Download or read book Learning Regression Analysis by Simulation written by Kunio Takezawa and published by Springer Science & Business Media. This book was released on 2013-10-08 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: The standard approach of most introductory books for practical statistics is that readers first learn the minimum mathematical basics of statistics and rudimentary concepts of statistical methodology. They then are given examples of analyses of data obtained from natural and social phenomena so that they can grasp practical definitions of statistical methods. Finally they go on to acquaint themselves with statistical software for the PC and analyze similar data to expand and deepen their understanding of statistical methods. This book, however, takes a slightly different approach, using simulation data instead of actual data to illustrate the functions of statistical methods. Also, R programs listed in the book help readers realize clearly how these methods work to bring intrinsic values of data to the surface. R is free software enabling users to handle vectors, matrices, data frames, and so on. For example, when a statistical theory indicates that an event happens with a 5 % probability, readers can confirm the fact using R programs that this event actually occurs with roughly that probability, by handling data generated by pseudo-random numbers. Simulation gives readers populations with known backgrounds and the nature of the population can be adjusted easily. This feature of the simulation data helps provide a clear picture of statistical methods painlessly. Most readers of introductory books of statistics for practical purposes do not like complex mathematical formulae, but they do not mind using a PC to produce various numbers and graphs by handling a huge variety of numbers. If they know the characteristics of these numbers beforehand, they treat them with ease. Struggling with actual data should come later. Conventional books on this topic frighten readers by presenting unidentified data to them indiscriminately. This book provides a new path to statistical concepts and practical skills in a readily accessible manner.
Download or read book Applied Regression Modeling written by Iain Pardoe and published by John Wiley & Sons. This book was released on 2020-12-03 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the fundamentals of regression without learning calculus with this one-stop resource The newly and thoroughly revised 3rd Edition of Applied Regression Modeling delivers a concise but comprehensive treatment of the application of statistical regression analysis for those with little or no background in calculus. Accomplished instructor and author Dr. Iain Pardoe has reworked many of the more challenging topics, included learning outcomes and additional end-of-chapter exercises, and added coverage of several brand-new topics including multiple linear regression using matrices. The methods described in the text are clearly illustrated with multi-format datasets available on the book's supplementary website. In addition to a fulsome explanation of foundational regression techniques, the book introduces modeling extensions that illustrate advanced regression strategies, including model building, logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series forecasting. Illustrations, graphs, and computer software output appear throughout the book to assist readers in understanding and retaining the more complex content. Applied Regression Modeling covers a wide variety of topics, like: Simple linear regression models, including the least squares criterion, how to evaluate model fit, and estimation/prediction Multiple linear regression, including testing regression parameters, checking model assumptions graphically, and testing model assumptions numerically Regression model building, including predictor and response variable transformations, qualitative predictors, and regression pitfalls Three fully described case studies, including one each on home prices, vehicle fuel efficiency, and pharmaceutical patches Perfect for students of any undergraduate statistics course in which regression analysis is a main focus, Applied Regression Modeling also belongs on the bookshelves of non-statistics graduate students, including MBAs, and for students of vocational, professional, and applied courses like data science and machine learning.
Download or read book Statistical Inference via Data Science A ModernDive into R and the Tidyverse written by Chester Ismay and published by CRC Press. This book was released on 2019-12-23 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Inference via Data Science: A ModernDive into R and the Tidyverse provides a pathway for learning about statistical inference using data science tools widely used in industry, academia, and government. It introduces the tidyverse suite of R packages, including the ggplot2 package for data visualization, and the dplyr package for data wrangling. After equipping readers with just enough of these data science tools to perform effective exploratory data analyses, the book covers traditional introductory statistics topics like confidence intervals, hypothesis testing, and multiple regression modeling, while focusing on visualization throughout. Features: ● Assumes minimal prerequisites, notably, no prior calculus nor coding experience ● Motivates theory using real-world data, including all domestic flights leaving New York City in 2013, the Gapminder project, and the data journalism website, FiveThirtyEight.com ● Centers on simulation-based approaches to statistical inference rather than mathematical formulas ● Uses the infer package for "tidy" and transparent statistical inference to construct confidence intervals and conduct hypothesis tests via the bootstrap and permutation methods ● Provides all code and output embedded directly in the text; also available in the online version at moderndive.com This book is intended for individuals who would like to simultaneously start developing their data science toolbox and start learning about the inferential and modeling tools used in much of modern-day research. The book can be used in methods and data science courses and first courses in statistics, at both the undergraduate and graduate levels.
Download or read book Regression Modeling with Actuarial and Financial Applications written by Edward W. Frees and published by Cambridge University Press. This book was released on 2010 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
Download or read book Global COVID 19 Research and Modeling written by Longbing Cao and published by Springer Nature. This book was released on with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The SAGE Handbook of Regression Analysis and Causal Inference written by Henning Best and published by SAGE. This book was released on 2013-12-20 with total page 577 pages. Available in PDF, EPUB and Kindle. Book excerpt: ′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written by leaders in the field. Everyone engaged in statistical analysis of social-science data will find something of interest in this book.′ - John Fox, Professor, Department of Sociology, McMaster University ′The authors do a great job in explaining the various statistical methods in a clear and simple way - focussing on fundamental understanding, interpretation of results, and practical application - yet being precise in their exposition.′ - Ben Jann, Executive Director, Institute of Sociology, University of Bern ′Best and Wolf have put together a powerful collection, especially valuable in its separate discussions of uses for both cross-sectional and panel data analysis.′ -Tom Smith, Senior Fellow, NORC, University of Chicago Edited and written by a team of leading international social scientists, this Handbook provides a comprehensive introduction to multivariate methods. The Handbook focuses on regression analysis of cross-sectional and longitudinal data with an emphasis on causal analysis, thereby covering a large number of different techniques including selection models, complex samples, and regression discontinuities. Each Part starts with a non-mathematical introduction to the method covered in that section, giving readers a basic knowledge of the method’s logic, scope and unique features. Next, the mathematical and statistical basis of each method is presented along with advanced aspects. Using real-world data from the European Social Survey (ESS) and the Socio-Economic Panel (GSOEP), the book provides a comprehensive discussion of each method’s application, making this an ideal text for PhD students and researchers embarking on their own data analysis.
Download or read book Applied Linear Regression written by Sanford Weisberg and published by John Wiley & Sons. This book was released on 2013-06-07 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: "I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression." —Technometrics, February 1987 "Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis." —American Scientist, May–June 1987 Applied Linear Regression, Third Edition has been thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, the Third Edition stresses the use of graphical methods in an effort to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results. The Third Edition incorporates new material reflecting the latest advances, including: Use of smoothers to summarize a scatterplot Box-Cox and graphical methods for selecting transformations Use of the delta method for inference about complex combinations of parameters Computationally intensive methods and simulation, including the bootstrap method Expanded chapters on nonlinear and logistic regression Completely revised chapters on multiple regression, diagnostics, and generalizations of regression Readers will also find helpful pedagogical tools and learning aids, including: More than 100 exercises, most based on interesting real-world data Web primers demonstrating how to use standard statistical packages, including R, S-Plus®, SPSS®, SAS®, and JMP®, to work all the examples and exercises in the text A free online library for R and S-Plus that makes the methods discussed in the book easy to use With its focus on graphical methods and analysis, coupled with many practical examples and exercises, this is an excellent textbook for upper-level undergraduates and graduate students, who will quickly learn how to use linear regression analysis techniques to solve and gain insight into real-life problems.
Download or read book Regression Analysis with R written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2018-01-31 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build effective regression models in R to extract valuable insights from real data Key Features Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values From Simple Linear Regression to Logistic Regression - this book covers all regression techniques and their implementation in R A complete guide to building effective regression models in R and interpreting results from them to make valuable predictions Book Description Regression analysis is a statistical process which enables prediction of relationships between variables. The predictions are based on the casual effect of one variable upon another. Regression techniques for modeling and analyzing are employed on large set of data in order to reveal hidden relationship among the variables. This book will give you a rundown explaining what regression analysis is, explaining you the process from scratch. The first few chapters give an understanding of what the different types of learning are – supervised and unsupervised, how these learnings differ from each other. We then move to covering the supervised learning in details covering the various aspects of regression analysis. The outline of chapters are arranged in a way that gives a feel of all the steps covered in a data science process – loading the training dataset, handling missing values, EDA on the dataset, transformations and feature engineering, model building, assessing the model fitting and performance, and finally making predictions on unseen datasets. Each chapter starts with explaining the theoretical concepts and once the reader gets comfortable with the theory, we move to the practical examples to support the understanding. The practical examples are illustrated using R code including the different packages in R such as R Stats, Caret and so on. Each chapter is a mix of theory and practical examples. By the end of this book you will know all the concepts and pain-points related to regression analysis, and you will be able to implement your learning in your projects. What you will learn Get started with the journey of data science using Simple linear regression Deal with interaction, collinearity and other problems using multiple linear regression Understand diagnostics and what to do if the assumptions fail with proper analysis Load your dataset, treat missing values, and plot relationships with exploratory data analysis Develop a perfect model keeping overfitting, under-fitting, and cross-validation into consideration Deal with classification problems by applying Logistic regression Explore other regression techniques – Decision trees, Bagging, and Boosting techniques Learn by getting it all in action with the help of a real world case study. Who this book is for This book is intended for budding data scientists and data analysts who want to implement regression analysis techniques using R. If you are interested in statistics, data science, machine learning and wants to get an easy introduction to the topic, then this book is what you need! Basic understanding of statistics and math will help you to get the most out of the book. Some programming experience with R will also be helpful
Download or read book Data Analysis Using Regression and Multilevel Hierarchical Models written by Andrew Gelman and published by Cambridge University Press. This book was released on 2007 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Download or read book A Student s Guide to Bayesian Statistics written by Ben Lambert and published by SAGE. This book was released on 2018-04-20 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference Understanding Bayes′ rule Nuts and bolts of Bayesian analytic methods Computational Bayes and real-world Bayesian analysis Regression analysis and hierarchical methods This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.
Download or read book Handbook of Regression Modeling in People Analytics written by Keith McNulty and published by CRC Press. This book was released on 2021-07-30 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite the recent rapid growth in machine learning and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best ‘swiss army knife’ we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and in Python, ranging from simple hypothesis testing to advanced multivariate modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a ‘sweet spot’ where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to students and researchers. Key Features: • 16 accompanying datasets across a wide range of contexts (e.g. academic, corporate, sports, marketing) • Clear step-by-step instructions on executing the analyses. • Clear guidance on how to interpret results. • Primary instruction in R but added sections for Python coders. • Discussion exercises and data exercises for each of the main chapters. • Final chapter of practice material and datasets ideal for class homework or project work.
Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Download or read book Simulation Tools and Techniques written by José-Luis Guisado-Lizar and published by Springer Nature. This book was released on with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook Of Digital Enterprise Systems Digital Twins Simulation And Ai written by Wolfgang Kuhn and published by World Scientific. This book was released on 2019-06-04 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digitalization is changing nearly everything. This compendium highlights a comprehensive understanding of the concepts and technologies about digitalization in industrial environments, using the Industrial Internet of Things, Digital Twins and data-driven decision-making approaches including Artificial Intelligence.The overview of industrial enterprise platforms and the consideration of future trends gives a fundamental idea of concepts and strategies, how to get started and about the required changes of business models.
Download or read book Surgical Simulation written by Prokar Dasgupta and published by Anthem Press. This book was released on 2014-01-15 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now widely recognized as one of the most effective methods for training future surgeons, simulation has become an integral part of the multidimensional landscape that makes up a surgical education curriculum. This book provides an overview of the current status of simulation-based training in various surgical disciplines and explains the science of surgical education, from developing a simulation programme to properly assess surgeons-in-training, to transferring the skills acquired through simulation into real-life settings. As such, the book can be used as a guide for understanding the basics of surgical education.
Download or read book Modeling Simulation and Visual Analysis of Crowds written by Saad Ali and published by Springer Science & Business Media. This book was released on 2013-11-22 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last several years there has been a growing interest in developing computational methodologies for modeling and analyzing movements and behaviors of ‘crowds' of people. This interest spans several scientific areas that includes Computer Vision, Computer Graphics, and Pedestrian Evacuation Dynamics. Despite the fact that these different scientific fields are trying to model the same physical entity (i.e. a crowd of people), research ideas have evolved independently. As a result each discipline has developed techniques and perspectives that are characteristically their own. The goal of this book is to provide the readers a comprehensive map towards the common goal of better analyzing and synthesizing the pedestrian movement in dense, heterogeneous crowds. The book is organized into different parts that consolidate various aspects of research towards this common goal, namely the modeling, simulation, and visual analysis of crowds. Through this book, readers will see the common ideas and vision as well as the different challenges and techniques, that will stimulate novel approaches to fully grasping “crowds."
Download or read book Non Cognitive Factors and Learning within a Business Simulation written by Tanja Kreitenweis and published by Springer Nature. This book was released on 2021-10-22 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: Serious games can provide a convenient and straightforward access to complex knowledge for all age groups. However, learning achievements depend largely on learners’ non-cognitive factor disposition. With the aim of combining the fields of serious games and non-cognitive factors, this research focuses on the use of a business simulation which conveys change management insights. Business simulations are a subset of serious games and are perceived as a non-traditional learning method.The objectives of this work are versatile: (1) developing a scale, which measures learners’ knowledge and skills increase gained from a business simulation, (2) investigating the effects of non-cognitive factors on learning in this business simulation environment and (3) exploring the moderating role of team preference in this type of learning setting. Using the newly developed scale, this work finds that learners' skills and knowledge states are more pronounced after playing the business simulation.
