Название: Statistical Tableau: How to Use Statistical Models and Decision Science in Tableau
Автор: Еthаn Lаng
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 314
Язык: английский
Формат: epub (true)
Размер: 24.4 MB

In today's data-driven world, understanding statistical models is crucial for effective analysis and decision making. Whether you're a beginner or an experienced user, this book equips you with the foundational knowledge to grasp and implement statistical models within Tableau. Gain the confidence to speak fluently about the models you employ, driving adoption of your insights and analysis across your organization. As AI continues to revolutionize industries, possessing the skills to leverage statistical models is no longer optional—it's a necessity. Stay ahead of the curve and harness the full potential of your data by mastering the ability to interpret and utilize the insights generated by these models. Whether you're a data enthusiast, analyst, or business professional, this book empowers you to navigate the ever-evolving landscape of data analytics with confidence and proficiency. Start your journey toward data mastery today. Statistical analysis and data visualization are often considered two separate things. However, both disciplines rely on one another. To understand your data and make accurate predictions or assumptions through statistical analysis, you must visualize your data. On the other hand, to deliver actionable insights and enable your audience to get the most from data visualizations, we must back up our assumptions with some statistical analysis. This book is to help you bring statistics into your visualizations in Tableau. You will learn how to read and interpret statistical models, implement them in Tableau, and ultimately draw out actionable insights to present to your stakeholders. Most decisions using data come with risks; it is your responsibility to arm the decision maker with as much information as possible and to mitigate those risks. R is a programming language and open source software environment primarily used for statistical computing and graphics. Python is a popular language in various fields, including web development (using frameworks like Django and Flask), Data Science and Machine Learning, and automation.

Название: Statistical Thinking: Analyzing Data in an Uncertain World
Автор: Russеll А. Роldrасk
Издательство: Princeton University Press
Год: 2023
Страниц: 281
Язык: английский
Формат: pdf (true)
Размер: 50.5 MB

An essential introduction to statistics for students of psychology and the social sciences. Statistical thinking is increasingly essential to understanding our complex world and making informed decisions based on uncertain data. This incisive undergraduate textbook introduces students to the main ideas of statistics in a way that focuses on deep comprehension rather than rote application or mathematical immersion. The presentation of statistical concepts is thoroughly modern, sharing cutting-edge ideas from the fields of machine learning and data science that help students effectively use statistical methods to ask questions about data. Statistical Thinking provides the tools to describe complex patterns that emerge from data and to make accurate predictions and decisions based on data. The only way to really learn statistics is to do statistics. While many statistics courses historically have been taught using point-and-click statistical software, it is increasingly common for statistical education to use open source languages in which students can code their own analyses. I think that being able to code one’s analyses is essential in order to gain a deep appreciation for statistical analysis, which is why the students in my course at Stanford are expected to learn to use the R statistical programming language to analyze data, alongside the theoretical knowledge that they learn from this book. There are two openly available companions to this textbook that can help the reader get started learning to program; one focuses on the R programming language, and another focuses on the Python language.

Название: Statistical Analysis with R Essentials For Dummies
Автор: Jоsерh Sсhmullеr
Издательство: For Dummies
Год: 2024
Страниц: 192
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB

The easy way to get started coding and analyzing data in the R programming language. Statistical Analysis with R Essentials For Dummies is your reference to all the core concepts about R―the widely used, open-source programming language and data analysis tool. This no-nonsense book gets right to the point, eliminating review material, wordy explanations, and fluff. Understand all you need to know about the foundations of R, swiftly and clearly. Perfect for a brush-up on the basics or as an everyday desk reference on the job, this is the reliable little book you can always turn to for answers. As the title indicates, this book covers the essentials of statistics and R. Although it’s designed to get you up and running in a hurry, and to quickly answer your questions, it’s not just a cookbook. Before I tell you about one of R’s features, I give you the statistical foundation it’s based on. My goal is that you understand that feature when you use it — and that you use it effectively. In the proper context, R can be a great tool for learning statistics and for refreshing what you already know. I’ve tried to supply that context in this book. Although the development of statistics concepts proceeds in a logical way, I organized this book so you can open it up in any chapter and start reading. The idea is for you to quickly find what you’re looking for and use it immediately — whether it’s a statistical concept or an R feature.

Название: Simulation and Statistics with Excel: An Introduction to Business Students
Автор: Luis Fеrnаndо Ibаrrа
Издательство: CRC Press
Год: 2024
Страниц: 291
Язык: английский
Формат: pdf (true)
Размер: 26.6 MB

The use of simulation techniques has increased in importance in recent history, and simulation activities are an important resource for advanced preparation for the organization and execution of events. When formal mathematics is not enough, simulation may be the only option capable of approximating solutions. Simulation and Statistics with Excel: An Introduction to Business Students offers a non-rigorous and practical tour of the simulation procedure on computers, using a versatile and accessible resource, the Microsoft Excel spreadsheet. This book covers the concepts essential to understanding the basic principles and approaches of statistical simulation, allowing for the study of complex systems. Aimed at students in business and operational research beginning to use simulation as an instrument for understanding existing or proposed processes, this book will lay solid foundations in understanding simulation experimentation.

Название: Multilevel Modeling Using R, 3rd Edition
Автор: W. Ноlmеs Finсh, Jосеlуn Е. Воlin
Издательство: Springer
Год: 2024
Страниц: 339
Язык: английский
Формат: pdf (true)
Размер: 10.9 MB

Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single-level and multilevel data. The third edition of the book includes several new topics that were not present in the second edition. Specifically, a new chapter has been included, focussing on fitting multilevel latent variable modeling in the R environment. With R, it is possible to fit a variety of latent variable models in the multilevel context, including factor analysis, structural models, item response theory, and latent class models. The third edition also includes new sections in Chapter 11 describing two useful alternatives to standard multilevel models, fixed effects models and generalized estimating equations.

Название: Statistics and Data Analysis for Engineers and Scientists
Автор: Таnvir Мustаfу, Мd. Таuhid Ur Rаhmаn
Издательство: Springer
Год: 2024
Страниц: 190
Язык: английский
Формат: pdf
Размер: 10.1 MB

This textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level. It aims to combine the data analysis methods using three different programs—Microsoft Excel, SPSS, and MATLAB. The book combining the different data analysis tools is a unique approach. The book presents a variety of real-life problems in data analysis and Machine Learning, delivering the best solution. Analysis methods presented in this book include but are not limited to, performing various algebraic and trigonometric operations, regression modeling, and correlation, as well as plotting graphs and charts to represent the results. Fundamental concepts of applied statistics are also explained here, with illustrative examples. Thus, this book presents a pioneering solution to help a wide range of students, researchers, and professionals learn data processing, interpret different findings derived from the analyses, and apply them to their research or professional fields. The book also includes worked examples of practical problems. The primary focus behind designing these examples is understanding the concepts of data analysis and how it can solve problems. The chapters include practice exercises to assist users in enhancing their skills to execute statistical analysis calculations using software instead of relying on tables for probabilities and percentiles in the present world.

Название: Statistics Slam Dunk: Statistical analysis with R on real NBA data (Final Release)
Автор: Gаrу Suttоn
Издательство: Manning Publications
Год: 2024
Страниц: 672
Язык: английский
Формат: pdf (true)
Размер: 16.8 MB

Learn statistics by analyzing professional basketball data! In this action-packed book, you’ll build your skills in exploratory data analysis by digging into the fascinating world of NBA games and player stats using the R language. Statistics Slam Dunk upgrades your R data science skills by taking on practical analysis challenges based on NBA game and player data. Is losing games on purpose a rational strategy? Which hustle statistics have an impact on wins and losses? Each chapter in this one-of-a-kind guide uses new data science techniques to reveal interesting insights like these. And just like in the real world, you’ll get no clean pre-packaged datasets in Statistics Slam Dunk. You’ll take on the challenge of wrangling messy data to drill on the skills that will make you the star player on any data team. Amazing insights are hiding in raw data, and statistical analysis with R can help reveal them! R was built for data, and it supports modeling and statistical techniques including regression and classification models, time series forecasts, and clustering algorithms. And when you want to see your results, R’s visualizations are stunning, with best-in-class plots and charts. Statistics Slam Dunk: Statistical analysis with R on real NBA data is an interesting and engaging how-to guide for statistical analysis using R. It’s packed with practical statistical techniques, each demonstrated using real-world data taken from NBA games. In each chapter, you’ll discover a new (and sometimes surprising!) insight into basketball, with careful step-by-step instructions on how to generate those revelations.

Название: MATLAB Statistics and Machine Learning Toolbox User’s Guide (R2023b)
Автор: MathWorks
Издательство: The MathWorks, Inc.
Год: September 2023
Страниц: 1176
Язык: английский
Формат: pdf (true)
Размер: 53.4 MB

Analyze and model data using statistics and Machine Learning. Statistics and Machine Learning Toolbox provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis, fit probability distributions to data, generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML. For multidimensional data analysis and feature extraction, the toolbox provides principal component analysis (PCA), regularization, dimensionality reduction, and feature selection methods that let you identify variables with the best predictive power. The toolbox provides supervised, semi-supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted decision trees, k-means, and other clustering methods. You can apply interpretability techniques such as partial dependence plots and LIME, and automatically generate C/C++ code for embedded deployment. Many toolbox algorithms can be used on data sets that are too big to be stored in memory. MATLAB Coder generates readable and portable C and C++ code from Statistics and Machine Learning Toolbox functions that support code generation. You can integrate the generated code into your projects as source code, static libraries, or dynamic libraries.

Название: Statistics Using Python
Автор: Оswаld Саmреsаtо
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 273
Язык: английский
Формат: pdf (true)
Размер: 18.0 MB

This book is designed to offer a fast-paced yet thorough introduction to essential statistical concepts using Python code samples, and aims to assist data scientists in their daily endeavors. The ability to extract meaningful insights from data requires a deep understanding of statistics. The book ensures that each topic is introduced with clarity, followed by executable Python code samples that can be modified and applied according to individual needs. Topics include working with data and exploratoryanalysis, the basics of probability, descriptive and inferential statistics and their applications, metrics for data analysis, probability distributions, hypothesis testing, and more. Appendices on Python and Pandas have been included. From foundational Python concepts to the intricacies of statistics, this book serves as a comprehensive resource for both beginners and seasoned professionals.

Название: Molecular Networking: Statistical Mechanics in the Age of AI and Machine Learning
Автор: Саrоlinе Dеsgrаngеs, Jеrоmе Dеlhоmmеllе
Издательство: CRC Press
Год: 2024
Страниц: 249
Язык: английский
Формат: pdf (true)
Размер: 26.8 MB

The book builds on the analogy between social groups and assemblies of molecules to introduce the concepts of statistical mechanics, Machine Learning and Data Science. Applying a data analytics approach to molecular systems, we show how individual (molecular) features and interactions between molecules, or "communication" processes, allow for the prediction of properties and collective behavior of molecular systems - just as polling and social networking shed light on the behavior of social groups. Applications to systems at the cutting-edge of research for biological, environmental, and energy applications are also presented. Social networks, Machine Learning, and Artificial Intelligence (AI) have become part of our daily lives. We live in an era where data analysis is present in all aspects of society and a driving force for many decisions that impact our present and future. While statistics have long played a significant role in numbers-driven domains, the development of novel machine learning algorithms, combined with the increase in computing performance and data storage, has led to a paradigm shift in how we approach and address challenges. For instance, in human health, the concept of precision medicine, which considers the individual features of a patient, has emerged as a promising alternative to one-size-fits-all medical treatments. Similarly, the sampling of opinion through polling methods had been for decades a staple of, for example, commercial and political analyses. It is now complemented by the analysis of data from social networks, which provide a window into human interactions and the interrelation between individual and collective responses.