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  • Quantile Regression
    Quantile Regression

    Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models.Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences.Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao and Naiman show, in their application of quantile regression to empirical research, how this model yields a more complete understanding of inequality.Inequality is a perennial concern in the social sciences, and recently there has been much research in health inequality as well.Major software packages have also gradually implemented quantile regression.Quantile Regression will be of interest not only to the traditional social science market but other markets such as the health and public health related disciplines. Key Features:Establishes a natural link between quantile regression and inequality studies in the social sciences Contains clearly defined terms, simplified empirical equations, illustrative graphs, empirical tables and graphs from examples Includes computational codes using statistical software popular among social scientists Oriented to empirical research

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  • Regression and Machine Learning for Education Sciences Using R
    Regression and Machine Learning for Education Sciences Using R

    This book provides a conceptual introduction to regression analysis and machine learning and their applications in education research.It discusses their diverse applications, including its role in predicting future events based on the current data or explaining why some phenomena occur.These identified important predictors provide data-based evidence for educational and psychological decision-making. Offering an applications-oriented approach while mapping out fundamental methodological developments, this book lays a sound foundation for understanding essential regression and machine learning concepts for data analytics.The first part of the book discusses regression analysis and provides a sturdy foundation to understand the logic of machine learning.With each chapter, the discussion and development of each statistical concept and data analytical technique is presented from an applied perspective, with the statistical results providing insights into decisions and solutions to problems using R.Based on practical examples, and written in a concise and accessible style, the book is learner-centric and does a remarkable job in breaking down complex concepts. Regression and Machine Learning for Education Sciences Using R is primarily for students or practitioners in education and psychology, although individuals from other related disciplines can also find the book beneficial.The dataset and examples used in the book are from an educational setting, and students will find that this text provides a good preparation ground for studying more statistical and data analytical materials.

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  • Spatial Regression Models
    Spatial Regression Models

    Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis.The text covers different modeling-related topics for continuous dependent variables, including mapping data on spatial units, creating data from maps, analyzing exploratory spatial data, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures. Using social science examples based on real data, the authors illustrate the concepts discussed, and show how to obtain and interpret relevant results.The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing.Users can download both the data and computer code to work through all the examples found in the text.New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.

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  • Applied Logistic Regression
    Applied Logistic Regression

    A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software.The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models.New and updated features include: A chapter on the analysis of correlated outcome dataA wealth of additional material for topics ranging from Bayesian methods to assessing model fitRich data sets from real-world studies that demonstrate each method under discussionDetailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.

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  • What is an exponential regression?

    An exponential regression is a type of statistical analysis used to model and predict data that exhibits exponential growth or decay. It involves fitting an exponential function to a set of data points in order to find the best-fitting curve that describes the relationship between the independent and dependent variables. This type of regression is commonly used in fields such as finance, biology, and physics to analyze trends and make predictions about future outcomes based on the exponential nature of the data.

  • What is a mathematical regression?

    A mathematical regression is a statistical method used to analyze the relationship between two or more variables. It is used to predict the value of one variable based on the value of one or more other variables. The most common type of regression is linear regression, which assumes a linear relationship between the variables. Other types of regression include polynomial regression, logistic regression, and multiple regression, which can handle more complex relationships between variables. Regression analysis is widely used in various fields such as economics, finance, biology, and social sciences to make predictions and understand the relationships between variables.

  • What is a regression curve?

    A regression curve is a graphical representation of the relationship between two variables in a regression analysis. It shows the predicted values of the dependent variable based on the values of the independent variable(s). The curve is fitted to the data points in such a way that it minimizes the differences between the observed values and the predicted values. Regression curves can be linear, quadratic, exponential, or of other forms, depending on the nature of the relationship between the variables being studied.

  • What regression models are there?

    There are several types of regression models, including linear regression, logistic regression, polynomial regression, ridge regression, lasso regression, and support vector regression. Each type of regression model is used for different types of data and has its own assumptions and characteristics. Linear regression is commonly used for predicting a continuous outcome, logistic regression is used for binary classification problems, and polynomial regression is used when the relationship between the independent and dependent variables is non-linear. Ridge and lasso regression are used for regularization to prevent overfitting, while support vector regression is used for handling non-linear relationships between variables.

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  • Linear Regression Analysis
    Linear Regression Analysis

    Concise, mathematically clear, and comprehensive treatment of the subject. * Expanded coverage of diagnostics and methods of model fitting. * Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models. * More than 200 problems throughout the book plus outline solutions for the exercises. * This revision has been extensively class-tested.

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  • Regression Inside Out
    Regression Inside Out

    Linear regression analysis, with its many generalizations, is the predominant quantitative method used throughout the social sciences and beyond.The goal of the method is to study relations among variables.In this book, Schoon, Melamed and Breiger turn regression modeling inside out to put the emphasis on the cases (people, organizations, and nations) that comprise the variables.By re-analyzing influential published research, they reveal new insights and present a principled way to unlock a set of more nuanced interpretations than has previously been attainable.The emphasis is on intuition and examples that can be reproduced using the code and datasets provided.Relating their contributions to methodologies that operate under quite different philosophical assumptions, the authors advance multi-method social science and help to bridge the divide between quantitative and qualitative research.The result is a modern, accessible, and innovative take on extracting knowledge from data.

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  • Past Life Regression
    Past Life Regression

    Jason Quever has been releasing timeless guitar-based dream pop as Papercuts since 2004, impervious to trends or micro genres that have come and gone around him. In that regard, his contemporaries are artists like Hiss Golden Messenger, Fruit Bats, Andy Shauf or Kings of Convenience - artists who are more concerned with song craft and perfecting their sound, and less concerned with gimmicks or fitting into a specific scene. 'Past Life Regression' is his 2022 album and it's a journey into the dreamier reaches of psychedelic folk-pop that digs deep into influences as wide-ranging as The west coast pop art experimental band, Spiritualized, Echo & The Bunnymen, Leonard Cohen and late '60s pop of various flavours.

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  • Regression Analysis in Medical Research : for Starters and 2nd Levelers
    Regression Analysis in Medical Research : for Starters and 2nd Levelers

    This edition is a pretty complete textbook and tutorial for medical and health care students, as well as a recollection/update bench, and help desk for professionals.Novel approaches already applied in published clinical research will be addressed: matrix analyses, alpha spending, gate keeping, kriging, interval censored regressions, causality regressions, canonical regressions, quasi-likelihood regressions, novel non-parametric regressions.Each chapter can be studied as a stand-alone, and covers one field in the fast growing world of regression analyses. The authors, as professors in statistics and machine learning at European universities, are worried, that their students find regression-analyses harder than any other methodology in statistics.This is serious, because almost all of the novel methodologies in current data mining and data analysis include elements of regression-analysis.It is the main incentive for writing this 28 chapter edition, consistent of - 28 major fields of regression analysis, - their condensed maths, - their applications in medical and health research as published so far, - step by step analyses for self-assessment, - conclusion and reference sections. Traditional regression analysis is adequate for epidemiology, but lacks the precision required for clinical investigations.However, in the past two decades modern regression methods have proven to be much more precise. And so it is time, that a book described regression analyses for clinicians.The current edition is the first to do so. It is written for a non-mathematical readership. Self-assessment data-files are provided through Springer' s "Extras Online".

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  • What is a sleep regression?

    A sleep regression is a period of time when a baby or young child who has been sleeping well suddenly has trouble sleeping. This can happen around certain developmental milestones, such as learning to crawl or walk, or during times of illness or teething. During a sleep regression, a child may have trouble falling asleep, staying asleep, or waking frequently during the night. It can be a challenging time for both the child and the parents, but it is usually temporary and resolves on its own.

  • Is the influence of a variable in multiple regression more significant than in simple regression?

    In multiple regression, the influence of a variable is typically more significant than in simple regression because multiple regression takes into account the effects of multiple independent variables on the dependent variable, while simple regression only considers the relationship between one independent variable and the dependent variable. This means that in multiple regression, the influence of a variable is assessed while controlling for the effects of other variables, providing a more comprehensive understanding of its impact. Additionally, multiple regression can help identify the unique contribution of each variable to the dependent variable, which can be especially useful in complex real-world scenarios.

  • What is inference in linear regression?

    Inference in linear regression refers to the process of drawing conclusions about the relationships between variables based on the estimated coefficients of the regression model. It involves testing hypotheses about the significance of these coefficients and making predictions about the dependent variable. Inference helps us understand the strength and direction of the relationships between the independent and dependent variables, as well as the overall fit of the model to the data. It is an important aspect of linear regression analysis that allows us to make informed decisions and interpretations based on the statistical results.

  • Is regression nonsense or really possible?

    Regression is a real phenomenon that occurs in statistics and can be observed in various fields such as psychology, economics, and biology. It refers to the tendency for extreme or unusual data points to move closer to the average over time. This can be due to a variety of factors such as measurement error, random chance, or natural fluctuations in a system. While regression is a real and observable phenomenon, it is important to carefully consider the context and potential causes before drawing conclusions about the data.

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