Introduction to Statistics through 'R' Programming Case Studies and Illustrations

This comprehensive guide introduces students and researchers to fundamental statistical concepts through R programming. Designed for postgraduate and research-level readers, the book presents a structured approach to essential statistical methods, making it valuable for those pursuing projects and advanced statistical applications. Covering topics such as measures of central tendency, hypothesis testing, small sample tests, ANOVA, correlation, regression, and forecasting, this text emphasizes practical applications with R coding examples. Authored with a commitment to clarity and systematic learning, this book aims to equip readers with both foundational knowledge and skills for integrating the latest advancements in R into their statistical analyses.

"Statistics and R Programming: An Applied Approach" is crafted to support students and researchers in mastering statistical analysis through the powerful R programming language. Targeting postgraduate and advanced learners, the book offers a thorough grounding in key statistical concepts including central tendency, hypothesis testing, ANOVA, correlation, regression, and trend analysis. Practical R coding examples are interwoven to demonstrate these concepts, enhancing students' analytical and coding skills. Written with the aim of making statistical concepts accessible and engaging, this book also acknowledges the guidance of the author’s mentors and family, whose support was integral in the creation of this educational resource.

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