Textbook in PDF format

In today’s data-driven world, the ability to analyze and interpret data is no longer optional?it’s a necessity for making informed business decisions. This book provides a comprehensive, hands-on guide to mastering the essential techniques and tools that empower business analysts to transform raw data into actionable insights. Packed with practical exercises and real-world case studies, this book focuses on applying statistical methods across the most widely used tools, including Excel, R, Python, and Generative AI platforms like ChatGPT. Key topics include framing analytical questions, descriptive and advanced statistical techniques, text analytics and A/B testing, and big data and visualization. Whether analyzing sales trends, optimizing marketing strategies, or improving operational efficiency, you’ll gain the skills to make data-backed decisions that drive business success. Before analysis can begin, data must be cleaned and prepared. This involves essential steps in data preparation, including data cleaning, shaping, and formatting, with practical examples and exercises to help readers develop the skills needed to prepare data for analysis using tools such as Excel and R. The book introduces a comprehensive suite of analysis tools that form the backbone of modern data analysis and statistical computing. Detailed instructions on how to install and use Excel’s Analysis ToolPak, R and its interface Jamovi, and generative AI tools like ChatGPT are provided. Descriptive statistics, fundamental to understanding data, are thoroughly explained, including measures of central tendency and variability. Through case studies, readers learn how to apply these techniques using Excel, R, and generative AI. The book also covers the creation and interpretation of histograms, providing practical exercises that demonstrate how to generate and interpret these visualizations. Pareto analysis is introduced as a method to identify the most significant factors in a dataset, with step-by-step instructions for conducting Pareto analyses using Excel, R, and generative AI. Scatterplots, used to examine relationships between variables, are explained with practical exercises on generating and interpreting these plots. The book then moves on to more advanced techniques, such as correlation analysis and linear regression, providing detailed case studies to show how to perform these analyses using various tools. Multivariate regression, extending linear regression to multiple predictors, is explained, with readers learning how to conduct and interpret these analyses through practical exercises and case studies. Features Includes hands-on exercises with industry-standard tools, including Excel, R, Python, and generative AI Covers step-by-step instructions for advanced techniques like time series forecasting and contingency analysis Uses real-world case studies to demonstrate the application of concepts to business scenarios Features companion files with datasets, surveys, and cases (available for downloading from the publisher) Preface Data Analysis and Business Framing Analytical Questions Data Preparation The Analysis Tools Descriptive Statistics Histograms Pareto Analysis Scatterplots Correlation and Linear Regression Multivariate Regression Forecasting and Time Series Inferential Statistics Contingency Analysis A/B Testing Text Analytics Analyzing Large Datasets Data Visualization Summary of Data Analysis Techniques Appendix: Data Sources