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International Marketing Data & Statistics
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Statistics : Learning from Data
Learn how to think like a statistician with Peck/Case's STATISTICS: LEARNING FROM DATA, 3rd Edition.This updated edition addresses common obstacles to learning based on the latest research for mastering statistics and probability.The authors use proven methods to carefully explain areas where you are most likely to struggle -- probability, hypothesis testing and selecting an appropriate method of analysis.You strengthen your conceptual understanding, procedural fluency and ability to put knowledge into practice with this edition's learning objectives, real-data examples, updated exercises and technology notes.WebAssign digital resources and Cengage's Statistical Analysis and Learning Tool (SALT) are also available to guide you in thinking statistically.SALT is an easy-to-use data analysis tool that allows you to manipulate data sets in order to visualize statistics and gain a deeper conceptual understanding about the meaning behind the data.
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Statistics for Data Scientists : An Introduction to Probability, Statistics, and Data Analysis
This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students.It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles.Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints.The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.
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Statistics for Spatio-Temporal Data
Winner of the 2013 DeGroot Prize. A state-of-the-art presentation of spatio-temporal processes, bridging classic ideas with modern hierarchical statistical modeling concepts and the latest computational methods Noel Cressie and Christopher K.Wikle, are also winners of the 2011 PROSE Award in the Mathematics category, for the book “Statistics for Spatio-Temporal Data” (2011), published by John Wiley and Sons. (The PROSE awards, for Professional and Scholarly Excellence, are given by the Association of American Publishers, the national trade association of the US book publishing industry.) Statistics for Spatio-Temporal Data has now been reprinted with small corrections to the text and the bibliography.The overall content and pagination of the new printing remains the same; the difference comes in the form of corrections to typographical errors, editing of incomplete and missing references, and some updated spatio-temporal interpretations. From understanding environmental processes and climate trends to developing new technologies for mapping public-health data and the spread of invasive-species, there is a high demand for statistical analyses of data that take spatial, temporal, and spatio-temporal information into account.Statistics for Spatio-Temporal Data presents a systematic approach to key quantitative techniques that incorporate the latest advances in statistical computing as well as hierarchical, particularly Bayesian, statistical modeling, with an emphasis on dynamical spatio-temporal models. Cressie and Wikle supply a unique presentation that incorporates ideas from the areas of time series and spatial statistics as well as stochastic processes.Beginning with separate treatments of temporal data and spatial data, the book combines these concepts to discuss spatio-temporal statistical methods for understanding complex processes. Topics of coverage include: Exploratory methods for spatio-temporal data, including visualization, spectral analysis, empirical orthogonal function analysis, and LISAsSpatio-temporal covariance functions, spatio-temporal kriging, and time series of spatial processesDevelopment of hierarchical dynamical spatio-temporal models (DSTMs), with discussion of linear and nonlinear DSTMs and computational algorithms for their implementationQuantifying and exploring spatio-temporal variability in scientific applications, including case studies based on real-world environmental data Throughout the book, interesting applications demonstrate the relevance of the presented concepts.Vivid, full-color graphics emphasize the visual nature of the topic, and a related FTP site contains supplementary material.Statistics for Spatio-Temporal Data is an excellent book for a graduate-level course on spatio-temporal statistics.It is also a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.
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How does data manipulation work in statistics?
Data manipulation in statistics involves the process of organizing, cleaning, and transforming raw data to make it suitable for analysis. This can include tasks such as removing outliers, filling in missing values, and reformatting data into a more usable structure. Data manipulation is essential for ensuring the accuracy and reliability of statistical analyses, as well as for making the data easier to interpret and visualize. It often involves using software tools like spreadsheets or statistical programming languages to perform these tasks efficiently. Overall, data manipulation is a crucial step in the statistical process that helps to ensure that the data is in a suitable form for meaningful analysis and interpretation.
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When are data in statistics paired and unpaired?
Data in statistics are considered paired when there are two sets of data that are directly related to each other, such as before and after measurements or data from matched pairs. Unpaired data, on the other hand, are two sets of data that are not directly related to each other and are independent of each other. Paired data is often used to compare the effects of a treatment or intervention, while unpaired data is used to compare two separate groups or populations.
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What are discrete and continuous data in statistics?
Discrete data in statistics refers to data that can only take specific, separate values. These values are often whole numbers and cannot be broken down into smaller units. For example, the number of students in a class or the number of cars in a parking lot are discrete data. On the other hand, continuous data in statistics refers to data that can take any value within a certain range. This type of data can be measured and broken down into smaller and smaller units. For example, the height of a person or the weight of an object are continuous data.
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Which do you prefer, Portal 1 or Portal 2?
As an AI language model, I don't have personal preferences, but I can provide information. Both Portal 1 and Portal 2 are highly acclaimed puzzle-platform games with unique gameplay mechanics and engaging storytelling. Portal 2 expands on the original game with new characters, mechanics, and a co-op mode, making it a favorite for many players. However, some fans appreciate the simplicity and originality of the first game. Ultimately, the preference between the two games comes down to individual tastes and what aspects of the games resonate most with each player.
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Statistics and Analysis of Scientific Data
The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields.It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains.Features new to this edition include: * a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets. * a new chapter on the various measures of the mean including logarithmic averages. * new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors. * a new case study and additional worked examples. * mathematical derivations and theoretical background material have been appropriately marked, to improve the readability of the text. * end-of-chapter summary boxes, for easy reference. As in the first edition, the main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the material.The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher.Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required.The book includes many numerical tables of data, as well as exercises and examples to aid the readers' understanding of the topic.
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Statistics and Analysis of Scientific Data
This book is the third edition of a successful textbook for upper-undergraduate and early graduate students, which offers a solid foundation in probability theory and statistics and their application to physical sciences, engineering, biomedical sciences and related disciplines.It provides broad coverage ranging from conventional textbook content of probability theory, random variables, and their statistics, regression, and parameter estimation, to modern methods including Monte-Carlo Markov chains, resampling methods and low-count statistics. In addition to minor corrections and adjusting structure of the content, particular features in this new edition include:Python codes and machine-readable data for all examples, classic experiments, and exercises, which are now more accessible to students and instructorsNew chapters on low-count statistics including the Poisson-based Cash statistic for regression in the low-count regime,and on contingency tables and diagnostic testing. An additional example of classic experiments based on testing data for SARS-COV-2 to demonstrate practical applications of the described statistical methods. This edition inherits the main pedagogical method of earlier versions—a theory-then-application approach—where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the materials.Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required.The book includes many numerical tables of data as well as exercises and examples to aid the readers' understanding of the topic.
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Statistics and Data Visualisation with Python
This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general.On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work.On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python.Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning.This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.
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Statistics for Big Data For Dummies
The fast and easy way to make sense of statistics for big data Does the subject of data analysis make you dizzy?You've come to the right place! Statistics For Big Data For Dummies breaks this often-overwhelming subject down into easily digestible parts, offering new and aspiring data analysts the foundation they need to be successful in the field.Inside, you'll find an easy-to-follow introduction to exploratory data analysis, the lowdown on collecting, cleaning, and organizing data, everything you need to know about interpreting data using common software and programming languages, plain-English explanations of how to make sense of data in the real world, and much more. Data has never been easier to come by, and the tools students and professionals need to enter the world of big data are based on applied statistics.While the word "statistics" alone can evoke feelings of anxiety in even the most confident student or professional, it doesn't have to.Written in the familiar and friendly tone that has defined the For Dummies brand for more than twenty years, Statistics For Big Data For Dummies takes the intimidation out of the subject, offering clear explanations and tons of step-by-step instruction to help you make sense of data mining—without losing your cool. Helps you to identify valid, useful, and understandable patterns in dataProvides guidance on extracting previously unknown information from large databasesShows you how to discover patterns available in big dataGives you access to the latest tools and techniques for working in big data If you're a student enrolled in a related Applied Statistics course or a professional looking to expand your skillset, Statistics For Big Data For Dummies gives you access to everything you need to succeed.
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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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Where is Portal RTX?
Portal RTX is a real-time ray tracing technology developed by NVIDIA. It is a feature that is available on NVIDIA's RTX series of graphics cards, allowing for more realistic lighting, reflections, and shadows in video games and other applications. This technology is not a physical location, but rather a software feature that can be utilized on compatible hardware.
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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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