Taxmann Business Statistics Book by Gurmeet Kaur, Soumya Sharma, Rachan Sareen

Business Statistics is a comprehensive, authentic, and well-illustrated textbook designed to introduce undergraduate students to core statistical concepts and applications. Recognising the essential role of statistics in today's data-driven environment, the book equips learners with the methods needed for data analysis, interpretation, and informed decision-making. The presentation strikes a balance between theoretical rigour and practical application, ensuring students can confidently apply statistical tools to practical business and economic scenarios.

This book will be helpful for the following audience:

• Undergraduate Students (Commerce & Management) – This book is aligned with the B.Com. (Hons.) and B.Com. syllabi under the new UGCF-NEP 2020 framework of the University of Delhi, as well as other universities in India and abroad
• Instructors & Faculty – Offers a structured pathway and a robust resource base for teaching fundamental to intermediate-level statistics with relevant examples and exercises
• Lifelong Learners & Budding Researchers – Those looking to develop a solid foundational understanding of statistical methods and their application in business settings will find this book indispensable

The Present Publication is the 2^nd Edition, authored by Dr Gurmeet Kaur, Soumya Sharma, and Dr Rachan Sareen, with the following noteworthy features:

• [Student-centric Design] Closely follows the B.Com. (Hons.) and B.Com. course structures under the UGCF-NEP 2020 Framework. Its simple language ensures a clear understanding of fundamental and advanced concepts
• [Solved Examples] Each chapter contains numerous worked-out examples that demonstrate key statistical techniques step-by-step
• [Practice Problems] End-of-chapter exercises ranging from conceptual questions and numerical problems to data-analysis tasks. It encourages hands-on learning and promotes critical thinking
• [Formula Summaries] Convenient formula lists for quick revision and reference, helping students to review essential concepts efficiently
• [Excel-based Exercises] Practical demonstrations and exercises using Excel, equipping students with industry-relevant data manipulation and statistical skills
• [Comprehensive Resource] Serves as a classroom text and a reference guide for deeper statistical exploration. It is appropriate for preparing assignments, projects, and university examinations
• [Robust Author Expertise] The authors combine decades of teaching and research experience, making the content authentic, academically rigorous, and learner-friendly

The coverage of the book is as follows:

• Descriptive Statistics
  • Measures of Central Tendency (Arithmetic Mean, Median, Mode)
  • Measures of Dispersion (Range, Quartile Deviation, Mean Deviation, Standard Deviation, Variance, Coefficient of Variation)
  • Moments, Skewness, and Kurtosis
• Probability & Probability Distributions
  • Fundamental Probability Theorems (Addition, Multiplication, Conditional Probability, Bayes' Theorem)
  • Discrete Distributions: Binomial and Poisson (Properties & Applications)
  • Normal Distribution (Properties, Computing Probabilities & Applications)
• Correlation and Regression Analysis
  • Correlation Concept and Computation (Pearson's Coefficient, Rank Correlation, Probable Errors)
  • Regression Lines, Regression Equations, Properties of Coefficients, Standard Error of Estimate
• Time Series Analysis
  • Components of Time Series: Additive and Multiplicative Models
  • Trend Fitting (Least Squares Method for Linear & Second-Degree Parabola)
  • Shifting Origin, Converting Annual Equations to Quarterly/Monthly Basis
• Index Numbers
  • Construction & Uses (Laspeyres, Paasche, Fisher's Ideal Index)
  • Consumer Price Indices, BSE SENSEX, NSE NIFTY
• Practical Exercises & Projects
  • Practical application via group research/surveys, data collection, and statistical analysis
  • Detailed guidance on spreadsheet-based problem-solving using Excel or open-source tools such as R/Python

The structure of the book is as follows:

• Chapter Organisation
  • Begins with a clear outline and learning objectives
  • Progresses systematically from foundational concepts to advanced topics
  • Includes logical sequencing to match typical undergraduate coursework coverage and timelines
• Pedagogical Tools
  • Step-by-step solutions and illustrative examples
  • Chapter-end practice sets for testing and reinforcing understanding
  • Quick-reference formula summaries and essential concept recaps
• Additional Spreadsheet/Software Section
  • Dedicated coverage on spreadsheet-based (Excel) or open-source software (R, Python) exercises
  • Helps translate theoretical knowledge into practical data analytics skills