Products related to Statistics:
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Spatial Analysis with R : Statistics, Visualization, and Computational Methods
In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R.The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis.Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data miningThis second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
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ISE Business Statistics and Analytics in Practice
Bowerman 9e covers both standard business statistics and business analytics topics and provides them in a clear presentation that is organized so that business analytics topics may be used or not used.Bowerman provides a continuous case throughout chapters and business analytics topics that allow students to use data for a more applied and practical approach.Featuring Connect, Smartbook, Guided examples, Algorithmic Problems and a Business Statistics, Math and Excel prep component, Bowerman is a perfect fit for the instructor who wants a Business Stats with Business Analytics focus.
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Applied Spatial Statistics and Econometrics : Data Analysis in R
This textbook is a comprehensive introduction to applied spatial data analysis using R.Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn.The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping.It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises.This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science.It will also be suitable for researchers and data scientists working with spatial data.
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Visualize This : The FlowingData Guide to Design, Visualization, and Statistics
One of the most influential data visualization books—updated with new techniques, technologies, and examples Visualize This demonstrates how to explain data visually, so that you can present and communicate information in a way that is appealing and easy to understand.Today, there is a continuous flow of data available to answer almost any question.Thoughtful charts, maps, and analysis can help us make sense of this data.But the data does not speak for itself. As leading data expert Nathan Yau explains in this book, graphics provide little value unless they are built upon a firm understanding of the data behind them.Visualize This teaches you a data-first approach from a practical point of view.You'll start by exploring what your data has to say, and then you'll design visualizations that are both remarkable and meaningful.With this book, you'll discover what tools are available to you without becoming overwhelmed with options.You'll be exposed to a variety of software and code and jump right into real-world datasets so that you can learn visualization by doing.You'll learn to ask and answer questions with data, so that you can make charts that are both beautiful and useful.Visualize This also provides you with opportunities to apply what you learn to your own data.This completely updated, full-color second edition: Presents a unique approach to visualizing and telling stories with data, from data visualization expert Nathan YauOffers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information designDetails tools that can be used to visualize data graphics for reports, presentations, and stories, for the web or for print, with major updates for the latest R packages, Python libraries, JavaScript libraries, illustration software, and point-and-click applicationsContains numerous examples and descriptions of patterns and outliers and explains how to show them Information designers, analysts, journalists, statisticians, data scientists—as well as anyone studying for careers in these fields—will gain a valuable background in the concepts and techniques of data visualization, thanks to this legendary book.
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What is spatial visualization ability?
Spatial visualization ability refers to the capacity to mentally manipulate and comprehend spatial relationships between objects. Individuals with strong spatial visualization skills can easily visualize and understand how objects relate to each other in space, such as rotating or manipulating shapes in their mind. This ability is crucial in various fields such as engineering, architecture, and mathematics, as it allows individuals to solve complex problems and understand spatial concepts more effectively. Improving spatial visualization ability can enhance problem-solving skills and overall cognitive performance.
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Is spatial visualization important for engineers?
Yes, spatial visualization is important for engineers as it allows them to mentally manipulate and understand complex 3D objects and structures. Engineers often need to design and analyze various components and systems, and spatial visualization skills help them to conceptualize and communicate their ideas effectively. Whether it's designing a new product, creating blueprints for a building, or solving complex problems, spatial visualization is a crucial skill that allows engineers to think critically and innovate in their field.
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Is this question about inferential statistics or descriptive statistics?
This question is about distinguishing between inferential statistics and descriptive statistics. Inferential statistics involves making predictions or inferences about a population based on a sample of data, while descriptive statistics involves summarizing and describing the characteristics of a dataset.
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Can one improve their spatial visualization skills?
Yes, it is possible to improve spatial visualization skills through practice and training. Engaging in activities such as puzzles, building models, and playing spatial reasoning games can help develop these skills. Additionally, practicing mental rotation exercises and regularly challenging oneself with spatial tasks can also contribute to improvement. With consistent effort and dedication, individuals can enhance their spatial visualization abilities over time.
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Primer for Data Analytics and Graduate Study in Statistics
This book is specially designed to refresh and elevate the level of understanding of the foundational background in probability and distributional theory required to be successful in a graduate-level statistics program.Advanced undergraduate students and introductory graduate students from a variety of quantitative backgrounds will benefit from the transitional bridge that this volume offers, from a more generalized study of undergraduate mathematics and statistics to the career-focused, applied education at the graduate level.In particular, it focuses on growing fields that will be of potential interest to future M.S. and Ph.D. students, as well as advanced undergraduates heading directly into the workplace: data analytics, statistics and biostatistics, and related areas.
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Statistics and Data Visualization in Climate Science with R and Python
A comprehensive overview of essential statistical concepts, useful statistical methods, data visualization, and modern computing tools for the climate sciences and many others such as geography and environmental engineering.It is an invaluable reference for students and researchers in climatology and its connected fields who wish to learn data science, statistics, R and Python programming.The examples and exercises in the book empower readers to work on real climate data from station observations, remote sensing and simulated results.For example, students can use R or Python code to read and plot the global warming data and the global precipitation data in netCDF, csv, txt, or JSON; and compute and interpret empirical orthogonal functions.The book's computer code and real-world data allow readers to fully utilize the modern computing technology and updated datasets.Online supplementary resources include R code and Python code, data files, figure files, tutorials, slides and sample syllabi.
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Foundations of Programming, Statistics, and Machine Learning for Business Analytics
Business Analysts and Data Scientists are in huge demand, as global companies seek to digitally transform themselves and leverage their data resources to realize competitive advantage. This book covers all the fundamentals, from statistics to programming to business applications, to equip you with the solid foundational knowledge needed to progress in business analytics. Assuming no prior knowledge of programming or statistics, this book takes a simple step-by-step approach which makes potentially intimidating topics easy to understand, by keeping Maths to a minimum and including examples of business analytics in practice. Key features: · Introduces programming fundamentals using R and Python · Covers data structures, data management and manipulation and data visualization · Includes interactive coding notebooks so that you can build up your programming skills progressively Suitable as an essential text for undergraduate and postgraduate students studying Business Analytics or as pre-reading for students studying Data Science. Ram Gopal is Pro-Dean and Professor of Information Systems at the University of Warwick. Daniel Philps is an Artificial Intelligence Researcher and Head of Rothko Investment Strategies. Tillman Weyde is Senior Lecturer at City, University of London.
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Business Statistics Using Excel : A Complete Course in Data Analytics
This book gives readers a hands-on understanding of Excel-assisted statistical techniques to take effective business decisions.It showcases applications of the tools and techniques of statistics for analysing business data from the domain of business statistics. The volume provides an exhaustive introduction to the application of statistics in solving business problems and implementing data analytics for effective decision making in all kinds of business situations around the world.With an emphasis on simplicity in presentation of concepts of statistical methods and associated Excel functions, the volume explores the implementation of Excel functions through well-defined sequences of steps.It covers an array of key topics which includeDiscussions on real-world problems, decision support systems, scope of business statistics, types, and steps of research;Introduction to Excel and its mathematical and preliminary statistical functions; usage of different types of average functions; mean, median, and mode functions; measures of variation; measures of skewness of Excel;In-depth discussions on probability distributions, sampling distributions, testing of hypothesis, chi-square test, non-parametric tests of Excel;Extensive coverage on correlation and covariance, forecasting, analysis of variance, charts in Excel; andAnalysis of the concept of linear programming, problem formulations, and techniques of linear programming, followed by the application in Excel. Comprehensive in scope and simple in approach, this book will be key for students and researchers of business studies, business administration, economics, finance, commerce, data analytics/science, and computer science.This will also serve as useful guidebook for business executives and working professionals across the globe.
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What are problems with spatial visualization skills?
Some problems with spatial visualization skills include difficulty in understanding and interpreting maps, graphs, and diagrams. Individuals with poor spatial visualization skills may struggle with tasks such as navigating through unfamiliar environments, understanding 3D objects, and mentally rotating objects. This can impact their performance in subjects such as math, science, and engineering, as well as in everyday activities such as driving and assembling furniture. Additionally, poor spatial visualization skills can lead to frustration and decreased confidence in one's abilities.
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What is the function of spatial visualization ability?
Spatial visualization ability allows individuals to mentally manipulate and understand spatial relationships between objects. This ability is crucial in fields such as engineering, architecture, and design, where individuals need to visualize and manipulate complex 3D objects and structures. It also plays a key role in tasks such as navigation, map reading, and understanding geometric concepts. Overall, spatial visualization ability is important for problem-solving, creativity, and understanding the physical world.
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How difficult is statistics?
Statistics can be challenging for some people due to its complex concepts and mathematical calculations. Understanding statistical theories and applying them to real-world data sets can be difficult for those who are not familiar with the subject. However, with practice and dedication, many people can grasp the fundamentals of statistics and become proficient in analyzing data. It is important to seek help from teachers, tutors, or online resources if you are struggling with statistics to improve your understanding and skills.
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What are NBA statistics?
NBA statistics are numerical data that track the performance of players and teams in the National Basketball Association. These statistics include a wide range of metrics such as points scored, rebounds, assists, steals, blocks, shooting percentages, turnovers, and many others. They are used to evaluate and compare the performance of players and teams, as well as to analyze trends and make strategic decisions. NBA statistics are an essential tool for coaches, analysts, and fans to understand the game and its players better.
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