دانلود کتاب رگرسیون خطی کاربردی برای تجزیه و تحلیل کسب و کار با پایتون

In regression analysis, the concepts to be learned are mathematical and require programming and intuition. The mathematical and intuition based content can be taught in a traditional sense, but real-world scenarios are necessary to inspire students to remain interested. This book focuses on applied regression analysis for business students with an introduction to the Python programming language. It emphasizes illustrating and solving modern problems. Case studies from business are available within the text, along with clear and concise explanations of the theory, intuition, hands-on examples, and the coding required to use regression modeling. Each chapter contains the mathematical formulation and details of regression analysis and provides a practical in-depth breakdown using the Python programming language. Throughout the book, examples make use of a lightweight Python utility package developed for instructional clarity, built on standard Python libraries, to streamline repetitive tasks while preserving transparency in the modeling process. My interest in applied regression analysis developed through project work for the US Department of Defense, where regression models were developed to estimate delivery timelines for requisitions originating in the United States and destined for combatant commands. The experience underscored the importance of building models that are not only statistically sound, but also interpretable and actionable in real operational settings. This book stresses the practical usage of regression analysis, making it applicable to data sets encountered in everyday business. Specifically, while traditional books contain excessive theories and mathematical formulas, they can overwhelm business students without advanced mathematical knowledge. On the contrary, some books that stress intuitive lessons often omit the coding components, which prevents students from understanding the process. To address this gap, the book blends theory and application through fully worked real world examples accompanied by detailed, reproducible code. Learning through relevant business cases is essential in today’s business world. To that end, numerous practical applications and exercises are available in this book, allowing students to quickly apply complex math to the data sets they collect. In this digital era, businesses have become highly dependent on data-driven analytics to guide their decision process. A survey by the GMAC even found that companies are increasingly looking to hire talent in data science and business analytics for advanced positions. Due to the increasing demand, the number of programs and courses in business analytics and data science has drastically grown in recent years. For example, Miami Herbert Business School at the University of Miami currently offers multiple annual sections dedicated to regression analysis. However, there is no single textbook that fully addresses applied regression analysis for business students using Python as the primary analytical tool. Hence, the primary audience of this book will be (1) advanced undergraduate-level students studying business analytics and (2) graduate students studying business: including but not limited to students who study Master of Science in Business Analytics, Master of Science in Accounting, Master of Science in Tax, and Master of Business Administration. Nevertheless, this book suits advanced under graduate level students studying engineering, math, and statistics.