Mathematical & Statistical Software books

Complete guide to Excel Key Features Basic components of excel, the available menu options and tools, some customizations, working with multiple workbooks and worksheets. An initial introduction to what are formulas, functions, and their components. Usage of multiple examples to explain the application in real world scenario. Explaining of different chart types available in Excel. Different ways and methods to consolidate data, complex analysis, and prediction or forecast based on trends. Description The first version of MS Excel was released in 1985 and was widely adopted across the globe and became the most prolific and important programs for over 750 million users. Many people assume that Excel is only used by accountants, analysts, scientists and other similar professions that points to the fact that there is a real problem with the understanding of what in this book. Through this book, we will explain why Excel is such an amazing program and why it can do so much more than you think. What are the main uses of Excel and how can this help my business? Excel enables any format of information to be organized in a spreadsheet and its innate flexibility as a program, you can define and structure the layout according to how you want to manage it. It allows for over 1 million rows by over 16,000 columns of data to be managed, which shows how Excel is advancing as a business tool. This allows Excel to be used for a large variety of business purposes. What You Will Learn New to Excel 2016 or upgrading from a previous version? Use this guide to learn the basics and advance features of Excel. This user guide will introduce you to some of Excel’s 2016 more complex functionality, such as: Use Conditional Logic in a formula IF statements Use the SumIF and CountIF functions Produce a chart Sort and filter Apply subtotals Data Validation What-if-Analysis Tools Introduction to Pivot Tables Introduction to Macros Advanced Image Processing Operations Who This Book is For Anyone who wants to learn Excel, do the mundane task easily and quickly, do complex calculations, analysis huge data from internal or external sources, take decisions based on predictions, do forecasting, create plans and charts, monitor dashboards for progress and status, and so on. Table of Contents 1. Overview of Excel 2016 2. Cell References and Range 3. Working with Formulas and Functions 4. Data Validation 5. Protection 6. Sorting a Database 7. Filtering a Database 8. Subtotals 9. Pivot Tables 10. Conditional formatting 11. What-if-Analysis Tools 12. Working with Multiple Worksheets, Workbooks and Applications 13. Working with Charts 14. Macros About the Author Ritu Arora, is a certified MS Excel 2016 corporate trainer having more than 14 years of experience. She has conducted more than 1,000 corporate trainings on Advance Excel and trained more than 10000 people. Linkedin profile: linkedin.com/in/ritu-arora-29019a13

Discover the story of your data using the essential elements of associations and correlations Key Features Get a comprehensive introduction to associations and correlations Explore multivariate analysis, understand its limitations, and discover the assumptions on which it's based Gain insights into the various ways of preparing your data for analysis and visualization Book Description Associations and correlations are ways of describing how a pair of variables change together as a result of their connection. By knowing the various available techniques, you can easily and accurately discover and visualize the relationships in your data. This book begins by showing you how to classify your data into the four distinct types that you are likely to have in your dataset. Then, with easy-to-understand examples, you'll learn when to use the various univariate and multivariate statistical tests. You'll also discover what to do when your univariate and multivariate results do not match. As the book progresses, it describes why univariate and multivariate techniques should be used as a tag team, and also introduces you to the techniques of visualizing the story of your data. By the end of the book, you'll know exactly how to select the most appropriate univariate and multivariate tests, and be able to use a single strategic framework to discover the true story of your data. What you will learn Identify a dataset that's fit for analysis using its basic features Understand the importance of associations and correlations Use multivariate and univariate statistical tests to confirm relationships Classify data as qualitative or quantitative and then into the four subtypes Build a visual representation of all the relationships in the dataset Automate associations and correlations with CorrelViz Who this book is for This is a book for beginners – if you're a novice data analyst or data scientist, then this is a great place to start. Experienced data analysts might also find value in this title, as it will recap the basics and strengthen your understanding of key concepts. This book focuses on introducing the essential elements of association and correlation analysis.

Solve real-world statistical problems using the most popular R packages and techniques Key Features Learn how to apply statistical methods to your everyday research with handy recipes Foster your analytical skills and interpret research across industries and business verticals Perform t-tests, chi-squared tests, and regression analysis using modern statistical techniques Book Description R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools. You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making. By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry. What you will learn Become well versed with recipes that will help you interpret plots with R Formulate advanced statistical models in R to understand its concepts Perform Bayesian regression to predict models and input missing data Use time series analysis for modelling and forecasting temporal data Implement a range of regression techniques for efficient data modelling Get to grips with robust statistics and hidden Markov models Explore ANOVA (Analysis of Variance) and perform hypothesis testing Who this book is for If you are a quantitative researcher, statistician, data analyst, or data scientist looking to tackle various challenges in statistics, this book is what you need! Proficiency in R programming and basic knowledge of linear algebra is necessary to follow along the recipes covered in this book.

Leverage AWS AI/ML managed services to generate value from your time series dataKey FeaturesSolve modern time series analysis problems such as forecasting and anomaly detectionGain a solid understanding of AWS AI/ML managed services and apply them to your business problemsExplore different algorithms to build applications that leverage time series dataBook DescriptionBeing a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes.The book begins with Amazon Forecast, where you'll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You'll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you'll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data.By the end of this AWS book, you'll have understood how to use the three AWS AI services effectively to perform time series analysis.What you will learnUnderstand how time series data differs from other types of dataExplore the key challenges that can be solved using time series dataForecast future values of business metrics using Amazon ForecastDetect anomalies and deliver forewarnings using Lookout for EquipmentDetect anomalies in business metrics using Amazon Lookout for MetricsVisualize your predictions to reduce the time to extract insightsWho this book is forIf you're a data analyst, business analyst, or data scientist looking to analyze time series data effectively for solving business problems, this is the book for you. Basic statistics knowledge is assumed, but no machine learning knowledge is necessary. Prior experience with time series data and how it relates to various business problems will help you get the most out of this book. This guide will also help machine learning practitioners find new ways to leverage their skills to build effective time series-based applications.