A classic that just keeps getting better, The Little SAS Book is essential for anyone learning SAS programming. Lora Delwiche and Susan Slaughter offer a user-friendly approach so that readers can quickly and easily learn the most commonly used features of the SAS language. Each topic is presented in a self-contained, two-page layout complete with examples and graphics.

Nearly every section has been revised to ensure that the sixth edition is fully up-to-date. This edition is also interface-independent, written for all SAS programmers whether they use SAS Studio, SAS Enterprise Guide, or the SAS windowing environment. New sections have been added covering PROC SQL, iterative DO loops, DO WHILE and DO UNTIL statements, %DO statements, using variable names with special characters, the ODS EXCEL destination, and the XLSX LIBNAME engine.

This title belongs on every SAS programmer's bookshelf. It's a resource not just to get you started, but one you will return to as you continue to improve your programming skills.

Learn more about the updates to The Little SAS Book, Sixth Edition here.

Reviews for The Little SAS Book, Sixth Edition can be read here.

Become data-savvy with the widely used data and AI software

Data and analytics are essential for any business, giving insight into what's working, what can be improved, and what else needs to be done. SAS software helps you make sure you're doing data right, with a host of data management, reporting, and analysis tools. SAS For Dummies teaches you the essentials, helping you navigate this statistical software and turn information into value. In this book, learn how to gather data, create reports, and analyze results. You'll also discover how SAS machine learning and AI can help deliver decisions based on data. Even if you're brand new to data and analytics, this easy-to-follow guide will turn you into an SAS power user.

• Become familiar with the most popular SAS applications, including SAS 9 and SAS Viya
• Connect to data, organize your information, and adopt sound data security practices
• Get a primer on working with data sets, variables, and statistical analysis
• Explore and analyze data through SAS programming and rich application interfaces
• Create and share graphs interactive visualizations to deliver insights

This is the perfect Dummies guide for new SAS users looking to improve their skills--in any industry and for any organization size.

Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded third edition of Fundamentals of Predictive Analytics with JMP bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis.

Using JMP 17, this book discusses the following new and enhanced features in an example-driven format:

• an add-in for Microsoft Excel
• Graph Builder
• dirty data
• visualization
• regression
• ANOVA
• logistic regression
• principal component analysis
• LASSO
• elastic net
• cluster analysis
• decision trees
• k-nearest neighbors
• neural networks
• bootstrap forests
• boosted trees
• text mining
• association rules
• model comparison
• time series forecasting

With a new, expansive chapter on time series forecasting and more exercises to test your skills, this third edition is invaluable to those who need to expand their knowledge of statistics and apply real-world, problem-solving analysis.

Learn to program SAS by example

Learning SAS by Example, A Programmer's Guide, Second Edition, teaches SAS programming from very basic concepts to more advanced topics. Because most programmers prefer examples rather than reference-type syntax, this book uses short examples to explain each topic. The second edition has brought this classic book on SAS programming up to the latest SAS version, with new chapters that cover topics such as PROC SGPLOT and Perl regular expressions. This book belongs on the shelf (or e-book reader) of anyone who programs in SAS, from those with little programming experience who want to learn SAS to intermediate and even advanced SAS programmers who want to learn new techniques or identify new ways to accomplish existing tasks.

In an instructive and conversational tone, author Ron Cody clearly explains each programming technique and then illustrates it with one or more real-life examples, followed by a detailed description of how the program works. The text is divided into four major sections: Getting Started, DATA Step Processing, Presenting and Summarizing Your Data, and Advanced Topics. Subjects addressed include

• Reading data from external sources
• Learning details of DATA step programming
• Subsetting and combining SAS data sets
• Understanding SAS functions and working with arrays
• Creating reports with PROC REPORT and PROC TABULATE
• Getting started with the SAS macro language
• Leveraging PROC SQL
• Generating high-quality graphics
• Using advanced features of user-defined formats and informats
• Restructuring SAS data sets
• Working with multiple observations per subject
• Getting started with Perl regular expressions

You can test your knowledge and hone your skills by solving the problems at the end of each chapter.

Simplify stats and learn how to graph, analyze, and interpret data the easy way

Statistical Analysis with R For Dummies makes stats approachable by combining clear explanations with practical applications. You'll learn how to download and use R and RStudio--two free, open-source tools--to learn statistics concepts, create graphs, test hypotheses, and draw meaningful conclusions. Get started by learning the basics of statistics and R, calculate descriptive statistics, and use inferential statistics to test hypotheses. Then, visualize it all with graphs and charts. This Dummies guide is your well-marked path to sailing through statistics.

• Get clear explanations of the basics of statistics and data analysis
• Learn how to analyze and visualize data with R, step by step
• Create charts, graphs, and summaries to interpret results
• Explore hypothesis testing, and prediction techniques

This is the perfect introduction to R for students, professionals, and the stat-curious.

Learn how to use R for everything from workload automation and creating online reports, to interpreting data, map making, and more.

Written by the founder of a very popular online training platform for the R programming language!

The R programming language is a remarkably powerful tool for data analysis and visualization, but its steep learning curve can be intimidating for some. If you just want to automate repetitive tasks or visualize your data, without the need for complex math, R for the Rest of Us is for you.

Inside you'll find a crash course in R, a quick tour of the RStudio programming environment, and a collection of real-world applications that you can put to use right away. You'll learn how to create informative visualizations, streamline report generation, and develop interactive websites--whether you're a seasoned R user or have never written a line of R code.

You'll also learn how to:
- Manipulate, clean, and parse your data with tidyverse packages like dplyr and tidyr to make data science operations more user-friendly
- Create stunning and customized plots, graphs, and charts with ggplot2 to effectively communicate your data insights
- Import geospatial data and write code to produce visually appealing maps automatically
- Generate dynamic reports, presentations, and interactive websites with R Markdown and Quarto that seamlessly integrate code, text, and graphics
- Develop custom functions and packages tailored to your specific needs, allowing you to extend R's functionality and automate complex tasks

Unlock a treasure trove of techniques to transform the way you work. With R for the Rest of Us, you'll discover the power of R to get stuff done. No advanced statistics degree required.

Transform your organization's data into actionable insights with Tableau

Tableau is designed specifically to provide fast and easy visual analytics. The intuitive drag-and-drop interface helps you create interactive reports, dashboards, and visualizations, all without any special or advanced training. This all new edition of Tableau Your Data is your Tableau companion, helping you get the most out of this invaluable business toolset.

Tableau Your Data shows you how to build dynamic, best of breed visualizations using the Tableau Software toolset. This comprehensive guide covers the core feature set for data analytics, and provides clear step-by-step guidance toward best practices and advanced techniques that go way beyond the user manual. You'll learn how Tableau is different from traditional business information analysis tools, and how to navigate your way around the Tableau 9.0 desktop before delving into functions and calculations, as well as sharing with the Tableau Server.
Analyze data more effectively with Tableau Desktop

• Customize Tableau's settings for your organization's needs with detailed real-world examples on data security, scaling, syntax, and more
• Deploy visualizations to consumers throughout the enterprise - from sales to marketing, operations to finance, and beyond
• Understand Tableau functions and calculations and leverage Tableau across every link in the value chain
• Learn from actual working models of the book's visualizations and other web-based resources via a companion website

Tableau helps you unlock the stories within the numbers, and Tableau Your Data puts the software's full functionality right at your fingertips.

In SAS Statistics by Example, Ron Cody offers up a cookbook approach for doing statistics with SAS. Structured specifically around the most commonly used statistical tasks or techniques--for example, comparing two means, ANOVA, and regression--this book provides an easy-to-follow, how-to approach to statistical analysis not found in other books. For each statistical task, Cody includes heavily annotated examples using ODS Statistical Graphics procedures such as SGPLOT, SGSCATTER, and SGPANEL that show how SAS can produce the required statistics. Also, you will learn how to test the assumptions for all relevant statistical tests. Major topics featured include descriptive statistics, one- and two-sample tests, ANOVA, correlation, linear and multiple regression, analysis of categorical data, logistic regression, nonparametric techniques, and power and sample size. This is not a book that teaches statistics. Rather, SAS Statistics by Example is perfect for intermediate to advanced statistical programmers who know their statistics and want to use SAS to do their analyses. This book is part of the SAS Press program.

Co-authored by four leading scientists from academia and industry, Numerical Recipes Third Edition starts with basic mathematics and computer science and proceeds to complete, working routines. Widely recognized as the most comprehensive, accessible and practical basis for scientific computing, this new edition incorporates more than 400 Numerical Recipes routines, many of them new or upgraded. The executable C++ code, now printed in color for easy reading, adopts an object-oriented style particularly suited to scientific applications. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Please visit www.nr.com or www.cambridge.org/us/numericalrecipes for more details. More information concerning licenses is available at: www.nr.com/licenses New key features:

• 2 new chapters, 25 new sections, 25% longer than Second Edition
• Thorough upgrades throughout the text
• Over 100 completely new routines and upgrades of many more.
• New Classification and Inference chapter, including Gaussian mixture models, HMMs, hierarchical clustering, Support Vector Machines
• New Computational Geometry chapter covers KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres
• New sections include interior point methods for linear programming, Monte Carlo Markov Chains, spectral and pseudospectral methods for PDEs, and many new statistical distributions
• An expanded treatment of ODEs with completely new routines

Plus comprehensive coverage of

• linear algebra, interpolation, special functions, random numbers, nonlinear sets of equations, optimization, eigensystems, Fourier methods and wavelets, statistical tests, ODEs and PDEs, integral equations, and inverse theory

Discover the power of mixed models with JMP and JMP Pro.

Mixed models are now the mainstream method of choice for analyzing experimental data. Why? They are arguably the most straightforward and powerful way to handle correlated observations in designed experiments. Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your experiments and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions.

JMP for Mixed Models brings together two of the strongest traditions in SAS software: mixed models and JMP. JMP's groundbreaking philosophy of tight integration of statistics with dynamic graphics is an ideal milieu within which to learn and apply mixed models, also known as hierarchical linear or multilevel models. If you are a scientist or engineer, the methods described herein can revolutionize how you analyze experimental data without the need to write code.

Inside you'll find a rich collection of examples and a step-by-step approach to mixed model mastery. Topics include:

• Learning how to appropriately recognize, set up, and interpret fixed and random effects
• Extending analysis of variance (ANOVA) and linear regression to numerous mixed model designs
• Understanding how degrees of freedom work using Skeleton ANOVA
• Analyzing randomized block, split-plot, longitudinal, and repeated measures designs
• Introducing more advanced methods such as spatial covariance and generalized linear mixed models
• Simulating mixed models to assess power and other important sampling characteristics
• Providing a solid framework for understanding statistical modeling in general
• Improving perspective on modern dilemmas around Bayesian methods, p-values, and causal inference

This book is written for beginners and students who wish to learn MATLAB. One of the objectives of writing this book is to introduce MATLAB to students in high schools. The material presented is very easy and simple to understand - written in a gentle manner. The topics covered in the book include arithmetic operations, variables, mathematical functions, complex numbers, vectors, matrices, programming, graphs, solving equations, and an introduction to calculus. In addition, the MATLAB Symbolic Math Toolbox is emphasized in this book. There are also over 230 exercises at the ends of chapters for students to practice. Detailed solutions to all the exercises are provided in the second half of the book. The author has been using MATLAB for the past fifteen years and is the author of the best selling book MATLAB Guide to Finite Elements. For the paperback edition, visit Amazon.com. This description applies to the Revised Edition.

The SAS^(R) Certified Specialist Prep Guide: Base Programming Using SAS^(R) 9.4 prepares you to take the new SAS 9.4 Base Programming -- Performance-Based Exam. This is the official guide by the SAS Global Certification Program.

This prep guide is for both new and experienced SAS users, and it covers all the objectives that are tested on the exam.

New in this edition is a workbook whose sample scenarios require you to write code to solve problems and answer questions. Answers for the chapter quizzes and solutions for the sample scenarios in the workbook are included. You will also find links to exam objectives, practice exams, and other resources such as the Base SAS^(R) glossary and a list of practice data sets. Major topics include importing data, creating and modifying SAS data sets, and identifying and correcting both data syntax and programming logic errors. All exam topics are covered in these chapters:

• Setting Up Practice Data
• Basic Concepts
• Accessing Your Data
• Creating SAS Data Sets
• Identifying and Correcting SAS Language Errors
• Creating Reports
• Understanding DATA Step Processing
• BY-Group Processing
• Creating and Managing Variables
• Combining SAS Data Sets
• Processing Data with DO Loops
• SAS Formats and Informats
• SAS Date, Time, and Datetime Values
• Using Functions to Manipulate Data
• Producing Descriptive Statistics
• Creating Output
• Practice Programming Scenarios (Workbook)

Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools.

RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You'll gain valuable programming skills and support your work as a data scientist at the same time.

• Work hands-on with three practical data analysis projects based on casino games
• Store, retrieve, and change data values in your computer's memory
• Write programs and simulations that outperform those written by typical R users
• Use R programming tools such as if else statements, for loops, and S3 classes
• Learn how to write lightning-fast vectorized R code
• Take advantage of R's package system and debugging tools
• Practice and apply R programming concepts as you learn them

R is an open-source programming language that has become the software tool of choice in statistics and data science. R has flexible data structures for organizing and storing data, graphical tools for generating statistical graphics, and sophisticated data analysis procedures for statistical inference.

Learning Base R provides an introduction to the language for those with or without prior programming experience. It introduces the key topics that you will need to begin analyzing data and programming in R. The focus here is on the R language rather than a particular application. The text contains over 400 exercises to assess your R skills.

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.

• Get a quick and thorough intro to the basic concepts of coding for data analysis in R
• Review what you've already learned or pick up essential new skills
• Perform statistical analysis for school, business, and beyond with R programming
• Keep this concise reference book handy for jogging your memory as you work

This book is to the point, focusing on the key topics readers need to know about this popular programming language. Great for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job.

Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an intermediate user, this book will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.

• Create vectors, handle variables, and perform basic functions
• Simplify data input and output
• Tackle data structures such as matrices, lists, factors, and data frames
• Work with probability, probability distributions, and random variables
• Calculate statistics and confidence intervals and perform statistical tests
• Create a variety of graphic displays
• Build statistical models with linear regressions and analysis of variance (ANOVA)
• Explore advanced statistical techniques, such as finding clusters in your data

Gain an understanding of the foundations of data science and its applications. This short book does not require technical abilities or cover how to code. The author focuses on nontechnical skills, such as the management of data science implementation efforts. This book is recommended for beginners and anybody who want to understand Data Science in an easy way. You don't need a big textbook to master Data Science today. Get your copy now Book ObjectivesThis book is an introduction to data science with the following objectives:

• To help you understand the correct meaning of data science.
• To help you understand the various fields that make data science.
• To familiarize you with various algorithms used in data science.
• To introduce you to the most popular data science software tools.
• To help you understand the best languages for data science tasks.
• To help you know where and how you can apply data science in your business.

Who is this Book is for? Here are the target readers for this book:

• Anybody who needs to familiarize themselves with the fundamentals of data science..
• Anybody who needs a thorough introduction to data science before venturing into its practicalities..
• Anybody who needs to know where and how they can apply data science in their business.
• Professionals in data science, computer programming, computer scientist.
• Professors, lecturers or tutors who are looking to find better ways to explain python for data analysis to their students in the simplest and easiest way.
• Students and academicians, especially those focusing on python programming, computer science, neural networks, machine learning, and deep learning.
• Anybody who needs to understand the various approaches, tools, and theories underlying data science.

What do you need for this Book? The author expects you to have a computer installed with an operating system such as Linux, Windows or Mac OS X. And you are required to have installed the following on your computer:

• Python 3.X
• Numpy
• Pandas
• Matplotlib

The Author guides you on how to install and configure the rest of the Python libraries that are required for data analysis.What is inside the book?

• BASICS OF DATA SCIENCE
• DECISION THEORY
• ESTIMATION THEORY
• COORDINATE SYSTEMS
• LINEAR TRANSFORMATION
• GRAPH THEORY
• ALGORITHMS
• MACHINE LEARNING
• DATA COLLECTION, MODELLING, AND COMPILATION
• DATA ANALYSIS
• DATA PRESENTATION AND VISUALIZATION
• DATASCIENCE SOFTWARE TOOLS
• PROGRAMMING LANGUAGES FOR DATA SCIENCE
• APPLICATIONS OF DATA SCIENCE.

The author has discussed everything that you need to know about data science. First, you are guided to learn the meaning of data science. The history of data science has been discussed to help you know how people came to realize that data is a rich source of knowledge and intelligence. The theories underlying data science have been discussed. Examples include decision and estimation theories. The author has discussed the various machine learning algorithms used in data science. The various steps one has to undergo when performing data science tasks have been discussed from data collection to data presentation and visualization. The author helps you know the various ways through which you can apply data science in your business for increased profits. A simple language has been used to ensure that there is ease of understanding, especially for beginners.

In SAS Statistics by Example, Ron Cody offers up a cookbook approach for doing statistics with SAS. Structured specifically around the most commonly used statistical tasks or techniques--for example, comparing two means, ANOVA, and regression--this book provides an easy-to-follow, how-to approach to statistical analysis not found in other books.

For each statistical task, Cody includes heavily annotated examples using ODS Statistical Graphics procedures such as SGPLOT, SGSCATTER, and SGPANEL that show how SAS can produce the required statistics. Also, you will learn how to test the assumptions for all relevant statistical tests. Major topics featured include descriptive statistics, one- and two-sample tests, ANOVA, correlation, linear and multiple regression, analysis of categorical data, logistic regression, nonparametric techniques, and power and sample size.

This is not a book that teaches statistics. Rather, SAS Statistics by Example is perfect for intermediate to advanced statistical programmers who know their statistics and want to use SAS to do their analyses.

This book is part of the SAS Press program.

For anyone who wants to be operating at a high level with the Excel Solver quickly, this is the book for you. Step-By-Step Optimization With Excel Solver is more than 200+ pages of simple yet thorough explanations on how to use the Excel Solver to solve today's most widely known optimization problems. Loaded with screen shots that are coupled with easy-to-follow instructions, this book will simplify many difficult optimization problems and make you a master of the Excel Solver almost immediately. Here are just some of the Solver optimization problems that are solved completely with simple-to-understand instructions and screen shots in this book: - The famous Traveling Salesman problem using Solver's Alldifferent constraint and the Solver's Evolutionary method to find the shortest path to reach all customers. This also provides an advanced use of the Excel INDEX function. - The well-known Knapsack Problem which shows how optimize the use of limited space while satisfying numerous other criteria. - How to perform nonlinear regression and curve-fitting on the Solver using the Solver's GRG Nonlinear solving method. - How to solve the Cutting Stock Problem faced by many manufacturing companies who are trying to determine the optimal way to cut sheets of material to minimize waste while satisfying customer orders. - Portfolio optimization to maximize return or minimize risk. - Venture capital investment selection using the Solver's Binary constraint to maximize Net Present Value of selected cash flows at year 0. Clever use of the If-Then-Else statements makes this a simple problem. - How use Solver to minimize the total cost of purchasing and shipping goods from multiple suppliers to multiple locations. - How to optimize the selection of different production machine to minimize cost while fulfilling an order. - How to optimally allocate a marketing budget to generate the greatest reach and frequency or number of inbound leads at the lowest cost. Step-By-Step Optimization With Excel Solver has complete instructions and numerous tips on every aspect of operating the Excel Solver. You'll fully understand the reports and know exactly how to tweek all of the Solver's settings for total custom use. The book also provides lots of inside advice and guidance on setting up the model in Excel so that it will be as simple and intuitive as possible to work with. All of the optimization problems in this book are solved step-by-step using a 6-step process that works every time. In addition to detailed screen shots and easy-to-follow explanations on how to solve every optimization problem in the book, a link is provided to download an Excel workbook that has all problems completed exactly as they are in this book. Step-By-Step Optimization With Excel Solver is exactly the book you need if you want to be optimizing at an advanced level with the Excel Solver quickly.

Master the concepts and techniques of statistical analysis using JMP(R)

Practical Data Analysis with JMP(R), Third Edition, highlights the powerful interactive and visual approach of JMP to introduce readers to statistical thinking and data analysis. It helps you choose the best technique for the problem at hand by using real-world cases. It also illustrates best-practice workflow throughout the entire investigative cycle, from asking valuable questions through data acquisition, preparation, analysis, interpretation, and communication of findings.

The book can stand on its own as a learning resource for professionals, or it can be used to supplement a college-level textbook for an introductory statistics course. It includes varied examples and problems using real sets of data. Each chapter typically starts with an important or interesting research question that an investigator has pursued. Reflecting the broad applicability of statistical reasoning, the problems come from a wide variety of disciplines, including engineering, life sciences, business, and economics, as well as international and historical examples.

Application Scenarios at the end of each chapter challenge you to use your knowledge and skills with data sets that go beyond mere repetition of chapter examples.

New in the third edition, chapters have been updated to demonstrate the enhanced capabilities of JMP, including projects, Graph Builder, Query Builder, and Formula Depot.