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Applied Statistics for All Disciplines with R and R Studio

Applied Statistics for All Disciplines with R and R Studio

Author(s): Ashok Singh

Edition: 1

Copyright: 2026

Pages: 232

Edition: 1

Copyright: 2026

Pages: 300

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ISBN 9798385147014

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Applied Statistics for All Disciplines with R and RStudio provides a clear, concise introduction to probability and statistical methods essential for understanding and conducting research across fields. Beginning with data types, summarization, and visualization, the book builds a strong foundation in probability, random variables, and key distributions, then progresses through sampling, statistical inference, and hypothesis testing with an especially clear explanation of p-values. Advanced topics such as ANOVA, general linear models, and multiple linear regression are presented in an accessible, applied manner, with attention to assumptions like normality. Using R and RStudio throughout, this text equips students and professionals in business, science, engineering, hospitality, nursing, and beyond with practical tools for data analysis, interpretation, and communication.

Introduction to Applied Statistics
Why This Book?

Chapter 1: An Overview of Statistics
Chapter 2: Data Summarization
Chapter 3: Introductory Probability
Chapter 4: RVs and Their Probability Distributions
Chapter 5: Sampling Distributions
Chapter 6: Confidence Interval Estimation and Testing Hypotheses
Chapter 7: Analysis of Variance (ANOVA) 
Chapter 8: Linear Regression
Chapter 9: Test of Goodness of Fit (GOF)
Chapter10: Chi-Square Tests for Categorical Data Analysis 

Ashok Singh

Ashok Singh is an applied statistics expert who earned his Ph.D. from Purdue University in West Lafayette, Indiana, in 1977. In addition to an extensive body of research, he has taught undergraduate and graduate courses in statistics, mathematics, and operations research at the New Mexico Institute of Mining & Technology in Socorro, New Mexico, from 1978 to 1991. From 1991 to 2005, he developed and taught undergraduate and graduate statistics courses in the Mathematical Sciences Department at UNLV and provided statistical support to the Superfund Technical Support program at the U.S. Environmental Protection Agency in Las Vegas. In January 2006, he transferred to the Department of Gaming in the William F. Harrah College of Hospitality at UNLV, where he continues to teach the Mathematics of Casino Games and advanced statistics courses, including a graduate-level data mining and machine learning class using software-based learning environments. He became chair of the Resorts, Gaming & Golf Management (RGGM) Department on July 1, 2021. His research interests include statistical and machine learning applications in hospitality and gaming, medical research, public health, and civil and environmental engineering, including transportation systems, supporting projects sponsored by local, state, and federal agencies as well as the private sector.

Applied Statistics for All Disciplines with R and RStudio provides a clear, concise introduction to probability and statistical methods essential for understanding and conducting research across fields. Beginning with data types, summarization, and visualization, the book builds a strong foundation in probability, random variables, and key distributions, then progresses through sampling, statistical inference, and hypothesis testing with an especially clear explanation of p-values. Advanced topics such as ANOVA, general linear models, and multiple linear regression are presented in an accessible, applied manner, with attention to assumptions like normality. Using R and RStudio throughout, this text equips students and professionals in business, science, engineering, hospitality, nursing, and beyond with practical tools for data analysis, interpretation, and communication.

Introduction to Applied Statistics
Why This Book?

Chapter 1: An Overview of Statistics
Chapter 2: Data Summarization
Chapter 3: Introductory Probability
Chapter 4: RVs and Their Probability Distributions
Chapter 5: Sampling Distributions
Chapter 6: Confidence Interval Estimation and Testing Hypotheses
Chapter 7: Analysis of Variance (ANOVA) 
Chapter 8: Linear Regression
Chapter 9: Test of Goodness of Fit (GOF)
Chapter10: Chi-Square Tests for Categorical Data Analysis 

Ashok Singh
Ashok Singh

Ashok Singh is an applied statistics expert who earned his Ph.D. from Purdue University in West Lafayette, Indiana, in 1977. In addition to an extensive body of research, he has taught undergraduate and graduate courses in statistics, mathematics, and operations research at the New Mexico Institute of Mining & Technology in Socorro, New Mexico, from 1978 to 1991. From 1991 to 2005, he developed and taught undergraduate and graduate statistics courses in the Mathematical Sciences Department at UNLV and provided statistical support to the Superfund Technical Support program at the U.S. Environmental Protection Agency in Las Vegas. In January 2006, he transferred to the Department of Gaming in the William F. Harrah College of Hospitality at UNLV, where he continues to teach the Mathematics of Casino Games and advanced statistics courses, including a graduate-level data mining and machine learning class using software-based learning environments. He became chair of the Resorts, Gaming & Golf Management (RGGM) Department on July 1, 2021. His research interests include statistical and machine learning applications in hospitality and gaming, medical research, public health, and civil and environmental engineering, including transportation systems, supporting projects sponsored by local, state, and federal agencies as well as the private sector.