Using JMP Student Edition is the official user guide for JMP Student Edition, the streamlined edition of JMP for first year statistics students. Clearly written, with easy-to-follow, step-by-step instructions, this book provides engaging illustrations and concept overviews. Chapters cover JMP basics such as importing data, creating formulas, creating graphs with Graph Builder, and performing univariate and bivariate data analysis. More complex analyses are covered, including Partition, Multiple Regression, Time Series, Design of Experiments, Variability Gauge Charts, and Quality Control, and more. JMP Student Edition software is available from major textbook publishers when packaged with their textbooks for course use.

"Learning SAP Business Objects Dashboards" is your definitive guide to mastering the creation of professional, interactive dashboards tailored for both desktop and mobile use. Whether you are starting with basics or looking to enhance your existing BI skill set, this book offers step-by-step guidance to build customizing and visually appealing dashboards using SAP Business Objects. What this Book will help me do Gain proficiency in using SAP BO Dashboard Designer to create compelling BI dashboards. Learn how to integrate data seamlessly into SAP dashboards for dynamic insights. Master the art of building interactive and responsive dashboards suitable for various devices. Explore advanced visualization techniques including maps, selectors, and styling. Understand how to ensure data security and successfully deploy your dashboards. Author(s) Taha Mahmoud is an experienced Business Intelligence consultant specializing in SAP technologies. With years of experience in crafting custom BI solutions, Taha brings a practical and approachable teaching style to the complex realm of dashboard design. His goal in writing this book is to impart practical skills to learners so they can confidently excel in their BI careers. Who is it for? This book is ideal for business analysts, BI developers, and data visualization enthusiasts aiming to learn or improve their SAP Business Objects dashboard skills. It requires basic knowledge of MS Excel and an analytical mindset. If you're tasked with presenting data in clear, impactful visuals or making interactive dashboards for your organization, this book is for you.

Mastering Matplotlib provides readers with the tools to not just create visualizations but to fully harness the capabilities of the Matplotlib library. You will explore advanced features, work on interactive visualizations, and learn to optimize plots for various platforms and datasets. By the end, you will be adept at using Matplotlib in complex projects involving data analysis and visualization. What this Book will help me do Understand the architecture and internals of Matplotlib to better utilize and extend its features. Develop visually dynamic and interactive plots that update in real-time with changes in the user interface. Leverage third-party libraries to visualize complex datasets and relationships efficiently. Create tailored styling for visualizations, meeting publication and presentation standards. Deploy and integrate Matplotlib-based visualizations into cloud environments and big data workflows seamlessly. Author(s) Duncan M. McGreggor is a seasoned software engineer with years of hands-on experience in data visualization and scientific computing. He specializes in utilizing Matplotlib for dynamic charting and advanced plotting use cases. His approach to writing focuses on empowering readers to apply and integrate visualization solutions in real-world scenarios. Who is it for? This book is ideal for scientists, software engineers, programmers, and students who have a foundational understanding of Matplotlib and are looking to take their skills to an advanced level. If you're aiming to leverage Matplotlib to handle intricate datasets or to create sophisticated visual representations, this book is for you. It caters to learners seeking practical guidance for professional or academic projects. Expand your visualization toolkit with this insightful guide.

Dive into the intersection of biology and data science with 'Bioinformatics with Python Cookbook.' This book equips you to leverage Python and its ecosystem of libraries to tackle complex challenges in computational biology, covering topics like genomics, phylogenetics, and big data bioinformatics. What this Book will help me do Understand the Python ecosystem specifically tailored for computational biology applications. Analyze and visualize next-generation sequencing data effectively. Explore and simulate population genetics for robust biological research. Utilize the Protein Data Bank to extract critical insights about proteins. Handle big genomics datasets with Python tools for large-scale bioinformatics studies. Author(s) Tiago Antao is an established bioinformatician with expertise in Python programming. With years of practical experience in computational biology, he has tailored this cookbook with detailed and actionable examples. Tiago's mission is to make bioinformatic techniques using Python accessible to researchers of varying skill levels. Who is it for? This book is ideal for researchers, biologists, and data scientists with intermediate Python skills looking to expand their expertise in bioinformatics. It caters to professionals wanting to utilize computational tools for solving biological problems. If you're involved in work or study related to genomics, phylogenetics, or large-scale biology datasets, this guide offers practical solutions. Make the most out of Python in your research journey.

"ggplot2 Essentials" takes you on a journey to mastering data visualization in R. Through this book, you will explore the full capabilities of the ggplot2 package and how it employs the principles of the grammar of graphics to create meaningful and visually appealing graphs. By reading this book, you will gain practical skills to produce stunning plots for your data analysis projects. What this Book will help me do Understand the core concepts of the grammar of graphics and how ggplot2 implements them. Learn to create a variety of plots using the ggplot2's basic and advanced functionalities. Master techniques for customizing plots, including aesthetics and graphical details. Become proficient in exporting plots in diverse formats and creating publication-ready graphs. Incorporate mapping and overlays into your plots, expanding the frontiers of ggplot2 capabilities. Author(s) Donato Teutonico is an experienced data analyst and programmer with a strong background in R and data visualization. With years of expertise, Donato specializes in creating clear, actionable tutorials for users of statistical software. His approach to teaching emphasizes practical, hands-on examples tailored to empower learners with practical plotting skills. Who is it for? This book is ideal for R programmers who want to harness the power of ggplot2 for data visualization. If you are already familiar with R and want to create more sophisticated and customizable graphics, this resource will enhance your skills. It is suited for intermediate R users aiming to discover ggplot2's features and create polished visual representations of their data. Advanced beginners passionate about data visualization will also appreciate the clear explanations and practical examples.

Delve into the capabilities of NumPy, the cornerstone of mathematical computations in Python. In this guide, you will learn how to utilize NumPy to its fullest by exploring its powerful array and matrix operations, and also integrate it with other libraries like SciPy and matplotlib for advanced analysis and visualization. What this Book will help me do Master the installation and configuration of the NumPy library on different systems. Perform advanced array and matrix operations efficiently using NumPy. Understand and utilize commonly used NumPy modules for computational tasks. Design and generate complex plots using the matplotlib library. Learn best practices for testing and validating numerical computations with NumPy. Author(s) Ivan Idris is an experienced data analyst and Python enthusiast, proficient in utilizing numerical and scientific libraries to address complex problems. With a strong background in mathematics and computer science, Ivan brings a practical approach to his teachings. He emphasizes clarity and hands-on practice, making expert-level concepts accessible and engaging for learners. Who is it for? This book is perfect for scientists, engineers, and data professionals with a solid foundation in Python. It's meant for those seeking to deepen their understanding of numerical methods and scientific computing. If you want to harness the power of NumPy to streamline your computations and develop high-performance solutions, this guide is for you.

" If you have trouble estimating cost or schedule for your projects, you are not alone. The question is this: who wants the estimate and why? The definition of estimate is to guess. But too often, the people who want estimates want commitments. Instead of a commitment, you can apply practical and pragmatic approaches to developing estimates and then meet your commitments. You can provide your managers with the information they want and that you can live with. Learn how to use different words for your estimates and how to report an estimate that includes uncertainty. Learn who should and should not estimate. Learn how to update your estimate when you know more about your project. Regain estimation sanity. Learn practical and pragmatic ways to estimate schedule or cost for your projects."

Supplying a comprehensive overview of healthcare analytics research, Healthcare Data Analytics provides an understanding of the analytical techniques currently available to solve healthcare problems. The book details novel techniques for acquiring, handling, retrieving, and making best use of healthcare data. It analyzes recent developments in healthcare computing and discusses emerging technologies that can help improve the health and well-being of patients. Written by prominent researchers and experts working in the healthcare domain, it sheds light on the computational challenges in the field of medical informatics.

This book provides an overview of recent work on developing a theory of statistical inference based on measuring statistical evidence. It attempts to establish a gold standard for how a statistical analysis should proceed. The book illustrates relative belief theory using many examples and describes the strengths and weaknesses of the theory.

The First Complete Guide to Mobile App Testing and Quality Assurance: Start-to-Finish Testing Solutions for Both Android and iOS Today, mobile apps must meet rigorous standards of reliability, usability, security, and performance. However, many mobile developers have limited testing experience, and mobile platforms raise new challenges even for long-time testers. Now, Hands-On Mobile App Testing provides the solution: an end-to-end blueprint for thoroughly testing any iOS or Android mobile app. Reflecting his extensive real-life experience, Daniel Knott offers practical guidance on everything from mobile test planning to automation. He provides expert insights on mobile-centric issues, such as testing sensor inputs, battery usage, and hybrid apps, as well as advice on coping with device and platform fragmentation, and more. If you want top-quality apps as much as your users do, this guide will help you deliver them. You’ll find it invaluable–;whether you’re part of a large development team or you are the team. Learn how to Establish your optimal mobile test and launch strategy Create tests that reflect your customers, data networks, devices, and business models Choose and implement the best Android and iOS testing tools Automate testing while ensuring comprehensive coverage Master both functional and nonfunctional approaches to testing Address mobile’s rapid release cycles Test on emulators, simulators, and actual devices Test native, hybrid, and Web mobile apps Gain value from crowd and cloud testing (and understand their limitations) Test database access and local storage Drive value from testing throughout your app lifecycle Start testing wearables, connected homes/cars, and Internet of Things devices

Dive into the realm of predictive analytics with this R-focused guide. Whether you're building your first model or refining complex analytics strategies, this book equips you with fundamental techniques and in-depth understanding of predictive modeling using R. What this Book will help me do Master the end-to-end predictive modeling process. Classify and select suitable predictive models for specific use cases. Understand the mechanics and assumptions of various predictive models. Evaluate predictive model performance with appropriate metrics. Enhance your R programming skills for analytical tasks. Author(s) The authors of this book combine strong technical expertise in data science and predictive analytics with extensive hands-on experience in applying them to real-world challenges. They excel at distilling complex topics into approachable, actionable steps for readers at varying levels of familiarity with R and data analysis. Their commitment to empowering learners defines their work. Who is it for? This book is perfect for budding data scientists and quantitative analysts with basic R knowledge who aspire to master predictive analytics. Even experienced professionals will find valuable model-specific insights. If you're familiar with basic statistics and eager to bridge the gap to robust machine learning applications, this book is for you.

This is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. The book links recent developments in spatio-temporal methodology with epidemiological applications. Drawing on real-life problems, it provides the tools required to exploit recent advances in methodology when assessing the health risks associated with environmental hazards. The text includes practical examples together with embedded R code, details of specific R packages, and other software, including WinBUGS/OpenBUGS and INLA.

Here's the net takeaway: Businesses want insights from data they can translate into meaningful actions and real results. Software vendors are beginning to deliver a new generation of advanced analytics packages that address business issues directly. In this O'Reilly report, Mike Barlow reveals how this new user-friendly software is helping businesses go beyond data analysis and straight to decision-making—without requiring data science expertise or truckloads of cash. How has advanced analytics progressed from lab project to commercial product so quickly? Through interviews with data analysts, you'll understand the role that machine learning plays in specialized analytics packages, and how this software alone can make decisions based on what's likely to happen next. When you have these capabilities, you’ve reached "the last mile of analytics."

A unique, in-depth guide to options pricing and valuing their greeks, along with a four dimensional approach towards the impact of changing market circumstances on options How to Calculate Options Prices and Their Greeks is the only book of its kind, showing you how to value options and the greeks according to the Black Scholes model but also how to do this without consulting a model. You'll build a solid understanding of options and hedging strategies as you explore the concepts of probability, volatility, and put call parity, then move into more advanced topics in combination with a four-dimensional approach of the change of the P&L of an option portfolio in relation to strike, underlying, volatility, and time to maturity. This informative guide fully explains the distribution of first and second order Greeks along the whole range wherein an option has optionality, and delves into trading strategies, including spreads, straddles, strangles, butterflies, kurtosis, vega-convexity, and more. Charts and tables illustrate how specific positions in a Greek evolve in relation to its parameters, and digital ancillaries allow you to see 3D representations using your own parameters and volumes. The Black and Scholes model is the most widely used option model, appreciated for its simplicity and ability to generate a fair value for options pricing in all kinds of markets. This book shows you the ins and outs of the model, giving you the practical understanding you need for setting up and managing an option strategy. Understand the Greeks, and how they make or break a strategy See how the Greeks change with time, volatility, and underlying Explore various trading strategies Implement options positions, and more Representations of option payoffs are too often based on a simple two-dimensional approach consisting of P&L versus underlying at expiry. This is misleading, as the Greeks can make a world of difference over the lifetime of a strategy. How to Calculate Options Prices and Their Greeks is a comprehensive, in-depth guide to a thorough and more effective understanding of options, their Greeks, and (hedging) option strategies.

The ultimate beginner's guide to SPSS and statistical analysis SPSS Statistics For Dummies is the fun and friendly guide to mastering SPSS. This book contains everything you need to know to get up and running quickly with this industry-leading software, with clear, helpful guidance on working with both the software and your data. Every chapter of this new edition has been updated with screenshots and steps that align with SPSS 23.0. You'll learn how to set up the software and organize your workflow, then delve deep into analysis to discover the power of SPSS capabilities. You'll discover the mechanics behind the calculations, perform predictive analysis, produce informative graphs, and maximize your data, even if it's been awhile since your last statistics class. SPSS is the leading statistical software for social sciences, marketing, health care, demography, government, education, data mining, and more. This powerful package gives you the tools you need to get more out of your data, and this book is your beginner-friendly guide to getting the most out of the software. Install and configure SPSS and learn the basics of how it works Master the process of getting data into SPSS and manipulating it to produce results See how to display data in dozens of different graphic formats to fit specific needs Make SPSS manufacture the numbers you want and take advantage of the many analysis options Discover ways to customize the SPSS interface and the look of your results, edit graphics and pivot tables, and program SPSS with Command Syntax Statistical analysis is crucial to so many industries, and accuracy and efficiency are crucial. SPSS offers you the capability to deliver, but you still must know how to take utmost advantage of the tools at your fingertips. SPSS Statistics For Dummies shows you how to handle data like a pro, with step-by-step instruction and expert advice.

"Mastering Pandas for Finance" takes a deep dive into applying Python and the pandas library to solve real-world financial data analysis problems. With a focus on financial modeling, backtesting trading strategies, and analyzing large datasets, this book equips you with the skills to leverage pandas effectively. What this Book will help me do Utilize pandas DataFrame for efficient financial data handling and manipulation. Develop robust time-series models and perform statistical analysis on financial data. Backtest algorithmic trading strategies including momentum and mean reversion. Price complex financial options and calculate Value at Risk for portfolio management. Optimize portfolio allocation and model financial performance using industry techniques. Author(s) Michael Heydt is an experienced software engineer and data scientist with a strong background in quantitative finance. He specializes in using Python for data analysis and has spent years teaching and writing about technical subjects. His detailed yet approachable writing style makes complex topics accessible to all. Who is it for? "Mastering Pandas for Finance" is perfect for finance professionals seeking to integrate Python into their workflows, data analysts exploring quantitative finance applications, and programmers aiming to specialize in financial analytics. Some baseline Python and pandas knowledge is recommended, but the book is structured to guide you effectively through advanced concepts too.

Collecting Bayesian material scattered throughout the literature, this volume examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics. Each chapter is self-contained, gives an overview of the area, presents theoretical insights, and emphasizes applications through motivating examples.

R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines. About the Technology Business pros and researchers thrive on data, and R speaks the language of data analysis. R is a powerful programming language for statistical computing. Unlike general-purpose tools, R provides thousands of modules for solving just about any data-crunching or presentation challenge you're likely to face. R runs on all important platforms and is used by thousands of major corporations and institutions worldwide. About the Book R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical, and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining, and dynamic report writing. What's Inside Complete R language tutorial Using R to manage, analyze, and visualize data Techniques for debugging programs and creating packages OOP in R Over 160 graphs About the Reader This book is designed for readers who need to solve practical data analysis problems using the R language and tools. Some background in mathematics and statistics is helpful, but no prior experience with R or computer programming is required. About the Author Dr. Rob Kabacoff is a seasoned researcher who specializes in data analysis. He has taught graduate courses in statistical programming and manages the Quick-R website at statmethods.net. Quotes Essential to anyone doing data analysis with R, whether in industry or academia. - Cristofer Weber, NeoGrid A go-to reference for general R and many statistics questions. - George Gaines, KYOS Systems Inc. Accessible language, realistic examples, and clear code. - Samuel D. McQuillin, University of Houston Offers a gentle learning curve to those starting out with R for the first time. - Indrajit Sen Gupta, Mu Sigma Business Solutions

Although there are countless books on statistics, few are dedicated to the application of statistical methods to software engineering. This book fills that void. Instead of delving into overly complex statistics, it focuses on simpler solutions that are just as effective. The authors not only explain the required statistical methods, but also supply detailed examples, stories, and case studies that facilitate the understanding required to apply those methods in real-world software engineering applications.

Data-driven technologies are now being adopted, developed, funded, and deployed throughout the health care market at an unprecedented scale. But, as this O'Reilly report reveals, health care innovation contains more hurdles and requires more finesse than many tech startups expect. By paying attention to the lessons from the report's findings, innovation teams can better anticipate what they'll face, and plan accordingly. Simply put, teams looking to apply collective intelligence and "big data" platforms to health and health care problems often don't appreciate the messy details of using and making sense of data in the heavily regulated hospital IT environment. Download this report today and learn how it helps prepare startups in six areas: Complexity: An enormous domain with noisy data not designed for machine consumption Computing: Lack of standard, interoperable schema for documenting human health in a digital format Context: Lack of critical contextual metadata for interpreting health data Culture: Startup difficulties in hospital ecosystems: why innovation can be a two-edged sword Contracts: Navigating the IRB, HIPAA, and EULA frameworks Commerce: The problem of how digital health startups get paid This report represents the initial findings of a study funded by a grant from the Robert Wood Johnson Foundation. Subsequent reports will explore the results of three deep-dive projects the team pursued during the study.