| Author | Peter Goos, David Meintrup |
| Publisher | Wiley |
| Year | 2015 |
| Language | English |
| Pages | 368 |
| Size | 7.92 MB |
| Extension |
Summary
"Statistics with JMP: Graphs, Descriptive Statistics and Probability" by Peter Goos and David Meintrup, published by Wiley in 2015, is a comprehensive textbook that introduces readers to the fundamentals of statistics and probability through the lens of the powerful statistical software JMP. Spanning 368 pages, this book is designed for students, researchers, and professionals who are new to statistical analysis or seeking to enhance their skills with hands-on software integration. Unlike traditional statistics texts that may overwhelm with theory alone, Goos and Meintrup strike a perfect balance by combining rigorous mathematical foundations with practical, interactive applications using JMP, making complex concepts accessible and engaging. The book opens with an exploration of what statistics entails and why it is indispensable in today's data-driven world. It defines key terms and sets the stage for data representation, emphasizing the importance of visualizing information to uncover patterns and insights. From there, it dives into graphical methods, covering a wide array of charts and plots tailored to different data types—nominal, ordinal, and quantitative. Traditional visualizations like histograms, pie charts, and needle charts are explained alongside modern techniques such as mosaic plots, bubble plots, and heat maps. Each graph is not just described but demonstrated step-by-step in JMP, with screenshots and instructions that allow readers to replicate the processes effortlessly. This hands-on approach ensures that learners grasp not only the 'how' but also the 'why' behind each visualization, fostering a deeper understanding of data storytelling. The core of the text focuses on descriptive statistics, where the authors meticulously detail measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation, range). These are illustrated with real-world examples, such as analyzing consumer preferences in marketing or quality control in manufacturing, always tying back to JMP functionalities for computation. The book distinguishes itself by delving into the mathematical derivations behind these measures, providing transparency for those who appreciate the theoretical underpinnings while keeping explanations clear for practical users. Probability theory forms the latter half, introducing discrete and continuous distributions with clarity. Binomial and Poisson distributions are explored for modeling count data, while exponential, normal, and lognormal densities address time-to-event and continuous phenomena. JMP's capabilities shine here, enabling readers to compute probabilities, simulate distributions, and visualize densities interactively. The authors use numerous case studies to illustrate applications, from risk assessment in finance to reliability engineering in product development. Throughout, the emphasis is on interpretation: how to draw meaningful conclusions from outputs and avoid common missteps like confusing correlation with causation. The book's structure is logical, progressing from basic data handling to advanced probabilistic modeling, with each chapter building on the previous. Exercises and datasets are provided via a companion website, encouraging active learning. At 7.92 MB in PDF format, it's portable and suitable for both classroom use and self-study. This text is particularly valuable for business, engineering, and science students, as JMP's user-friendly interface bridges the gap between statistical theory and professional practice. By the end, readers will be proficient in using JMP to explore data graphically, summarize it descriptively, and apply probability concepts confidently, equipping them for more advanced statistical endeavors or real-world analytical challenges. In an era where data literacy is paramount, "Statistics with JMP" stands out as an exemplary resource that transforms statistics from an abstract subject into a dynamic tool for discovery.
Key Features
- Introduces each concept with practical examples and demonstrations directly in JMP software.
- Provides in-depth statistical theory, including detailed mathematical derivations for transparency.
- Illustrative examples in every chapter, accompanied by step-by-step JMP instructions and screenshots.
- Covers a broad range of graphical representations, from traditional histograms to advanced mosaic and heat maps.
- Thorough overview of descriptive statistics for various data types: nominal, ordinal, and quantitative.
- Comprehensive treatment of probability theory, including discrete and continuous distributions like binomial, Poisson, normal, and lognormal.
- Companion website with datasets, teaching materials, and additional resources for hands-on practice.
- Balanced approach combining theory, computation, and application for diverse learners.
- Focus on interpretation and real-world applications in fields like business, engineering, and sciences.
- User-friendly structure suitable for self-study or classroom integration.
About Author
Peter Goos is a Full Professor of Statistics at KU Leuven (University of Leuven) in Belgium, where he serves in the Department of Biosystems at the Mechatronics, Biostatistics and Sensors (MeBioS) division. He also holds a position at the University of Antwerp's Faculty of Applied Economics. With a PhD in Applied Economics, Goos specializes in the statistical design and analysis of experiments, a field where he has published extensively in prestigious journals such as the Journal of the Royal Statistical Society and Marketing Science. His research has garnered over 5,800 citations on Google Scholar, reflecting his influence in applied statistics. Goos is an award-winning researcher, recognized for his contributions to optimal design theory and split-plot experiments. Beyond academia, he consults for industries and has authored several influential books, including collaborations on JMP-based statistics texts. His teaching philosophy emphasizes practical, software-driven learning, making complex statistical methods accessible to students and professionals alike. David Meintrup is a Professor of Mathematics and Statistics at Technische Hochschule Ingolstadt (Ingolstadt University of Applied Sciences) in Germany, within the Faculty of Mechanical Engineering. Holding dual doctorates (Dr. and Dr. rer. nat.), Meintrup focuses on mathematical modeling, statistical thinking, and problem-solving in engineering contexts. He is also a partner at Statcon, a consulting firm specializing in statistical applications for industry. Meintrup's work bridges academia and practice, with publications on topics like reliability analysis and data-driven decision-making. At Ingolstadt, he develops curricula that integrate software tools like JMP into statistical education, preparing students for real-world challenges in mechanical and industrial engineering. His collaboration with Goos on this book exemplifies his commitment to innovative teaching methods that combine theoretical rigor with interactive tools. Together, Goos and Meintrup bring decades of combined expertise, making "Statistics with JMP" a authoritative guide shaped by their passion for effective statistical education.
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Frequently Asked Questions
Q: What can I learn by reading this book ?
A: By reading this book, you can learn the fundamentals of descriptive statistics and probability theory, including graphical data representation, measures of central tendency and dispersion, discrete and continuous probability distributions, all applied using the JMP software with step-by-step instructions and real-world examples.
Q: This book is suitable for beginners?
A: Yes, this book is highly suitable for beginners, as it provides an accessible introduction to statistics with clear explanations, practical JMP demonstrations, and no advanced prerequisites required.
Q: This book is recommended for professionals?
A: Yes, it is recommended for professionals in fields like business, engineering, and sciences who want to leverage JMP for efficient data analysis and probabilistic modeling in their work.
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