the R programming language. It transitions from fundamental concepts, such as frequency distributions and central tendency, toward complex procedures including analysis of variance, multiple regression, and logistic modeling. The authors integrate software instruction with statistical theory, explaining how to write code, clean datasets, and interpret output. Throughout the chapters, a cast of fictional characters presents examples that illustrate how to test hypotheses and verify the assumptions required for various parametric and non-parametric tests.

Students and researchers in the social and behavioral sciences use this book to bridge the gap between abstract mathematics and practical computation. It serves those who need to move away from expensive proprietary software toward open-source tools for reproducible research. Readers finish the text with the ability to manage large data sets, perform sophisticated statistical operations, and generate publication-quality visualizations. By following the provided diagnostic checklists, they learn to identify model biases and report their scientific findings with greater accuracy.

Outward-looking, growth-minded and clear-cut; a moderate read.

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