Articles by "Statistics"

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Showing posts with label Statistics. Show all posts

Patrick Laurie Davies ... 320 pages - Publisher: Chapman and Hall/CRC; (July, 2014) ... Language: English - ASIN: B00L2LDVM0 by Amazon

The First Detailed Account of Statistical Analysis That Treats Models as Approximations: The idea of truth plays a role in both Bayesian and frequentist statistics. The Bayesian concept of coherence is based on the fact that two different models or parameter values cannot both be true. Frequentist statistics is formulated as the problem of estimating the "true but unknown" parameter value that generated the data. Forgoing any concept of truth, Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis presents statistical analysis/inference based on approximate models. Developed by the author, this approach consistently treats models as approximations to data, not to some underlying truth.

The author develops a concept of approximation for probability models with applications to: Discrete data + Location scale + Analysis of variance (ANOVA) + Nonparametric regression, image analysis, and densities + Time series + Model choice. The book first highlights problems with concepts such as likelihood and efficiency and covers the definition of approximation and its consequences. A chapter on discrete data then presents the total variation metric as well as the Kullback–Leibler and chi-squared discrepancies as measures of fit. After focusing on outliers, the book discusses the location-scale problem, including approximation intervals, and gives a new treatment of higher-way ANOVA. The next several chapters describe novel procedures of nonparametric regression based on approximation. The final chapter assesses a range of statistical topics, from the likelihood principle to asymptotics and model choice.

Daniel J. Denis ... 224 pages - Publisher: Wiley; (September, 2018) ... Language: English - ISBN-10: 9781119465812 - ISBN-13: 978-1119465812

This concise and very easy-to-use primer introduces readers to a host of computational tools useful for making sense out of data, whether that data come from the social, behavioral, or natural sciences. The book places great emphasis on both data analysis and drawing conclusions from empirical observations. It also provides formulas where needed in many places, while always remaining focused on concepts rather than mathematical abstraction. SPSS Data Analysis for Univariate, Bivariate, and Multivariate Statistics offers a variety of popular statistical analyses and data management tasks using SPSS that readers can immediately apply as needed for their own research, and emphasizes many helpful computational tools used in the discovery of empirical patterns. The book begins with a review of essential statistical principles before introducing readers to SPSS. The book then goes on to offer chapters on: Exploratory Data Analysis, Basic Statistics, and Visual Displays; Data Management in SPSS; Inferential Tests on Correlations, Counts, and Means; Power Analysis and Estimating Sample Size; Analysis of Variance – Fixed and Random Effects; Repeated Measures ANOVA; Simple and Multiple Linear Regression; Logistic Regression; Multivariate Analysis of Variance (MANOVA) and Discriminant Analysis; Principal Components Analysis; Exploratory Factor Analysis; and Non-Parametric Tests. This helpful resource allows readers to: Understand data analysis in practice rather than delving too deeply into abstract mathematical concepts. Make use of computational tools used by data analysis professionals. Focus on real-world application to apply concepts from the book to actual research.

Conrad Carlberg ... 568 pages - Publisher: Que Publishing; (December, 2017) ... Language: English - ISBN-10: 0789759055 - ISBN-13: 978-0789759054

Nationally recognized Excel expert Conrad Carlberg shows you how to use Excel 2016 to perform core statistical tasks every business professional, student, and researcher should master. Using real-world examples and downloadable workbooks, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features. Along the way, he clarifies confusing statistical terminology and helps you avoid common mistakes. You’ll learn how to use correlation and regression, analyze variance and covariance, and test statistical hypotheses using the normal, binomial, t, and F distributions. To help you make accurate inferences based on samples from a population, Carlberg offers insightful coverage of crucial topics ranging from experimental design to the statistical power of F tests. Updated for Excel 2016, this guide covers both modern consistency functions and legacy compatibility functions. Becoming an expert with Excel statistics has never been easier! In this book, you’ll find crystal-clear instructions, insider insights, and complete step-by-step guidance.

Practical Statistics For Data And Business Analysis [Size: 6.34 GB] ... This course material is prepared from highly experienced engineers worked in a leader companies like Microsoft , Facebook and Google. After hard working from five months ago we created +270 Lectures/Articles to cover everything related to practical statistics. In no time with simple and easy way you will learn and love statistics. We stress in this course to make it very spontaneous to make all students love statistics .

Statgraphics Centurion 18.1.12 for x64 [Size: 176 MB] ... Statgraphics Centurion 18 is a comprehensive Windows desktop product for statistical analysis, data visualization and predictive analytics. It contains over 260 procedures covering a wide range of data analysis techniques. Statgraphics 18 features an easy-to-use GUI that does not require learning a complicated command language. Especially helpful to practitioners is the StatAdvisor, which explains the results of statistical analyses in a manner suitable for presentation to non-statisticians. Statgraphics 18 is a major upgrade that contains many new features, including: A new file format for handling big data. The 64-bit version can now analyze datasets containing in excess of 100 million rows. * 9 new interactive Statlets for dynamic data visualization, including sunflower plots, population pyramids, wind roses, and violin plots. * Improved demographic maps, with support for SHP files. * Dialog-box driven procedures for accessing R libraries to use classification and regression trees (CART), text mining, multidimensional scaling, analysis of arbitrarily censored data, and seasonal adjustment with X-13ARIMA-SEATS. * Over a dozen other new statistical procedures, including equivalence analysis and noninferiority testing, orthogonal regression, capability control charts, multivariate tolerance intervals, tests for multivariate normality, and random number generation for multivariate normal distributions. * New definitive screening designs in the DOE Wizard.* A new network installation program which allows users to check out seats for use on individual computers. * Extensive enhancements to the process capability analysis procedures to implement the methods described in the new book by Dr. Neil W. Polhemus, Process Capability Analysis: Estimating Quality to be published by Chapman and Hall/CRC Press in December, 2017.

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Keith McCormick, Jesus Salcedo, Jon Peck, Andrew Wheeler ... 528 pages - Publisher: Wiley; 1st edition (May, 2017) ... Language: English - ISBN-10: 1119003555 - ISBN-13: 978-1119003557

Dive deeper into SPSS Statistics for more efficient, accurate, and sophisticated data analysis and visualization: SPSS Statistics for Data Analysis and Visualization goes beyond the basics of SPSS Statistics to show you advanced techniques that exploit the full capabilities of SPSS. The authors explain when and why to use each technique, and then walk you through the execution with a pragmatic, nuts and bolts example. Coverage includes extensive, in-depth discussion of advanced statistical techniques, data visualization, predictive analytics, and SPSS programming, including automation and integration with other languages like R and Python. You'll learn the best methods to power through an analysis, with more efficient, elegant, and accurate code. IBM SPSS Statistics is complex: true mastery requires a deep understanding of statistical theory, the user interface, and programming. Most users don't encounter all of the methods SPSS offers, leaving many little-known modules undiscovered. This book walks you through tools you may have never noticed, and shows you how they can be used to streamline your workflow and enable you to produce more accurate results. Conduct a more efficient and accurate analysis - Display complex relationships and create better visualizations - Model complex interactions and master predictive analytics - Integrate R and Python with SPSS Statistics for more efficient, more powerful code. These "hidden tools" can help you produce charts that simply wouldn't be possible any other way, and the support for other programming languages gives you better options for solving complex problems. If you're ready to take advantage of everything this powerful software package has to offer, SPSS Statistics for Data Analysis and Visualization is the expert-led training you need.

John Fox, Sanford Weisberg ... 608 pages - Publisher: SAGE Publications; 3rd edition (October, 2018) ... Language: English - ISBN-10: 1544336470 - ISBN-13: 978-1544336473 ...

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the carand effects packages for R for this new edition, and include coverage of RStudio and R Markdown.

Jacek Welc, Pedro J. R. Esquerdo ... 286 pages - Publisher: Springer; (December, 2017) ... Language: English - ASIN: B078Q1RQDV by Amazon...

This book offers hands-on statistical tools for business professionals by focusing on the practical application of a single-equation regression. The authors discuss commonly applied econometric procedures, which are useful in building regression models for economic forecasting and supporting business decisions. A significant part of the book is devoted to traps and pitfalls in implementing regression analysis in real-world scenarios. The book consists of nine chapters, the final two of which are fully devoted to case studies. Today's business environment is characterised by a huge amount of economic data. Making successful business decisions under such data-abundant conditions requires objective analytical tools, which can help to identify and quantify multiple relationships between dozens of economic variables. Single-equation regression analysis, which is discussed in this book, is one such tool. The book offers a valuable guide and is relevant in various areas of economic and business analysis, including marketing, financial and operational management.

David L. Olson, Desheng Wu ... 102 pages - Publisher: Springer; (September, 26) ... Language: English - ASIN: B01LWORTEH by Amazon ...

This book reviews forecasting data mining models, from basic tools for stable data through causal models, to more advanced models using trends and cycles. These models are demonstrated on the basis of business-related data, including stock indices, crude oil prices, and the price of gold. The book’s main approach is above all descriptive, seeking to explain how the methods concretely work; as such, it includes selected citations, but does not go into deep scholarly reference. The data sets and software reviewed were selected for their widespread availability to all readers with internet access.

Daniel J. Denis ... 760 pages - Publisher: Wiley; (December, 2015) ... Language: English - ISBN-10: 9781118632338 - ISBN-13: 978-1118632338 ...

A clear and efficient balance between theory and application of statistical modeling techniques in the social and behavioral sciences: Written as a general and accessible introduction, Applied Univariate, Bivariate, and Multivariate Statistics provides an overview of statistical modeling techniques used in fields in the social and behavioral sciences. Blending statistical theory and methodology, the book surveys both the technical and theoretical aspects of good data analysis. Featuring applied resources at various levels, the book includes statistical techniques such as t-tests and correlation as well as more advanced procedures such as MANOVA, factor analysis, and structural equation modeling. To promote a more in-depth interpretation of statistical techniques across the sciences, the book surveys some of the technical arguments underlying formulas and equations. Applied Univariate, Bivariate, and Multivariate Statistics also features: Demonstrations of statistical techniques using software packages such as R and SPSS + Examples of hypothetical and real data with subsequent statistical analyses + Historical and philosophical insights into many of the techniques used in modern social science + A companion website that includes further instructional details, additional data sets, solutions to selected exercises, and multiple programming options. An ideal textbook for courses in statistics and methodology at the upper- undergraduate and graduate-levels in psychology, political science, biology, sociology, education, economics, communications, law, and survey research, Applied Univariate, Bivariate, and Multivariate Statistics is also a useful reference for practitioners and researchers in their field of application.

Xiaofeng Wang, Yu Ryan Yue, Julian J. Faraway ... 324 pages - Publisher: Chapman and Hall/CRC; Language: English - ISBN-10: 1498727255 - ISBN-13: 978-1498727259 ...

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (MCMC), the standard tool for Bayesian inference. Bayesian Regression Modeling with INLA covers a wide range of modern regression models and focuses on the INLA technique for building Bayesian models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to demonstrate the interplay of theory and practice with reproducible studies. Complete R commands are provided for each example, and a supporting website holds all of the data described in the book. An R package including the data and additional functions in the book is available to download. The book is aimed at readers who have a basic knowledge of statistical theory and Bayesian methodology. It gets readers up to date on the latest in Bayesian inference using INLA and prepares them for sophisticated, real-world work.

Conrad Carlberg ... 426 pages - Publisher: Que Publishing; (April, 2011) ... Language: English - ASIN: B0054RF7YI by Amazon

Top Excel guru Conrad Carlberg shows how to use Excel 2010 to perform the core statistical tasks every business professional, student, and researcher should master. Using real-world examples, Carlberg helps you choose the right technique for each problem and get the most out of Excel’s statistical features, including its new consistency functions. Along the way, you discover the most effective ways to use correlation and regression and analysis of variance and covariance. You see how to use Excel to test statistical hypotheses using the normal, binomial, t and F distributions. Becoming an expert with Excel statistics has never been easier! You’ll find crystal-clear instructions, insider insights, and complete step-by-step projects—all complemented by an extensive set of web-based resources. • Master Excel’s most useful descriptive and inferential statistical tools. • Tell the truth with statistics, and recognize when others don’t. • Accurately summarize sets of values. • View how values cluster and disperse. • Infer a population’s characteristics from a sample’s frequency distribution. • Explore correlation and regression to learn how variables move in tandem. • Understand Excel’s new consistency functions. • Test differences between two means using z tests, t tests, and Excel’s Data Analysis Add-in. • Use ANOVA and ANCOVA to test differences between more than two means. • Explore statistical power by manipulating mean differences, standard errors, directionality, and alpha.

Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining ... 672 pages - Publisher: Wiley; 5th edition (April, 2012) ... Language: English - ISBN-10: 9780470542811 - ISBN-13: 978-0470542811 ... 

A comprehensive and up-to-date introduction to the fundamentals of regression analysis: Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling,including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics,including: * A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models. * Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model. * Tests on individual regression coefficients and subsets of coefficients. * Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition.Numerous exercises have been added throughout, allowing readers to test their understanding of the material. Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.

Luca Massaron ... 312 pages - Publisher: Packt Publishing; (February, 2016) ... Language: English - ISBN-10: 1785286315 - ISBN-13: 978-1785286315 ...

Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.

Andrew Gelman, Jennifer Hill ... 648 pages - Publisher: Cambridge Univ. Press; (December, 2006) ... Language: English - ISBN-10: 052168689X - ISBN-13: 978-0521686891 ...

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

Stata Software for Statistics and Data Science v15 [Size: 295.5 MB] ... Stata is a general-purpose statistical software package created in 1985 by StataCorp. Most of its users work in research, especially in the fields of economics, sociology, political science, biomedicine and epidemiology.[citation needed]Stata's capabilities include data management, statistical analysis, graphics, simulations, regression analysis (linear and multiple), and custom programming. The name Stata is a portmanteau of the words statistics and data. The correct English pronunciation of Stata "must remain a mystery"; any of "Stay-ta", "Sta-ta" or "Stah-ta" are considered acceptable.

Features: Linear models: regression • censored outcomes • endogenous regressors • bootstrap, jackknife, and robust and cluster–robust variance • instrumental variables • three-stage least squares • constraints • quantile regression • GLS • more. Panel/longitudinal data: random and fixed effects with robust standard errors • linear mixed models • random-effects probit • GEE • random- and fixed-effects Poisson • dynamic panel-data models • instrumental variables • panel unit-root tests • more. Multilevel mixed-effects models: continuous, binary, count, and survival outcomes • two-, three-, and higher-level models • generalized linear models • nonlinear models • random intercepts • random slopes • crossed random effects • BLUPs of effects and fitted values • hierarchical models • residual error structures • DDF adjustments • support for survey data • more. Binary, count, and limited outcomes: logistic, probit, tobit • Poisson and negative binomial • conditional, multinomial, nested, ordered, rank-ordered, and stereotype logistic • multinomial probit • zero-inflated and left-truncated count models • selection models • marginal effects • more. Extended regression models (ERMs): combine endogenous covariates, sample selection, and nonrandom treatment in models for continuous, interval-censored, binary, and ordinal outcomes • more.

Jian Qing Shi, Taeryon Choi ... 216 pages - Publisher: CRC; (July, 2011) .. Language: English - ISBN-10: 1439837732 - ISBN-13: 978-1439837733 ...

Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables. Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dimensional data and variable selection. The remainder of the text explores advanced topics of functional regression analysis, including novel nonparametric statistical methods for curve prediction, curve clustering, functional ANOVA, and functional regression analysis of batch data, repeated curves, and non-Gaussian data. Many flexible models based on Gaussian processes provide efficient ways of model learning, interpreting model structure, and carrying out inference, particularly when dealing with large dimensional functional data. This book shows how to use these Gaussian process regression models in the analysis of functional data. Some MATLAB® and C codes are available on the first author’s website.

Geoff Der, Brian S. Everitt ... 246 pages - Publisher: SAS Institute; (December, 2015) ... Language: English - ISBN-10: 1629598437 - ISBN-13: 978-1629598437

Students and instructors of statistics courses using SAS University Edition will welcome this book. Learning fundamental statistics is essential to solving problems with SAS. Essential Statistics Using SAS University Edition demonstrates how to use SAS University Edition to apply a variety of statistical methodologies, from the simple to the not-so-simple, to a range of data sets. Learn how to apply the appropriate statistical method to answer a particular question about a data set, and correctly interpret the numerical results that you obtain. SAS University Edition users who are new to SAS or who need a refresher course will benefit from the statistics overview and topics, such as multiple linear regression, logistic regression, and Poisson regression.

Sabine Landau, Brian S. Everitt ... 366 pages - Publisher: Chapman and Hall/CRC; 1st edition (November, 2003) ... Language: English - ISBN-10: 1584883693 - ISBN-13: 978-1584883692 ...

A Handbook of Statistical Analyses Using SPSS clearly describes how to conduct a range of univariate and multivariate statistical analyses using the latest version of the Statistical Package for the Social Sciences, SPSS 11. Each chapter addresses a different type of analytical procedure applied to one or more data sets, primarily from the social and behavioral sciences areas. Each chapter also contains exercises relating to the data sets introduced, providing readers with a means to develop both their SPSS and statistical skills. Model answers to the exercises are also provided. Readers can download all of the data sets from a companion Web site furnished by the authors.

Mohammed M. Alani, Hissam Tawfik, Mohammed Saeed, Obinna Anya ... 214 pages - Publisher: Springer; 1st edition (July, 2018) ... Language: English - ASIN: B07FP1J69L by Amazon ...

This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery.

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