Understand the concepts and techniques of analysis while learning to reason statistically.

Being an effective analyst requires that you know how to properly define a problem and apply suitable statistical techniques, as well as clearly and honestly communicate the results with information-rich visualizations and precise language. Being a well-informed consumer of analyses requires the same set of skills so that you can recognize credible, actionable research when you see it.

Robert Carver's Practical Data Analysis with JMP, Second Edition uses the powerful interactive and visual approach of JMP to introduce readers to the logic and methods of statistical thinking and data analysis. It enables you to discriminate among and to use fundamental techniques of analysis, enabling you to engage in statistical thinking by analyzing real-world problems. “Application Scenarios” at the end of each chapter challenge you to put your knowledge and skills to use with data sets that go beyond mere repetition of chapter examples, and three new review chapters help readers integrate ideas and techniques. In addition, the scope and sequence of the chapters have been updated with more coverage of data management and analysis of data.

The book can stand on its own as a learning resource for professionals or be used to supplement a standard college-level introduction-to-statistics textbook. It includes varied examples and problems that rely on real sets of data, typically starting with an important or interesting research question that an investigator has pursued. Reflective of the broad applicability of statistical reasoning, the problems come from a wide variety of disciplines, including engineering, life sciences, business, economics, among

Practical Data Analysis with JMP, Second Edition introduces you to the major platforms and essential features of JMP and will leave you with a sufficient background and the confidence to continue your exploration independently.

This book is part of the SAS Press program.

Improve efficiency while reducing costs in clinical trials with centralized monitoring techniques using JMP and SAS.

International guidelines recommend that clinical trial data should be actively reviewed or monitored; the well-being of trial participants and the validity and integrity of the final analysis results are at stake. Traditional interpretation of this guidance for pharmaceutical trials has led to extensive on-site monitoring, including 100% source data verification. On-site review is time consuming, expensive (estimated at up to a third of the cost of a clinical trial), prone to error, and limited in its ability to provide insight for data trends across time, patients, and clinical sites. In contrast, risk-based monitoring (RBM) makes use of central computerized review of clinical trial data and site metrics to determine if and when clinical sites should receive more extensive quality review or intervention.

Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS presents a practical implementation of methodologies within JMP Clinical for the centralized monitoring of clinical trials. Focused on intermediate users, this book describes analyses for RBM that incorporate and extend the recommendations of TransCelerate Biopharm Inc., methods to detect potential patient-or investigator misconduct, snapshot comparisons to more easily identify new or modified data, and other novel visual and analytical techniques to enhance safety and quality reviews. Further discussion highlights recent regulatory guidance documents on risk-based approaches, addresses the requirements for CDISC data, and describes methods to supplement analyses with data captured external to the study database.

Given the interactive, dynamic, and graphical nature of JMP Clinical, any individual from the clinical trial team - including clinicians, statisticians, data managers, programmers, regulatory associates, and monitors - can make use of this book and the numerous examples contained within to streamline, accelerate, and enrich their reviews of clinical trial data.

The analytical methods described in Risk-Based Monitoring and Fraud Detection in Clinical Trials Using JMP and SAS enable the clinical trial team to take a proactive approach to data quality and safety to streamline clinical development activities and address shortcomings while the study is ongoing.

This book is part of the SAS Press

Key decisions determine the success of big data strategy Dynamic Customer Strategy: Big Profits from Big Data is a comprehensive guide to exploiting big data for both business-to-consumer and business-to-business marketing. This complete guide provides a process for rigorous decision making in navigating the data-driven industry shift, informing marketing practice, and aiding businesses in early adoption. Using data from a five-year study to illustrate important concepts and scenarios along the way, the author speaks directly to marketing and operations professionals who may not necessarily be big data savvy. With expert insight and clear analysis, the book helps eliminate paralysis-by-analysis and optimize decision making for marketing performance. Nearly seventy-five percent of marketers plan to adopt a big data analytics solution within two years, but many are likely to fail. Despite intensive planning, generous spending, and the best intentions, these initiatives will not succeed without a manager at the helm who is capable of handling the nuances of big data projects. This requires a new way of marketing, and a new approach to data. It means applying new models and metrics to brand new consumer behaviors. Dynamic Customer Strategy clarifies the situation, and highlights the key decisions that have the greatest impact on a company's big data plan. Topics include: Applying the elements of Dynamic Customer Strategy Acquiring, mining, and analyzing data Metrics and models for big data utilization Shifting perspective from model to customer Big data is a tremendous opportunity for marketers and may just be the only factor that will allow marketers to keep pace with the changing consumer and thus keep brands relevant at a time of unprecedented choice. But like any tool, it must be wielded with skill and precision. Dynamic Customer Strategy: Big Profits from Big Data helps marketers shape a strategy that works.

" Everyone encounters statistics on a daily basis. They are used in proposals, reports, requests, and advertisements, among others, to support assertions, opinions, and theories. Unless you're a trained statistician, it can be bewildering. What are the numbers really saying or not saying? Better Business Decisions from Data: Statistical Analysis for Professional Success provides the answers to these questions and more. It will show you how to use statistical data to improve small, every-day management judgments as well as major business decisions with potentially serious consequences. Author Peter Kenny-with deep experience in industry-believes that "while the methods of statistics can be complicated, the meaning of statistics is not." He first outlines the ways in which we are frequently misled by statistical results, either because of our lack of understanding or because we are being misled intentionally. Then he offers sound approaches for understanding and assessing statistical data to make excellent decisions. Kenny assumes no prior knowledge of statistical techniques; he explains concepts simply and shows how the tools are used in various business situations. With the arrival of Big Data, statistical processing has taken on a new level of importance. Kenny lays a foundation for understanding the importance and value of Big Data, and then he shows how mined data can help you see your business in a new light and uncover opportunity. Among other things, this book covers: How statistics can help you assess the probability of a successful outcome How data is collected, sampled, and best interpreted How to make effective forecasts based on the data at hand How to spot the misuse or abuse of statistical evidence in advertisements, reports, and proposals How to commission a statistical analysis Arranged in seven parts-Uncertainties, Data, Samples, Comparisons, Relationships, Forecasts, and Big Data-" Better Business Decisions from Data is a guide for busy people in general management, finance, marketing, operations, and other business disciplines who run across statistics on a daily or weekly basis. You'll return to it again and again as new challenges emerge, making better decisions each time that boost your organization's fortunes—as well as your own.

" R is a popular and growing open source statistical analysis and graphics environment as well as a programming language and platform. If you need to use a variety of statistics, then Using R for Statistics will get you the answers to most of the problems you are likely to encounter. Using R for Statistics is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests. The book walks you through R basics and how to use R to accomplish a wide variety statistical operations. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background. After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics. "

This up-to-date second edition provides a comprehensive examination of the theory and application of Statistical Energy Analysis (SEA) in acoustics and vibration. Complete with examples and data taken from real problems this unique book also exploresthe influence of computers on SEA and emphasizes computer based SEA calculations. In addition to a discussion of the relationship between SEA and other procedures used in response estimation, Theory and Application of Statistical Energy Anlaysis, SecondEdition, explores the basic relationships between model and wave descriptions of systems.

When it comes to discovering glitches inherent in complex systems—be it a railway or banking, chemical production, medical, manufacturing, or inventory control system—developing a simulation of a system can identify problems with less time, effort, and disruption than it would take to employ the original. Advantageous to both academic and industrial practitioners, Discrete and Continuous Simulation: Theory and Practice offers a detailed view of simulation that is useful in several fields of study. This text concentrates on the simulation of complex systems, covering the basics in detail and exploring the diverse aspects, including continuous event simulation and optimization with simulation. It explores the connections between discrete and continuous simulation, and applies a specific focus to simulation in the supply chain and manufacturing field. It discusses the Monte Carlo simulation, which is the basic and traditional form of simulation. It addresses future trends and technologies for simulation, with particular emphasis given to .NET technologies and cloud computing, and proposes various simulation optimization algorithms from existing literature. Includes chapters on input modeling and hybrid simulation Introduces general probability theory Contains a chapter on Microsoft ® Excel ™ and MATLAB ®/Simulink ® Discusses various probability distributions required for simulation Describes essential random number generators Discrete and Continuous Simulation: Theory and Practice defines the simulation of complex systems. This text benefits academic researchers in industrial/manufacturing/systems engineering, computer sciences, operations research, and researchers in transportation, operations management, healthcare systems, and human–machine systems.

" Power Query for Power BI and Excel is a book for people who are tired of copying and pasting data into Excel worksheets. Power Query, part of the Microsoft Power BI suite, is a tool that automates the process of getting data into Excel and will save you hours of dull, repetitive, and error-prone work! Power Query makes it easy to extract data from many different data sources, filter that data, aggregate it, clean it and perform calculations on it, finally loading that data into either your worksheet or directly into the new Excel 2013 Data Model used by Power Pivot. This concise, practical book provides a complete guide to Power Query and how to use it to solve all of your Excel data-loading problems. Power Query for Power BI and Excel goes well beyond the surface of what Power Query can do. The book goes deep into the underlying M language, showing you how to do amazing things that aren't going to be possible from just the GUI interface that is covered in most other books. You'll have full command of the GUI, and you'll be able to drop into the M language to go beyond what the GUI provides. The depth in this book makes it a must-have item for anyone who is pushing Power BI and Excel to their limits in the pursuit of business intelligence from data analysis. " Teaches the basics of using Power Query to load data into Excel Helps you solve common, data-related problems with Power Query Shows how to write your own solutions in the powerful M language

This book is a summary of more than a decade of research in the area of backend optimization. It contains the latest fundamental research results in this field. While existing books are often more oriented toward Masters students, this book is aimed more towards professors and researchers as it contains more advanced subjects. It is unique in the sense that it contains information that has not previously been covered by other books in the field, with chapters on phase ordering in optimizing compilation; register saturation in instruction level parallelism; code size reduction for software pipelining; memory hierarchy effects and instruction level parallelism. Other chapters provide the latest research results in well-known topics such as register need, and software pipelining and periodic register allocation.

Recursive Identification and Parameter Estimation describes a recursive approach to solving system identification and parameter estimation problems arising from diverse areas. Supplying rigorous theoretical analysis, it presents the material and proposed algorithms in a manner that makes it easy to understand—providing readers with the modeling and identification skills required for successful theoretical research and effective application. The book begins by introducing the basic concepts of probability theory, including martingales, martingale difference sequences, Markov chains, mixing processes, and stationary processes. Next, it discusses the root-seeking problem for functions, starting with the classic RM algorithm, but with attention mainly paid to the stochastic approximation algorithms with expanding truncations (SAAWET) which serves as the basic tool for recursively solving the problems addressed in the book. The book not only identifies the results of system identification and parameter estimation, but also demonstrates how to apply the proposed approaches for addressing problems in a range of areas, including: Identification of ARMAX systems without imposing restrictive conditions Identification of typical nonlinear systems Optimal adaptive tracking Consensus of multi-agents systems Principal component analysis Distributed randomized PageRank computation This book recursively identifies autoregressive and moving average with exogenous input (ARMAX) and discusses the identification of non-linear systems. It concludes by addressing the problems arising from different areas that are solved by SAAWET. Demonstrating how to apply the proposed approaches to solve problems across a range of areas, the book is suitable for students, researchers, and engineers working in systems and control, signal processing, communication, and mathematical statistics.

A quantifiable framework for unlocking the unconscious forces that shape markets There has long been a notion that subliminal forces play a great part in causing the seemingly irrational financial bubbles, which conventional economic theory, again and again, fails to explain. However, these forces, sometimes labeled 'animal spirits' or 'irrational exuberance, have remained elusive - until now. The Mystery of Market Movements provides you with a methodology to timely predict and profit from changes in human investment behaviour based on the workings of the collective unconscious. Niklas Hageback draws in on one of psychology's most influential ideas - archetypes - to explain how they form investor's perceptions and can be predicted and turned into profit. The Mystery of Market Movements provides; A review of the collective unconscious and its archetypes based on Carl Jung's theories and empirical case studies that highlights and assesses the influences of the collective unconscious on financial bubbles and zeitgeists For the first time being able to objectively measure the impact of archetypal forces on human thoughts and behaviour with a view to provide early warning signals on major turns in the markets. This is done through a step-by-step guide on how to develop a measurement methodology based on an analysis of the language of the unconscious; figurative speech such as metaphors and symbolism, drawn out and deciphered from Big Data sources, allowing for quantification into time series The book is supplemented with an online resource that presents continuously updated bespoken archetypal indexes with predictive capabilities to major financial indexes Investors are often unaware of the real reasons behind their own financial decisions. This book explains why psychological drivers in the collective unconscious dictates not only investment behaviour but also political, cultural and social trends. Understanding these forces allows you to stay ahead of the curve and profit from market tendencies that more traditional methods completely overlook.

Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets. The first three chapters explain the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. These chapters cover discrete Bayesian, Gaussian Bayesian, and hybrid networks, including arbitrary random variables. The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R and other software packages appropriate for Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein signaling network paper and graphical modeling approaches for predicting the composition of different body parts. Suitable for graduate students and non-statisticians, this text provides an introductory overview of Bayesian networks. It gives readers a clear, practical understanding of the general approach and steps involved.

Go beyond spreadsheets and tables and design a data presentation that really makes an impact. This practical guide shows you how to use Tableau Software to convert raw data into compelling data visualizations that provide insight or allow viewers to explore the data for themselves. Ideal for analysts, engineers, marketers, journalists, and researchers, this book describes the principles of communicating data and takes you on an in-depth tour of common visualization methods. You’ll learn how to craft articulate and creative data visualizations with Tableau Desktop 8.1 and Tableau Public 8.1. Present comparisons of how much and how many Use blended data sources to create ratios and rates Create charts to depict proportions and percentages Visualize measures of mean, median, and mode Lean how to deal with variation and uncertainty Communicate multiple quantities in the same view Show how quantities and events change over time Use maps to communicate positional data Build dashboards to combine several visualizations

The long-awaited revision of Fundamentals of Applied Probability and Random Processes expands on the central components that made the first edition a classic. The title is based on the premise that engineers use probability as a modeling tool, and that probability can be applied to the solution of engineering problems. Engineers and students studying probability and random processes also need to analyze data, and thus need some knowledge of statistics. This book is designed to provide students with a thorough grounding in probability and stochastic processes, demonstrate their applicability to real-world problems, and introduce the basics of statistics. The book's clear writing style and homework problems make it ideal for the classroom or for self-study. Demonstrates concepts with more than 100 illustrations, including 2 dozen new drawings Expands readers’ understanding of disruptive statistics in a new chapter (chapter 8) Provides new chapter on Introduction to Random Processes with 14 new illustrations and tables explaining key concepts. Includes two chapters devoted to the two branches of statistics, namely descriptive statistics (chapter 8) and inferential (or inductive) statistics (chapter 9).

This book, 'Learning NumPy Array,' is the ultimate guide to mastering the fundamental library for numerical computing in Python: NumPy. Through concise explanations and practical examples, you will learn how to create and manipulate arrays, perform complex computations, and leverage NumPy's capabilities to streamline data analysis workflows. What this Book will help me do Install and set up NumPy in your Python environment for numerical computing. Create and manipulate multidimensional arrays to handle and process large data sets. Perform complex mathematical and statistical computations with NumPy's built-in methods. Explore time series analysis and signal processing techniques using NumPy. Optimize and improve the performance of Python code leveraging NumPy's efficient operations. Author(s) Ivan Idris is a seasoned programmer and data scientist with a great passion for Python and numerical computing. With years of experience working on data analysis projects, he has solidified his expertise in Python's scientific libraries, including NumPy. Ivan creates practical, reader-friendly guides that not only teach the technical how-to's but also inspire confidence in solving real-world problems. Who is it for? This book is ideal for Python programmers taking their first steps into the world of numerical computing or data analysis. Beginners looking to understand the basics of handling large numerical datasets in Python will find this resource highly enlightening. Developers and scientists wanting to streamline their calculations using efficient techniques will gain valuable insights. If working with Python in a data-driven environment interests you, this book is for you.

Learn How to Program Stochastic Models Highly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an excellent, easy-to-read introduction with extensive examples and exercises. This second edition continues to introduce scientific programming and stochastic modelling in a clear, practical, and thorough way. Readers learn programming by experimenting with the provided R code and data. The book’s four parts teach: Core knowledge of R and programming concepts How to think about mathematics from a numerical point of view, including the application of these concepts to root finding, numerical integration, and optimisation Essentials of probability, random variables, and expectation required to understand simulation Stochastic modelling and simulation, including random number generation and Monte Carlo integration In a new chapter on systems of ordinary differential equations (ODEs), the authors cover the Euler, midpoint, and fourth-order Runge-Kutta (RK4) schemes for solving systems of first-order ODEs. They compare the numerical efficiency of the different schemes experimentally and show how to improve the RK4 scheme by using an adaptive step size. Another new chapter focuses on both discrete- and continuous-time Markov chains. It describes transition and rate matrices, classification of states, limiting behaviour, Kolmogorov forward and backward equations, finite absorbing chains, and expected hitting times. It also presents methods for simulating discrete- and continuous-time chains as well as techniques for defining the state space, including lumping states and supplementary variables. Building readers’ statistical intuition, Introduction to Scientific Programming and Simulation Using R, Second Edition shows how to turn algorithms into code. It is designed for those who want to make tools, not just use them. The code and data are available for download from CRAN.

Methods and Applications of Statistics in Clinical Trials, Volume 2: Planning, Analysis, and Inferential Methods includes updates of established literature from the Wiley Encyclopedia of Clinical Trials as well as original material based on the latest developments in clinical trials. Prepared by a leading expert, the second volume includes numerous contributions from current prominent experts in the field of medical research. In addition, the volume features: Multiple new articles exploring emerging topics, such as evaluation methods with threshold, empirical likelihood methods, nonparametric ROC analysis, over- and under-dispersed models, and multi-armed bandit problems Up-to-date research on the Cox proportional hazard model, frailty models, trial reports, intrarater reliability, conditional power, and the kappa index Key qualitative issues including cost-effectiveness analysis, publication bias, and regulatory issues, which are crucial to the planning and data management of clinical trials

The second book in Darbyshire and Hampton's Hedge Fund Modelling and Analysis series, Hedge Fund Modelling and Analysis Using MATLAB® takes advantage of the huge library of built-in functions and suite of financial and analytic packages available to MATLAB®. This allows for a more detailed analysis of some of the more computationally intensive and advanced topics, such as hedge fund classification, performance measurement and mean-variance optimisation. Darbyshire and Hampton's first book in the series, Hedge Fund Modelling and Analysis Using Excel & and VBA, is seen as a valuable supplementary text to this book. Starting with an overview of the hedge fund industry the book then looks at a variety of commercially available hedge fund data sources. After covering key statistical techniques and methods, the book discusses mean-variance optimisation, hedge fund classification and performance with an emphasis on risk-adjusted return metrics. Finally, common hedge fund market risk management techniques, such as traditional Value-at-Risk methods, modified extensions and expected shortfall are covered. The book's dedicated website, www.darbyshirehampton.com provides free downloads of all the data and MATLAB® source code, as well as other useful resources. Hedge Fund Modelling and Analysis Using MATLAB® serves as a definitive introductory guide to hedge fund modelling and analysis and will provide investors, industry practitioners and students alike with a useful range of tools and techniques for analysing and estimating alpha and beta sources of return, performing manager ranking and market risk management.

In recent years, the theory has become widely accepted and has been further developed, but a detailed introduction is needed in order to make the material available and accessible to a wide audience. This will be the first book providing such an introduction, covering core theory and recent developments which can be applied to many application areas. All authors of individual chapters are leading researchers on the specific topics, assuring high quality and up-to-date contents. An Introduction to Imprecise Probabilities provides a comprehensive introduction to imprecise probabilities, including theory and applications reflecting the current state if the art. Each chapter is written by experts on the respective topics, including: Sets of desirable gambles; Coherent lower (conditional) previsions; Special cases and links to literature; Decision making; Graphical models; Classification; Reliability and risk assessment; Statistical inference; Structural judgments; Aspects of implementation (including elicitation and computation); Models in finance; Game-theoretic probability; Stochastic processes (including Markov chains); Engineering applications. Essential reading for researchers in academia, research institutes and other organizations, as well as practitioners engaged in areas such as risk analysis and engineering.

This book provides beginners with a thorough foundation in SAS macro programming.

The macro facility is a popular part of SAS. Macro programming is a required skill for many SAS programming jobs, and the SAS Advanced Programming Certification Exam tests macro processing concepts. Whether you're looking to become certified, land a job, or increase your skills, you'll benefit from SAS Macro Programming Made Easy, Third Edition.

By following Michele Burlew's examples and step-by-step instructions, you'll be able to rapidly perform repetitive programming tasks, to pass information between programming steps more easily, and to make your programming easier to read.

Updated for SAS 9.4, this book teaches you the elements of the macro facility (macro variables, macro programs, macro language), how to write a macro program, techniques for macro programming, tips on using the macro facility, how the macro facility fits into SAS, and about the interfaces between the macro facility and other components of SAS.

Beginning macro programmers will learn to write SAS macro programs quickly and efficiently. More experienced programmers will find this book useful to refresh their conceptual knowledge and expand on their macro programming skills. Ultimately, any user interested in automating their programs—including analysts, programmers, and report writers—will find Michele Burlew's book an excellent tutorial.

This book is part of the SAS Press program.

Nonlinear Parameter Optimization Using R John C. Nash, Telfer School of Management, University of Ottawa, Canada A systematic and comprehensive treatment of optimization software using R In recent decades, optimization techniques have been streamlined by computational and artificial intelligence methods to analyze more variables, especially under non-linear, multivariable conditions, more quickly than ever before. Optimization is an important tool for decision science and for the analysis of physical systems used in engineering. Nonlinear Parameter Optimization with R explores the principal tools available in R for function minimization, optimization, and nonlinear parameter determination and features numerous examples throughout. Nonlinear Parameter Optimization with R: Provides a comprehensive treatment of optimization techniques Examines optimization problems that arise in statistics and how to solve them using R Enables researchers and practitioners to solve parameter determination problems Presents traditional methods as well as recent developments in R Is supported by an accompanying website featuring R code, examples and datasets Researchers and practitioners who have to solve parameter determination problems who are users of R but are novices in the field optimization or function minimization will benefit from this book. It will also be useful for scientists building and estimating nonlinear models in various fields such as hydrology, sports forecasting, ecology, chemical engineering, pharmaco-kinetics, agriculture, economics and statistics.

Statistical Applications for Environmental Analysis and Risk Assessment guides readers through real-world situations and the best statistical methods used to determine the nature and extent of the problem, evaluate the potential human health and ecological risks, and design and implement remedial systems as necessary. Featuring numerous worked examples using actual data and "ready-made" software scripts, Statistical Applications for Environmental Analysis and Risk Assessment also includes: Descriptions of basic statistical concepts and principles in an informal style that does not presume prior familiarity with the subject Detailed illustrations of statistical applications in the environmental and related water resources fields using real-world data in the contexts that would typically be encountered by practitioners Software scripts using the high-powered statistical software system, R, and supplemented by USEPA's ProUCL and USDOE's VSP software packages, which are all freely available Coverage of frequent data sample issues such as non-detects, outliers, skewness, sustained and cyclical trend that habitually plague environmental data samples Clear demonstrations of the crucial, but often overlooked, role of statistics in environmental sampling design and subsequent exposure risk assessment.

Microsoft SQL Server 2014 Business Intelligence Development Beginner's Guide introduces you to Microsoft's BI tools and systems. You'll gain hands-on experience building solutions that handle data warehousing, reporting, and predictive analytics. With step-by-step tutorials, you'll be equipped to transform data into actionable insights. What this Book will help me do Understand and implement multidimensional data models using SSAS and MDX. Write and use DAX queries and leverage SSAS tabular models effectively. Improve and maintain data integrity using MDS and DQS tools. Design and develop polished, insightful dashboards and reports using PerformancePoint, Power View, and SSRS. Explore advanced data analysis features, such as Power Query, Power Map, and basic data mining techniques. Author(s) Abolfazl Radgoudarzi and Reza Rad are experienced practitioners and educators in the field of business intelligence. They specialize in SQL Server BI technologies and have extensive careers helping organizations harness data for decision-making. Their approach combines clear explanations with practical examples, ensuring readers can effectively apply what they learn. Who is it for? This book is ideal for database developers, system analysts, and IT professionals looking to build strong foundations in Microsoft SQL Server's BI technologies. Beginners in business intelligence or data management will find the topics accessible. Intermediate practitioners will expand their ability to build complete BI solutions. It's designed for anyone eager to develop skills in data modeling, analysis, and visualization.

Although widely used in science and technology for experimental data generating, modeling, and optimization, the response surface methodology (RSM) has many limitations. Showing how robust response surface methodology (RRSM) can overcome these limitations, Robust Response Surfaces, Regression, and Positive Data Analyses presents RRS designs, along with the relevant regression and positive data analysis techniques. It explains how to use RRSM in experimental designs and regression analysis. The book addresses problems of RRS designs, such as rotatability, slope-rotatability, weak rotatability, and optimality. It describes methods for estimating model parameters as well as positive data analysis techniques. The author illustrates the concepts and methods with real examples of lifetime responses, resistivity, replicated measures, and more. The range of topics and applications gives the book broad appeal both to theoreticians and practicing professionals. The book helps quality engineers, scientists in any area, medical practitioners, demographers, economists, and statisticians understand the theory and applications of RRSM. It can also be used in a second course on the design of experiments.

Use computational intelligence to drive more value from business analytics, overcome real-world uncertainties and complexities, and make better decisions. Drawing on his pioneering experience as an instructor and researcher, Dr. Les Sztandera thoroughly illuminates today's key computational intelligence tools, knowledge, and strategies for analysis, exploration, and knowledge generation. Sztandera demystifies artificial neural networks, genetic algorithms, and fuzzy systems, and guides you through using them to model, discover, and interpret new patterns that can't be found through statistical methods alone. Packed with relevant case studies and examples, this guide demonstrates: Customer segmentation for direct marketing Customer profiling for relationship management Efficient mailing campaigns Customer retention Identification of cross-selling opportunities Credit score analysis Detection of fraudulent behavior and transactions Hedge fund strategies, and more Szandera shows how computational intelligence can inform the design and integration of services, architecture, brand identity, and product portfolio across the entire enterprise. He also shows how to complement computational intelligence with visualization, explorative interfaces and advanced reporting, thereby empowering business users and enterprise stakeholders to take full advantage of it. For analytics professionals, managers, and students.