Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific libraries
Key Features
Compute complex mathematical problems using programming logic with the help of step-by-step recipes
Learn how to utilize Python's libraries for computation, mathematical modeling, and statistics
Discover simple yet effective techniques for solving mathematical equations and apply them in real-world statistics
Book Description
Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.
The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.
By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
What you will learn
Get familiar with basic packages, tools, and libraries in Python for solving mathematical problems
Explore various techniques that will help you to solve computational mathematical problems
Understand the core concepts of applied mathematics and how you can apply them in computer science
Discover how to choose the most suitable package, tool, or technique to solve a certain problem
Implement basic mathematical plotting, change plot styles, and add labels to the plots using Matplotlib
Get to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methods
Who this book is for
This book is for professional programmers and students looking to solve mathematical problems computationally using Python. Advanced mathematics knowledge is not a requirement, but a basic knowledge of mathematics will help you to get the most out of this book. The book assumes familiarity with Python concepts of data structures.
Автор: Siegel, Andrew F. Название: Practical Business Statistics ISBN: 0128042508 ISBN-13(EAN): 9780128042502 Издательство: Elsevier Science Рейтинг: Цена: 12225.00 р. Наличие на складе: Поставка под заказ.
Описание: Practical Business Statistics, Seventh Edition, provides a conceptual, realistic, and matter-of-fact approach to managerial statistics that carefully maintains, but does not overemphasize mathematical correctness. The book provides deep understanding of how to learn from data and how to deal with uncertainty while promoting the use of practical computer applications. This valuable, accessible approach teaches present and future managers how to use and understand statistics without an overdose of technical detail, enabling them to better understand the concepts at hand and to interpret results. . The text uses excellent examples with real world data relating to business sector functional areas such as finance, accounting, and marketing. Written in an engaging style, this timely revision is class-tested and designed to help students gain a solid understanding of fundamental statistical principles without bogging them down with excess mathematical details.
Описание: Experts on handling longitudinal & multiple-group data contribute various, practical, analytic approaches including SEM,LTA,GLM, and multi-level techniques.Includes helpful tips, applications and examples. Grad students & researchers in social & behavior
Автор: Bailer-Jones Coryn A L Название: Practical Bayesian Inference ISBN: 1316642216 ISBN-13(EAN): 9781316642214 Издательство: Cambridge Academ Рейтинг: Цена: 6018.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This volume introduces the major concepts of probability and statistics and the computational tools students need to extract information from data in the presence of uncertainty. Using a simple and intuitive Bayesian approach, the emphasis throughout is on the principles and showing how these methods can be implemented in practice.
Описание: This book shows the capabilities of Microsoft Excel in teaching health services management statistics effectively. Similar to the previously published Excel 2016 for Health Services Management Statistics, this book is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical health services management problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically inclined, or if you are wary of computers, this is the right book for you. Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in health services courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2019 for Health Services Management Statistics: A Guide to Solving Practical Problems, 2nd Edition capitalizes on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work. Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand health services management problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full practice test (with answers in an appendix) that allows readers to test what they have learned.
Newly revised for Excel 2019, this text is a step-by-step guide for students taking a first course in statistics for advertising and for advertising managers and practitioners who want to learn how to use Excel to solve practical statistics problems in the workplace, whether or not they have taken a course in statistics.
Excel 2019 for Advertising Statistics explains statistical formulas and offers practical examples for how students can solve real-world advertising statistics problems. Each chapter offers a concise overview of a topic, and then demonstrates how to use Excel commands and formulas to solve specific advertising statistics problems. This book demonstrates how to use Excel 2019 in two different ways: (1) writing formulas (e.g., confidence interval about the mean, one-group t-test, two-group t-test, correlation) and (2) using Excel's drop-down formula menus (e.g., simple linear regression, multiple correlation and multiple regression, and one-way ANOVA). Three practice problems are provided at the end of each chapter, along with their solutions in an appendix. An additional practice test allows readers to test their understanding of each chapter by attempting to solve a specific practical advertising statistics problem using Excel; the solution to each of these problems is also given in an appendix. This latest edition features a wealth of new end-of-chapter problems and an update of the chapter content throughout.
Preface.- Acknowledgements.- 1 Sample Size, Mean, Standard Deviation, and Standard Error of the Mean.- 2 Random Number Generator.- 3 Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing.- 4 One-Group t-Test for the Mean.- 5 Two-Group t-Test of the Difference of the Means for Independent Groups.- 6 Correlation and Simple Linear Regression.- 7 Multiple Correlation and Multiple Regression.- 8 One-Way Analysis of Variance (ANOVA).- Appendix A: Answers to End-of-Chapter Practice Problems.- Appendix B: Practice Test.- Appendix C: Answers to Practice Test.- Appendix D: Statistical Formulas.- Appendix E: t-table.- Index.
Автор: Ravid, Ruth Название: Practical statistics for educators ISBN: 1475846827 ISBN-13(EAN): 9781475846829 Издательство: NBN International Рейтинг: Цена: 4106.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book introduces educational students and practitioners to the use of statistics in education.
Описание: Some probability problems are so difficult that they stump the smartest mathematicians. But even the hardest of these problems can often be solved with a computer and a Monte Carlo simulation, in which a random-number generator simulates a physical process, such as a million rolls of a pair of dice. This is what Digital Dice is all about: how to ge
Автор: Everitt Название: Modern Medical Statistics - A Practical Guide ISBN: 0470711167 ISBN-13(EAN): 9780470711163 Издательство: Wiley Рейтинг: Цена: 10922.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Statistical science plays an increasingly important role in medical research. Over the last few decades, many new statistical methods have been developed which have particular relevance for medical researchers and, with the appropriate software now easily available, these techniques can be used almost routinely to great effect.
Автор: Fu Название: A Practical Guide To Age-Period Coh ISBN: 1466592656 ISBN-13(EAN): 9781466592650 Издательство: Taylor&Francis Рейтинг: Цена: 11789.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Age-Period-Cohort analysis has a wide range of applications, from chronic disease incidence and mortality data in public health and epidemiology, to many social events (birth, death, marriage, etc) in social sciences and demography, and most recently investment, healthcare and pension contribution in economics and finance. Although APC analysis has been studied for the past 40 years and a lot of methods have been developed, the identification problem has been a major hurdle in analyzing APC data, where the regression model has multiple estimators, leading to indetermination of parameters and temporal trends. A Practical Guide to Age-Period Cohort Analysis: The Identification Problem and Beyond provides practitioners a guide to using APC models as well as offers graduate students and researchers an overview of the current methods for APC analysis while clarifying the confusion of the identification problem by explaining why some methods address the problem well while others do not.
Features
- Gives a comprehensive and in-depth review of models and methods in APC analysis.
- Provides an in-depth explanation of the identification problem and statistical approaches to addressing the problem and clarifying the confusion.
- Utilizes real data sets to illustrate different data issues that have not been addressed in the literature, including unequal intervals in age and period groups, etc.
Contains step-by-step modeling instruction and R programs to demonstrate how to conduct APC analysis and how to conduct prediction for the future
Reflects the most recent development in APC modeling and analysis including the intrinsic estimator
Wenjiang Fu is a professor of statistics at the University of Houston. Professor Fu's research interests include modeling big data, applied statistics research in health and human genome studies, and analysis of complex economic and social science data.
Описание: This book is intended for periodontal residents and practicing periodontists who wish to incorporate the principles of moderate sedation into daily practice. Comprehensive airway management and rescue skills are then documented in detail so that the patient may be properly managed in the event that the sedation progresses beyond the intended level.
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