Selected for originality, general interest, or because they demonstrate valuable techniques, the problems are ideal as a supplement to courses in probability or statistics, or as stimulating recreation for the mathematically minded. Detailed solutions. Illustrated.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, intuitive visual representations of real-world examples are used to show how Bayes' rule is actually a form of commonsense reasoning. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to gain an intuitive understanding of Bayesian analysis. As an aid to understanding, online computer code (in MatLab, Python and R) reproduces key numerical results and diagrams.Stone's book is renowned for its visually engaging style of presentation, which stems from teaching Bayes' rule to psychology students for over 10 years as a university lecturer.

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    Stewart's Calculus is successful throughout the world because he explains the material in a way that makes sense to a wide variety of readers. His explanations make ideas come alive, and his problems challenge, to reveal the beauty of calculus. Stewart's examples stand out because they are not just models for problem solving or a means of demonstrating techniques--they also encourage readers to develp an analytic view of the subject. This edition includes new problems, examples, and projects.

    This book develops statistical thinking over rote drill and practice. The Nature of Statistics; Organizing Data; Descriptive Measures; Probability Concepts; Discrete Random Variables; The Normal Distribution; The Sampling Distribution of the Sample Menu; Confidence Intervals for One Population Mean; Hypothesis Tests for One Population Mean; Inferences for Two Population Means; Inferences for Population Standard Deviations; Inferences for Population Proportions; Chi-Square Procedures; Descriptive Methods in Regression and Correlation; Inferential Methods in Regression and Correlation; Analysis of Variance (ANOVA) For all readers interested in Introductory Statistics.

    This supplement will cater to the requirements of students by covering all important topics of Laplace transformation, Matrices, Numerical Methods. Further enhanced is its usability by inclusion of chapter end questions in sync with student needs. Table of contents: 1. Basic Concepts 2. An Introduction to Modeling and Qualitative Methods 3. Classification of First-Order Differential Equations 4. Separable First-Order Differential Equations 5. Exact First-order Differential Equations 6. Linear First-Order Differential Equations 7. Applications of First-Order Differential Equations 8. Linear Differential Equations: Theory of Solutions 9. Second-Order Linear Homogeneous Differential Equations with Constant Coefficients 10. nth-Order Linear Homogeneous Differential Equations with Constant Coefficients 11. The Method of Undetermined Coefficients 12. Variation of Parameters 13. Initial-Value Problems for Linear Differential Equations 14. Applications of Second-Order Linear Differential Equations 15. Matrices 16. eAt 17. Reduction of Linear Differential Equations to a System of First-Order Equations 18. Existence and Uniqueness of Solutions 19. Graphical and Numerical Methods for Solving First-Order Differential Equations 20. Further Numerical Methods for Solving First-Order Differential Equations 21. Numerical Methods for Solving Second-Order Differential Equations Via Systems 22. The Laplace Transform 23. Inverse Laplace Transforms 24. Convolutions and the Unit Step Function 25. Solutions of Linear Differential Equations with Constant Coefficients by Laplace Transforms 26. Solutions of Linear?Systems by Laplace Transforms 27. Solutions of Linear Differential Equations with Constant Coefficients by Matrix Methods 28. Power Series Solutions of Linear Differential Equations with Variable Coefficients 29. Special Functions 30. Series Solutions N

    Beginning with the background and very nature of probability theory, the book then proceeds through sample spaces, combinatorial analysis, fluctuations in coin tossing and random walks, the combination of events, types of distributions, Markov chains, stochastic processes, and more. The book's comprehensive approach provides a complete view of theory along with enlightening examples along the way.

    Theoretical, yet applied. Statistics for Business and Economics, Eleventh Edition, gives you the best of both worlds. Using a rich array of applications from a variety of industries, McClave/Sincich/Benson clearly demonstrates how to use statistics effectively in a business environment.The book focuses on developing statistical thinking so the reader can better assess the credibility and value of inferences made from data. As consumers and future producers of statistical inferences, readers are introduced to a wide variety of data collection and analysis techniques to help them evaluate data and make informed business decisions. As with previous editions, this revision offers an abundance of applications with many new and updated exercises that draw on real business situations and recent economic events. The authors assume a background of basic algebra.

    This is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, least squares estimation, the bivariate normal distribution, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems. The book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.

    Essentially, you make an initial guess, and then get more data to improve it. Bayes Theorem, or Bayes Rule, has a ton of real world applications, from estimating your risk of a heart attack to making recommendations on Netflix But It Isn't That Complicated This book is a short introduction to Bayes Theorem. It is only 15 pages long, and is intended to show you how Bayes Theorem works as quickly as possible. The examples are intentionally kept simple to focus solely on Bayes Theorem without requiring that the reader know complicated probability distributions. If you want to learn the basics of Bayes Theorem as quickly as possible, with some easy to duplicate examples, this is a good book for you.

    Whether you're a student, a professional, or just curious about statistical analysis, Head First's brain-friendly formula helps you get a firm grasp of statistics so you can understand key points and actually use them. Learn to present data visually with charts and plots; discover the difference between taking the average with mean, median, and mode, and why it's important; learn how to calculate probability and expectation; and much more.Head First Statistics is ideal for high school and college students taking statistics and satisfies the requirements for passing the College Board's Advanced Placement (AP) Statistics Exam. With this book, you'll:Study the full range of topics covered in first-year statistics Tackle tough statistical concepts using Head First's dynamic, visually rich format proven to stimulate learning and help you retain knowledge Explore real-world scenarios, ranging from casino gambling to prescription drug testing, to bring statistical principles to life Discover how to measure spread, calculate odds through probability, and understand the normal, binomial, geometric, and Poisson distributions Conduct sampling, use correlation and regression, do hypothesis testing, perform chi square analysis, and moreBefore you know it, you'll not only have mastered statistics, you'll also see how they work in the real world. Head First Statistics will help you pass your statistics course, and give you a firm understanding of the subject so you can apply the knowledge throughout your life.

    The authors help students learn key sociological concepts through real research examples related to the dynamic interplay of race, class, gender, and other social variables.

    Fundamentals of Biostatistics with CD-Rom.