Stats: Data & Models (5版)

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【簡介】 Encourages statistical thinking using technology, innovative methods, and a sense of humour Inspired by the 2016 GAISE Report revision, Stats: Data and Models, 5th Edition by De Veaux, Velleman, and Bock uses innovative strategies to help students think critically about data, while maintaining the book's core concepts, coverage, and most importantly, readability. The authors make it easier for instructors to teach and for students to understand more complicated statistical concepts later in the course (such as the Central Limit Theorem). In addition, students get more exposure to large data sets and multivariate thinking, which better prepares them to be critical consumers of statistics in the 21st century. The 5th Edition’s approach to teaching Stats: Data and Models is revolutionary, yet it retains the book's lively tone and hallmark pedagogical features such as its Think/Show/Tell Step-by-Step Examples. 【目錄】 Part I: Exploring and Understanding Data 1. Stats Starts Here 2. Displaying and Describing Data 3. Relationships Between Categorical Variables—Contingency Tables 4. Understanding and Comparing Distributions 5. The Standard Deviation as a Ruler and the Normal Model Part II: Exploring Relationships Between Variables 6. Scatterplots, Association, and Correlation 7. Linear Regression 8. Regression Wisdom 9. Multiple Regression Part III: Gathering Data 10. Sample Surveys 11. Experiments and Observational Studies Part IV: Randomness and Probability 12. From Randomness to Probability 13. Probability Rules! 14. Random Variables 15. Probability Models Part V: Inference for One Parameter 16. Sampling Distribution Models and Confidence Intervals for Proportions 17. Confidence Intervals for Means 18. Testing Hypotheses 19. More About Tests and Intervals Part VI: Inference for Relationships 20. Comparing Groups 21. Paired Samples and Blocks 22. Comparing Counts 23. Inferences for Regression Part VII: Inference When Variables Are Related 24. Multiple Regression Wisdom 25. Analysis of Variance 26. Multifactor Analysis of Variance 27. Introduction to Statistical Learning and Data Science

Statistics for Chemical Engineers: From Data to Models to Decisions (1版)

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【簡介】 Build a firm foundation for studying statistical modelling, data science, and machine learning with this practical introduction to statistics, written with chemical engineers in mind. It introduces a data-model-decision approach to applying statistical methods to real-world chemical engineering challenges, establishes links between statistics, probability, linear algebra, calculus, and optimization, and covers classical and modern topics such as uncertainty quantification, risk modelling, and decision-making under uncertainty. Over 100 worked examples using Matlab and Python demonstrate how to apply theory to practice, with over 70 end-of-chapter problems to reinforce student learning, and key topics are introduced using a modular structure, which supports learning at a range of paces and levels. Requiring only a basic understanding of calculus and linear algebra, this textbook is the ideal introduction for undergraduate students in chemical engineering, and a valuable preparatory text for advanced courses in data science and machine learning with chemical engineering applications.

Network Models for Data Science: Theory, Algorithms, and Applications

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This text on the theory and applications of network science is aimed at beginning graduate students in statistics, data science, computer science, machine learning, and mathematics, as well as advanced students in business, computational biology, physics, social science, and engineering working with large, complex relational data sets. It provides an exciting array of analysis tools, including probability models, graph theory, and computational algorithms, exposing students to ways of thinking about types of data that are different from typical statistical data. Concepts are demonstrated in the context of real applications, such as relationships between financial institutions, between genes or proteins, between neurons in the brain, and between terrorist groups. Methods and models described in detail include random graph models, percolation processes, methods for sampling from huge networks, network partitioning, and community detection. In addition to static networks the book introduces dynamic networks such as epidemics, where time is an important component.