Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include: Assessing if searches during a police stop in San Diego are dependent on driver’s race Visualizing the association between fat percentage and moisture percentage in Canadian cheese Modeling taxi fares in Chicago using data from millions of rides Analyzing mean sales per unit of legal marijuana products in Washington state Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook: Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory Relies on Minitab to present how to perform tasks with a computer Presents and motivates use of data that comes from open portals Focuses on developing an intuition on how the procedures work Exposes readers to the potential in Big Data and current failures of its use Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data Features an appendix with solutions to some practice problems Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.

All four arithmetic examples and exercises are provided with detailed and smooth versions of video teaching It is suitable to - Children with strong self-learning ability - Parents who train their children on their own - Kindergarten or Primary school teacher - Students majoring in early childhood education or elementary education in universities and colleges - Those who are interested in becoming an abacus and mental arithmetic teacher or are interested in running an abacus and mental arithmetic class

ADVANCES IN BUSINESS STATISTICS, METHODS AND DATA COLLECTION Advances in Business Statistics, Methods and Data Collection delivers insights into the latest state of play in producing establishment statistics, obtained from businesses, farms and institutions. Presenting materials and reflecting discussions from the 6th International Conference on Establishment Statistics (ICES-VI), this edited volume provides a broad overview of methodology underlying current establishment statistics from every aspect of the production life cycle while spotlighting innovative and impactful advancements in the development, conduct, and evaluation of modern establishment statistics programs. Highlights include: Practical discussions on agile, timely, and accurate measurement of rapidly evolving economic phenomena such as globalization, new computer technologies, and the informal sector. Comprehensive explorations of administrative and new data sources and technologies, covering big (organic) data sources and methods for data integration, linking, machine learning and visualization. Detailed compilations of statistical programs’ responses to wide-ranging data collection and production challenges, among others caused by the Covid-19 pandemic. In-depth examinations of business survey questionnaire design, computerization, pretesting methods, experimentation, and paradata. Methodical presentations of conventional and emerging procedures in survey statistics techniques for establishment statistics, encompassing probability sampling designs and sample coordination, non-probability sampling, missing data treatments, small area estimation and Bayesian methods. Providing a broad overview of most up-to-date science, this book challenges the status quo and prepares researchers for current and future challenges in establishment statistics and methods. Perfect for survey researchers, government statisticians, National Bank employees, economists, and undergraduate and graduate students in survey research and economics, Advances in Business Statistics, Methods and Data Collection will also earn a place in the toolkit of researchers working –with data– in industries across a variety of fields.

This book focuses on three core knowledge requirements for effective and thorough data analysis for solving business problems. These are a foundational understanding of: 1. statistical, econometric, and machine learning techniques; 2. data handling capabilities; 3. at least one programming language. Practical in orientation, the volume offers illustrative case studies throughout and examples using Python in the context of Jupyter notebooks. Covered topics include demand measurement and forecasting, predictive modeling, pricing analytics, customer satisfaction assessment, market and advertising research, and new product development and research. This volume will be useful to business data analysts, data scientists, and market research professionals, as well as aspiring practitioners in business data analytics. It can also be used in colleges and universities offering courses and certifications in business data analytics, data science, and market research.

An excellent book for commerce students appearing in competitive, professional and other examinations. Business Statistics 1.Statistics : Concept, Nature and Limitations, 2.Statistics : Scope and Significance, 3.Types and Collection of Data, 4. Classification and Tabulation of Data, 5. Frequency Distribution, 6. Graphic Presentation of Data, 7. Measures of Central Tendency (Mean, Median, Mode), 8. Measures of Variation or Dispersion (Rang, Q. D., M. D. & S. D.), 9. Measures of Skewness, 10. Measures of Kurtosis, 11. Correlation, 12. Regression Analysis, 13. Probability Theory, 14. Probability Distributions (Binomial, Poisson and Normal), 15. Sampling Theory and Tests of Significance. 16. Appendix. SYLLABUS Unit I : Statistics : Concept, Significance & Limitation, Type of Data, Classification & Tabulation, Frequency Distribution & Graphical Representation. Unit II : Measures of Central Tendency (Mean, Median, Mode), Measures of Variation : Significance & Properties of a Good Measure of Variation : Range, Quartile Deviation, Mean Deviation and Standard Deviation, Measures of Skewness & Kurtosis. Unit III :Correlation : Significance of Correlation, Types of correlation, Simple Correlation, Scatter Diagram Method, Karl Pearson Coefficient of Correlation. Regression : Introduction, Regression Lines, Regression Equation & Regression Coefficient. Unit IV :Probability : Concept, Events, Addition Law, Conditional Probability, Multiplication Law & Baye’s Theorem [Simple Numerical], Probability Distribution : Binomial, Poisson and Normal. Unit V : Sampling Method of Sampling, Sampling and Non-Sampling Errors. Test of Hypothesis, Type-I and Type-II Errors, Large Sample Tests.

1. Statistics : Meaning, Nature and Limitation, 2. Statistics : Scope and Importance, 3. Statistical Investigation, 4. Process of Data Collection, 5. Methods of Sampling, 6. Preparation of Questionnaire, 7. Classification and Tabulation of Data, 8. Measures of Central Tendency, 9. Quartiles, 10. Geometric Mean and Harmonic Mean, 11. Measures of Dispersion, 12. Measures of Skewness, 13. Analysis of Time Series, 14. Correlation, 15. Regression Analysis, 16. Regression Analysis, 17. Diagrammatic Presentation of Data, 18. Graphic Presentation of Data, Appendix (Log-Antilog Table).

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This technical note introduces analytics for compliance risk management in tax administration. Together with its accompanying toolkit, the note is intended as a starter kit to support capacity development in compliance planning, risk, and intelligence groups. Developed primarily for emerging analysts new to tax administration, the note presents both theory and practical aspects of analytics. Its toolkit is comprised of an initial collection of analytics templates designed to assist in turning the theory presented into practice in the areas of: (1) compliance planning; (2) taxpayer profiling; and (3) audit case selection.

The book guides the reader through the auditing and compliance process of the newly released ISO Artificial Intelligence standard. It provides tools and best practices on how to put together an AI management system that is certifiable and sheds light on ethical and legal challenges business leaders struggle with to make their AI system comply with existing laws and regulations, and the ethical framework of the organization. The book is unique because it provides implementation guidance on the new certification and conformity assessment process required by the new ISO Standard on Artificial Intelligence (ISO 42001:2023 Artificial Intelligence Management System) published by ISO in August 2023. This is the first book that addresses this issue. As a member of the US/ISO team who participated in the drafting of this standard during the last 3 years, the author has direct knowledge and insights that are critical to the implementation of the standard. He explains the context of how to interpret ISO clauses, gives examples and guidelines, and provides best practices that help compliance managers and senior leadership understand how to put together the AI compliance system to certify their AI system. The reader will find in the book a complete guide to the certification process of AI systems and the conformity assessment required by the standard. It also provides guidance on how to read the new EU AI Act and some of the U.S. legislations, such as NYC Local Law 144, enacted in July 2023. This is the first book that helps the reader create an internal auditing program that enhances the company’s AI compliance framework. Generative AI has taken the world by storm, and currently, there is no international standard that provides guidance on how to put together a management system that helps business leaders address issues of AI governance, AI structure, AI risk, AI audit, and AI impact analysis. ISO/IEC 42001:2023 is the first international mandatory and certifiable standard that provides a comprehensive and well-integrated framework for the issue of AI governance. This book provides a step-by-step process on how to implement the standard so the AI system can pass the ISO accreditation process.