Business Statistics

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 2nd 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