Articles by "Statistics"

Showing posts with label Statistics. Show all posts

Sunday, November 10, 2024 , ,
Antonio Marco ... 160 pages - Language: ‎English - Publisher: Chapman and Hall/CRC; (December, 2023).

Statistics is central in the biosciences, social sciences and other disciplines, yet many students often struggle to learn how to perform statistical tests, and to understand how and why statistical tests work. Although there are many approaches to teaching statistics, a common framework exists between them: starting with probability and distributions, then sampling from distribution and descriptive statistics and later introducing both simple and complex statistical tests, typically ending with regression analysis (linear models).

This book proposes to reverse the way statistics is taught, by starting with the introduction of linear models. Today, many statisticians know that the one unifying principle of statistical tests is that most of them are instances of linear models. This teaching method has two advantages: all statistical tests in a course can be presented under the same unifying framework, simplifying things; second, linear models can be expressed as lines over squared paper, replacing any equation with a drawing.

This book explains how and why statistics works without using a single equation, just lines and squares over grid paper. The reader will have the opportunity to work through the examples and compute sums of squares by just drawing and counting, and finally evaluating whether observed differences are statistically significant by using the tables provided. Intended for students, scientists and those with little prior knowledge of statistics, this book is for all with simple and clear examples, computations and drawings helping the reader to not only do statistical tests but also understand statistics.
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Saturday, August 31, 2024 , ,
Tanvir Mustafy, T. Ur Rahman ... 281 pages - Language: ‎English - Publisher: ‎Springer; (January, 2024).

This textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level. It aims to combine the data analysis methods using three different programs—Microsoft Excel, SPSS, and MATLAB. The book combining the different data analysis tools is a unique approach. The book presents a variety of real-life problems in data analysis and machine learning, delivering the best solution. Analysis methods presented in this book include but are not limited to, performing various algebraic and trigonometric operations, regression modeling, and correlation, as well as plotting graphs and charts to represent the results. Fundamental concepts of applied statistics are also explained here, with illustrative examples. Thus, this book presents a pioneering solution to help a wide range of students, researchers, and professionals learn data processing, interpret different findings derived from the analyses,and apply them to their research or professional fields.

The book also includes worked examples of practical problems. The primary focus behind designing these examples is understanding the concepts of data analysis and how it can solve problems. The chapters include practice exercises to assist users in enhancing their skills to execute statistical analysis calculations using software instead of relying on tables for probabilities and percentiles in the present world.
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Sunday, June 23, 2024 , ,
W. Holmes Finch, Jocelyn E. Bolin ... 338 pages - Language: ‎English - Publisher: Chapman and Hall/CRC; 3rd edition (April, 2024).

Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment.

After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single-level and multilevel data.

The third edition of the book includes several new topics that were not present in the second edition. Specifically, a new chapter has been included, focussing on fitting multilevel latent variable modeling in the R environment. With R, it is possible to fit a variety of latent variable models in the multilevel context, including factor analysis, structural models, item response theory, and latent class models. The third edition also includes new sections in Chapter 11 describing two useful alternatives to standard multilevel models, fixed effects models and generalized estimating equations. These approaches are particularly useful with small samples and when the researcher is interested in modeling the correlation structure within higher-level units (e.g., schools). The third edition also includes a new section on mediation modeling in the multilevel context, in Chapter 11.

This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research.
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Wednesday, June 19, 2024 , ,
David Kaplan ... 250 pages - Language: English - Publisher: ‎The Guilford Press; 2nd edition (November, 2023) - ISBN-10:‎ 1462553540 - ISBN-13: ‎978-1462553549.

The second edition of this practical book equips social science researchers to apply the latest Bayesian methodologies to their data analysis problems. It includes new chapters on model uncertainty, Bayesian variable selection and sparsity, and Bayesian workflow for statistical modeling. Clearly explaining frequentist and epistemic probability and prior distributions, the second edition emphasizes use of the open-source RStan software package. The text covers Hamiltonian Monte Carlo, Bayesian linear regression and generalized linear models, model evaluation and comparison, multilevel modeling, models for continuous and categorical latent variables, missing data, and more. Concepts are fully illustrated with worked-through examples from large-scale educational and social science databases, such as the Program for International Student Assessment and the Early Childhood Longitudinal Study. Annotated RStan code appears in screened boxes; the companion website (www.guilford.com/kaplan-materials) provides data sets and code for the book's examples.

New to This Edition: • Utilizes the R interface to Stan—faster and more stable than previously available Bayesian software—for most of the applications discussed. • Coverage of Hamiltonian MC; Cromwell’s rule; Jeffreys' prior; the LKJ prior for correlation matrices; model evaluation and model comparison, with a critique of the Bayesian information criterion; variational Bayes as an alternative to Markov chain Monte Carlo (MCMC) sampling; and other new topics. • Chapters on Bayesian variable selection and sparsity, model uncertainty and model averaging, and Bayesian workflow for statistical modeling.
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Saturday, May 25, 2024 , ,
William Navidi ... 960 pages - Language: English - Publisher: McGraw Hill; 6th edition (January, 2023) - ISBN-10: ‎1265875855 - ISBN-13: ‎978-1265875855.

Statistics for Engineers and Scientists stands out for its clear presentation of applied statistics. The book takes a practical approach to methods of statistical modeling and data analysis that are most often used in scientific work. This edition features a unique approach highlighted by an engaging writing style that explains difficult concepts clearly, along with the use of contemporary real world data sets, to help motivate students and show direct connections to industry and research. While focusing on practical applications of statistics, the text makes extensive use of examples to motivate fundamental concepts and to develop intuition.
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Sunday, August 06, 2023 , , ,
Language: English - Education Time: 7 hours and 28 minutes - Level: Elementary, Secondary - Size: 2.72 GB.

Data analysis is one of the leading jobs in the current technology market. As per the forecasts of Glassdoor and World Economic Forum, the demand for data scientists will also increase in the next few years. We are generating huge data every day from different domains like Social Media, Healthcare, Sensor data… we have a great tool to analyze them and the tool is R. R programming is a powerful language used widely for data analysis and statistical computing. It is completely free and has rich repositories for packages.

In this course first, you will learn how to install R and start programming on it. It will also help you to know the programming structures and functions. This R programming in Data Science and Data Analytics covers all the steps of Exploratory data analysis, Data pre-processing, and Modelling process. In EDA sections you will learn how to import data sets and create data frames from it. Then it will help you to visualize the variables using different plots. It will give you an initial structure of your data points. In Data pre-processing sections you will get the full idea of Missing value & outliers treatment and data split methods. Finally, you will be able to generate machine learning models using Linear and Logistic Regression.

This R programming for data science and data analytics is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science!
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Tuesday, July 04, 2023 , ,
Franz Kronthaler ... 367 pages - Language: English - Publisher: ‎Springer; (November, 2022) - ISBN-10: 3662643189 - ISBN-13: 978-3662643181.

This book shows you how to analyze data sets systematically and to use Excel 2019 to extract information from data almost effortlessly. Both are (not) an art! The statistical methods are presented and discussed using a single data set. This makes it clear how the methods build on each other and gradually more and more information can be extracted from the data. The Excel functions used are explained in detail - the procedure can be easily transferred to other data sets. 

Various didactic elements facilitate orientation and working with the book: At the checkpoints, the most important aspects from each chapter are briefly summarized. In the freak knowledge section, more advanced aspects are addressed to whet the appetite for more. All examples are calculated with hand and Excel. Numerous applications and solutions as well as further data sets are available on the author's internet platform.
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Sunday, May 22, 2022 , , ,
Richard W. Hamming ... Publisher: ‎Dover Publications; (November, 2004) - Language:‎ English - 880 pages - ISBN-10:‎ 0486439453 - ISBN-13:‎ 978-0486439457.

Understanding calculus is vital to the creative applications of mathematics in numerous areas. This text focuses on the most widely used applications of mathematical methods, including those related to other important fields such as probability and statistics. The four-part treatment begins with algebra and analytic geometry and proceeds to an exploration of the calculus of algebraic functions and transcendental functions and applications. In addition to three helpful appendixes, the text features answers to some of the exercises. Appropriate for advanced undergraduates and graduate students, it is also a practical reference for professionals. 1985 edition. 310 figures. 18 tables.
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Saturday, April 23, 2022 , ,
Ann Lehman, Norm O'Rourke, Larry Hatcher, Edward J. Stepanski ... 481 pages - Language: ‎ English - Publisher: ‎ SAS Institute; (February, 2005) - ISBN-10: ‎1590475763 - ISBN-13: 978-1590475768.

Doing statistics in JMP has never been easier! Learn how to manage JMP data and perform the statistical analyses most commonly used in research in the social sciences and other fields with JMP for Basic Univariate and Multivariate Statistics: A Step-by-Step Guide, authored by Ann Lehman, Norm O'Rourke, Larry Hatcher, and Edward Stepanski. Clearly written instructions guide you through the basic concepts of research and data analysis, enabling you to easily perform statistical analyses and solve problems in real-world research. Step by step, you'll discover how to obtain descriptive and inferential statistics, summarize results, perform a wide range of JMP analyses, interpret the results, and more. Topics include: screening data for errors and selecting subsets with the JMP Distribution platform, computing the coefficient alpha reliability index (Cronbach's alpha) for a multiple-item scale, performing bivariate analyses for all types of variables, performing a one-way analysis of variance (ANOVA), performing a multiple regression, and using the JMP Fit Model platform to perform a one-way multivariate analysis of variance (MANOVA). This user-friendly book introduces researchers and students of the social sciences to JMP and to elementary statistical procedures, while more advanced statistical procedures that are presented make it an invaluable reference guide for experienced researchers as well.
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Saturday, April 02, 2022 , , ,
Randall Pruim ... 820 pages - Publisher: American Mathematical Society; 2nd Edition (April, 2018) - Language: English - ISBN-10: ‎1470428482 - ISBN-13: 978-1470428488.

Foundations and Applications of Statistics simultaneously emphasizes both the foundational and the computational aspects of modern statistics. Engaging and accessible, this book is useful to undergraduate students with a wide range of backgrounds and career goals. The exposition immediately begins with statistics, presenting concepts and results from probability along the way. Hypothesis testing is introduced very early, and the motivation for several probability distributions comes from p-value computations. Pruim develops the students' practical statistical reasoning through explicit examples and through numerical and graphical summaries of data that allow intuitive inferences before introducing the formal machinery. The topics have been selected to reflect the current practice in statistics, where computation is an indispensible tool. In this vein, the statistical computing environment $\textsf{R}$ is used throughout the text and is integral to the exposition. Attention is paid to developing students' mathematical and computational skills as well as their statistical reasoning. Linear models, such as regression and ANOVA, are treated with explicit reference to the underlying linear algebra, which is motivated geometrically. Foundations and Applications of Statistics discusses both the mathematical theory underlying statistics and practical applications that make it a powerful tool across disciplines. The book contains ample material for a two-semester course in undergraduate probability and statistics. A one-semester course based on the book will cover hypothesis testing and confidence intervals for the most common situations.
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Friday, March 25, 2022 , , ,
Subal C. Kumbhakar, Hung-Jen Wang, Alan P. Horncastle ... 374 pages - Language: ‎ English - Publisher: ‎ Cambridge Univ. Press; (January, 2015) - ISBN-10: ‎ 1107609461 - ISBN-13: 978-1107609464.

Stochastic Frontier Analysis Using Stata provides practitioners in academia and industry with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach. The authors explain in detail how to estimate production, cost, and profit efficiency and introduce the basic theory of each model in an accessible way, using empirical examples that demonstrate the interpretation and application of models. This book also provides computer code, allowing users to apply the models in their own work, and incorporates the most recent stochastic frontier models developed in academic literature. Such recent developments include models of heteroscedasticity and exogenous determinants of inefficiency, scaling models, panel models with time-varying inefficiency, growth models, and panel models that separate firm effects and persistent and transient inefficiency. Immensely helpful to applied researchers, this book bridges the chasm between theory and practice, expanding the range of applications in which production frontier analysis may be implemented.
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Friday, March 11, 2022 , , ,
MathWorks MATLAB 2022a v9.12.0.1884302  [Size: 20.8 GB] ... MATLAB is a highlevel language and interactive environment that is used by millions of engineers and scientists around the world. It allows you to explore and visualize ideas and collaborate in various disciplines, including signal and image processing, communications, management systems and financial engineering. Whether you’re analyzing data, developing algorithms, or creating models, MATLAB is designed for the way you think and the work you do. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. 

Features of Mathworks Matlab: Perform a variety of complex mathematical calculations and heavy + Development environment for managing code, files, and data + Explore ways to achieve this solution + A variety of mathematical functions for linear algebra, statistics, Fourier analysis, optimization, filtering, numerical integration and ... + Drawing two-dimensional and three-dimensional graphics functions for visualizing data as + Design and construction of user interfaces under the programming languages ​​C ++, C or Java + Ability to test and measure the exact functions and graphs + The possibility of communication signal processing, image and video + There are various Jbhabzarhay engineering companies for specific applications such as + Telecommunications, control, fuzzy, estimates, statistics, data collection, simulation systems.
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Saturday, February 05, 2022 , , ,
Tonny J. Oyana ... 354 pages - Language: ‎English - Publisher: CRC Press; 2nd edition (September, 2020) - ISBN-10: 0367860856 - ISBN-13:‎ 978-0367860851.

In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R. The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.

New in the Second Edition: Includes new practical exercises and worked-out examples using R + Presents a wide range of hands-on spatial analysis worktables and lab exercises + All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences + Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods + Explains big data, data management, and data mining
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Saturday, January 29, 2022 , ,
Frederick J. Gravetter, Larry B. Wallnau, Lori-Ann B. Forzano, James E. Witnauer ... 608 pages - Language: English - Publisher:‎ Cengage Learning; 10th edition (January, 2020) - ISBN-10: 0357365291 - ISBN-13: ‎978-0357365298.

Updated with the latest research, Gravetter/Wallnau/Forzano/Witnauer's Essentials of Statistics for the Behavioral Sciences, 10th Edition delivers straightforward instruction, hands-on learning tools and real-world examples to help you go beyond memorizing formulas to truly understanding the hows and whys of statistics. Giving extra focus to the topics students typically struggle with most, the authors take time to fully explain statistical concepts. Integrated applications reinforce concepts, offering further support to ensure that even those with a weak background in mathematics can fully grasp statistics. The authors also illustrate how an understanding of statistical procedures will help you comprehend published findings -- ultimately leading you to become a savvy consumer of information. Also available, the MindTap digital learning solution helps you learn on your own terms.
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Saturday, January 15, 2022 , ,
John Sall, Mia L. Stephens, Ann Lehman, Sheila Loring ... 1017 pages - Language: English - Publisher: SAS Institute; 6th edition (February, 2017).

This book provides hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP. Each chapter features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises.Updated for JMP 13, JMP Start Statistics, Sixth Edition includes many new features, including: The redesigned Formula Editor. + New and improved ways to create formulas in JMP directly from the data table or dialogs. + Interface updates, including improved menu layout. + Updates and enhancements in many analysis platforms. + New ways to get data into JMP and to save and share JMP results. + Many new features that make it easier to use JMP.
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Thursday, January 13, 2022 , , ,
James Ramsay, Giles Hooker, Spencer Graves ... 202 pages - Publisher: Springer; (April, 2010) - Language: English - ISBN-10: 0387981845 - ISBN-13: 978-0387981840.

The book provides an application-oriented overview of functional analysis, with extended and accessible presentations of key concepts such as spline basis functions, data smoothing, curve registration, functional linear models and dynamic systems. Functional data analysis is put to work in a wide a range of applications, so that new problems are likely to find close analogues in this book. The code in R and Matlab in the book has been designed to permit easy modification to adapt to new data structures and research problems.

From the BackCover: Scientists often collect samples of curves and other functional observations, and develop models where parameters are also functions. This volume in the UseR! Series is aimed at a wide range of readers, and especially those who would like apply these techniques to their research problems.  It complements Functional Data Analysis, Second Edition and Applied Functional Data Analysis: Methods and Case Studies by providing computer code in both the R and Matlab languages for a set of data analyses that showcase functional data analysis techniques. The authors make it easy to get up and running in new applications by adapting the code for the examples, and by being able to access the details of key functions within these pages. This book is accompanied by additional web-based support at http://www.functionaldata.org for applying existing functions and developing new ones in either language. The companion 'fda' package for R includes script files to reproduce nearly all the examples in the book including all but one of the 76 figures.
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Friday, November 05, 2021 , , ,
Glyn Davis, Branko Pecar.... Publication date: ‎(January, 2021) - Language: English.

Statistics for Business Students (ISBN: 978-1-63795-762-2) brings the teaching of statistics into the 21st century. Rather than focusing on mathematics behind formulae, this textbook uses two most used platforms to teach the subject, and these are Microsoft Excel and IBM SPSS. The emphasis is on applications, understanding what the functions deliver, and how to interpret the results.

A brief content of this 756-page book is: Preface + Chapter 1 Data Visualisation + Chapter 2 Descriptive statistics + Chapter 3 Probability distributions + Chapter 4 Sampling distributions + Chapter 5 Point and interval estimates + Chapter 6 Hypothesis testing + Chapter 7 Parametric hypothesis tests + Chapter 8 Nonparametric tests + Chapter 9 Linear correlation and regression analysis + Chapter 10 Introduction to time series data, long term forecasts and seasonality + Chapter 11 Short and medium-term forecasts + Appendices + Index.
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Saturday, July 24, 2021 , , , ,
Lawrence S. Meyers, Glenn C. Gamst, A. J. Guarino ... 736 pages - Publisher: Wiley; (August, 2013) ... Language: English - ISBN-10: 1118357019 - ISBN-13: 978-1118357019.

Features easy-to-follow insight and clear guidelines to perform data analysis using IBM SPSS: Performing Data Analysis Using IBM SPSS® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming, and step-by-step instructions for all analyses are provided in addition to IBM SPSS point-and-click methods, including details on how to view and manipulate output. Designed as a user’s guide for students and other interested readers to perform statistical data analysis with IBM SPSS, this book addresses the needs, level of sophistication, and interest in introductory statistical methodology on the part of readers in social and behavioral science, business, health-related, and education programs. Each chapter of Performing Data Analysis Using IBM SPSS covers a particular statistical procedure and offers the following: an example problem or analysis goal, together with a data set; IBM SPSS analysis with step-by-step analysis setup and accompanying screen shots; and IBM SPSS output with screen shots and narrative on how to read or interpret the results of the analysis. The book provides in-depth chapter coverage of: IBM SPSS statistical output - Descriptive statistics procedures - Score distribution assumption evaluations - Bivariate correlation - Regressing (predicting) quantitative and categorical variables -Survival analysis -t Test - ANOVA and ANCOVA - Multivariate group differences - Multidimensional scaling - Cluster analysis - Nonparametric procedures for frequency data. Performing Data Analysis Using IBM SPSS is an excellent text for upper-undergraduate and graduate-level students in courses on social, behavioral, and health sciences as well as secondary education, research design, and statistics. Also an excellent reference, the book is ideal for professionals and researchers in the social, behavioral, and health sciences; applied statisticians; and practitioners working in industry.
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Saturday, February 27, 2021 ,
Riccardo Russo ... 330 pages - Publisher: Routledge; 2nd edition (November, 2020) - Language: English - ASIN: B08QV74121.

This accessible textbook is for those without a mathematical background (just some notions of basic algebra are sufficient) and provides a comprehensive introduction to all topics covered in introductory behavioural science statistics courses. It includes plenty of real examples to demonstrate approaches in depth based on real psychology experiments utilizing the statistical techniques described. New content in this thoroughly updated second edition includes an introduction to Bayesian statistics which complements the coverage of Classical/Frequentist statistics present in the first edition. It also offers practical details on how to perform analyses using JASP – a globally employed, freely downloadable statistical package. The updated eResources also feature a range of new material including additional exercises so readers can test themselves on what they have learned in the book.

This timely and highly readable text will be invaluable to undergraduate students of psychology and research methods courses in related disciplines, as well as anyone with an interest in understanding and applying the basic concepts and inferential techniques associated with statistics in the behavioural sciences.
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Sunday, February 21, 2021 , ,
Mauro da Cunha Naghettini ... 660 pages - Publisher: Springer; (October, 2016) ... Language: English - ISBN-10: 3319435604 - ISBN-13: 978-3319435602.

This textbook covers the main applications of statistical methods in hydrology. It is written for upper undergraduate and graduate students but can be used as a helpful guide for hydrologists, geographers, meteorologists and engineers. The book is very useful for teaching, as it covers the main topics of the subject and contains many worked out examples and proposed exercises. Starting from simple notions of the essential graphical examination of hydrological data, the book gives a complete account of the role that probability considerations must play during modelling, diagnosis of model fit, prediction and evaluating the uncertainty in model predictions, including the essence of Bayesian application in hydrology and statistical methods under nonstationarity. The book also offers a comprehensive and useful discussion on subjective topics, such as the selection of probability distributions suitable for hydrological variables. On a practical level, it explains MS Excel charting and computing capabilities, demonstrates the use of Winbugs free software to solve Monte Carlo Markov Chain (MCMC) simulations, and gives examples of free R code to solve nonstationary models with nonlinear link functions with climate covariates.
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