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Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 219
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Book Description
Unveil the Secrets of Data Analysis and Inference In the realm of data-driven decision-making, probability and statistics are the bedrock of understanding uncertainty, variability, and drawing meaningful conclusions. "Mastering Probability and Statistics" is your definitive guide to unraveling the intricacies of these essential mathematical tools, empowering you to make informed decisions and draw insightful conclusions from data. About the Book: As data becomes increasingly integral to various fields, a solid foundation in probability and statistics becomes a critical asset. "Mastering Probability and Statistics" offers a comprehensive exploration of these core concepts—an indispensable toolkit for students, analysts, researchers, and enthusiasts alike. This book caters to both newcomers and experienced learners aiming to excel in probability, statistical analysis, and data interpretation. Key Features: Probability Essentials: Begin by understanding the core principles of probability. Learn about random variables, probability distributions, and the mathematics of uncertainty. Descriptive Statistics: Dive into descriptive statistics. Explore techniques for summarizing and visualizing data using measures of central tendency and variability. Probability Distributions: Grasp the art of working with probability distributions. Understand the characteristics of common distributions like the normal, binomial, and exponential distributions. Statistical Inference: Explore the realm of statistical inference. Learn how to make decisions and draw conclusions about populations based on sample data using hypothesis testing and confidence intervals. Regression Analysis: Understand the power of regression analysis. Explore techniques for modeling relationships between variables and making predictions using linear and nonlinear regression. Probability and Sampling: Delve into probability and sampling techniques. Learn how to apply probability concepts to sampling methods and estimate population parameters. Multivariate Analysis: Grasp multivariate analysis techniques. Explore methods for analyzing data with multiple variables, including principal component analysis and factor analysis. Real-World Applications: Gain insights into how probability and statistics are applied across industries. From business to science, discover the diverse applications of these concepts in various fields. Why This Book Matters: In an era of data-driven decision-making, mastering probability and statistics offers a competitive advantage. "Mastering Probability and Statistics" empowers learners, analysts, researchers, and technology enthusiasts to leverage these foundational concepts, enabling them to analyze data, make informed decisions, and draw meaningful insights. Uncover the Power of Data Insight: In the landscape of data-driven decision-making, probability and statistics are the keys to understanding uncertainty and drawing meaningful insights. "Mastering Probability and Statistics" equips you with the knowledge needed to leverage these essential mathematical tools, enabling you to analyze data, make informed decisions, and draw valuable conclusions. Whether you're an experienced analyst or new to the world of data analysis, this book will guide you in building a solid foundation for effective statistical reasoning and data interpretation. Your journey to mastering probability and statistics starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Author: Cybellium Ltd
Publisher: Cybellium Ltd
ISBN:
Category : Computers
Languages : en
Pages : 219
Get Book Here
Book Description
Unveil the Secrets of Data Analysis and Inference In the realm of data-driven decision-making, probability and statistics are the bedrock of understanding uncertainty, variability, and drawing meaningful conclusions. "Mastering Probability and Statistics" is your definitive guide to unraveling the intricacies of these essential mathematical tools, empowering you to make informed decisions and draw insightful conclusions from data. About the Book: As data becomes increasingly integral to various fields, a solid foundation in probability and statistics becomes a critical asset. "Mastering Probability and Statistics" offers a comprehensive exploration of these core concepts—an indispensable toolkit for students, analysts, researchers, and enthusiasts alike. This book caters to both newcomers and experienced learners aiming to excel in probability, statistical analysis, and data interpretation. Key Features: Probability Essentials: Begin by understanding the core principles of probability. Learn about random variables, probability distributions, and the mathematics of uncertainty. Descriptive Statistics: Dive into descriptive statistics. Explore techniques for summarizing and visualizing data using measures of central tendency and variability. Probability Distributions: Grasp the art of working with probability distributions. Understand the characteristics of common distributions like the normal, binomial, and exponential distributions. Statistical Inference: Explore the realm of statistical inference. Learn how to make decisions and draw conclusions about populations based on sample data using hypothesis testing and confidence intervals. Regression Analysis: Understand the power of regression analysis. Explore techniques for modeling relationships between variables and making predictions using linear and nonlinear regression. Probability and Sampling: Delve into probability and sampling techniques. Learn how to apply probability concepts to sampling methods and estimate population parameters. Multivariate Analysis: Grasp multivariate analysis techniques. Explore methods for analyzing data with multiple variables, including principal component analysis and factor analysis. Real-World Applications: Gain insights into how probability and statistics are applied across industries. From business to science, discover the diverse applications of these concepts in various fields. Why This Book Matters: In an era of data-driven decision-making, mastering probability and statistics offers a competitive advantage. "Mastering Probability and Statistics" empowers learners, analysts, researchers, and technology enthusiasts to leverage these foundational concepts, enabling them to analyze data, make informed decisions, and draw meaningful insights. Uncover the Power of Data Insight: In the landscape of data-driven decision-making, probability and statistics are the keys to understanding uncertainty and drawing meaningful insights. "Mastering Probability and Statistics" equips you with the knowledge needed to leverage these essential mathematical tools, enabling you to analyze data, make informed decisions, and draw valuable conclusions. Whether you're an experienced analyst or new to the world of data analysis, this book will guide you in building a solid foundation for effective statistical reasoning and data interpretation. Your journey to mastering probability and statistics starts here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Author: Dimitri Bertsekas
Publisher: Athena Scientific
ISBN: 188652923X
Category : Mathematics
Languages : en
Pages : 544
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Book Description
An intuitive, yet precise introduction to probability theory, stochastic processes, statistical inference, and probabilistic models used in science, engineering, economics, and related fields. This is the currently used textbook for an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students, and for a leading online class on the subject. 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, including transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes, Bayesian inference, and an introduction to classical statistics. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis is explained intuitively in the main text, and then developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems.
Author: Larry Wasserman
Publisher: Springer Science & Business Media
ISBN: 0387217363
Category : Mathematics
Languages : en
Pages : 446
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Book Description
Taken literally, the title "All of Statistics" is an exaggeration. 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 non-parametric curve estimation, bootstrapping, and classification, 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 analysing data.
Author: Michael J. Evans
Publisher: Macmillan
ISBN: 9780716747420
Category : Mathematics
Languages : en
Pages : 704
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Book Description
Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.
Author: José Unpingco
Publisher: Springer
ISBN: 3030185451
Category : Technology & Engineering
Languages : en
Pages : 384
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Book Description
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
Author: Daren Starnes
Publisher: Macmillan Higher Education
ISBN: 131912013X
Category : Mathematics
Languages : en
Pages : 1532
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Book Description
Statistics and Probability with Applications, Third Edition is the only introductory statistics text written by high school teachers for high school teachers and students. Daren Starnes, Josh Tabor, and the extended team of contributors bring their in-depth understanding of statistics and the challenges faced by high school students and teachers to development of the text and its accompanying suite of print and interactive resources for learning and instruction. A complete re-envisioning of the authors’ Statistics Through Applications, this new text covers the core content for the course in a series of brief, manageable lessons, making it easy for students and teachers to stay on pace. Throughout, new pedagogical tools and lively real-life examples help captivate students and prepare them to use statistics in college courses and in any career.
Author: Henk Tijms
Publisher: Cambridge University Press
ISBN: 1139465457
Category : Mathematics
Languages : en
Pages : 407
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Book Description
In this fully revised second edition of Understanding Probability, the reader can learn about the world of probability in an informal way. The author demystifies the law of large numbers, betting systems, random walks, the bootstrap, rare events, the central limit theorem, the Bayesian approach and more. This second edition has wider coverage, more explanations and examples and exercises, and a new chapter introducing Markov chains, making it a great choice for a first probability course. But its easy-going style makes it just as valuable if you want to learn about the subject on your own, and high school algebra is really all the mathematical background you need.
Author: Richard McElreath
Publisher: CRC Press
ISBN: 1315362619
Category : Mathematics
Languages : en
Pages : 488
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Book Description
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.
Author: Roman Vershynin
Publisher: Cambridge University Press
ISBN: 1108415199
Category : Business & Economics
Languages : en
Pages : 299
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Book Description
An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Author: Arak M. Mathai
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 311056260X
Category : Mathematics
Languages : en
Pages : 732
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Book Description
This book offers an introduction to concepts of probability theory, probability distributions relevant in the applied sciences, as well as basics of sampling distributions, estimation and hypothesis testing. As a companion for classes for engineers and scientists, the book also covers applied topics such as model building and experiment design. Contents Random phenomena Probability Random variables Expected values Commonly used discrete distributions Commonly used density functions Joint distributions Some multivariate distributions Collection of random variables Sampling distributions Estimation Interval estimation Tests of statistical hypotheses Model building and regression Design of experiments and analysis of variance Questions and answers
