Products related to Regression:
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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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Past Life Regression
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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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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.
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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.
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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.
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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
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 and Other Stories
Most textbooks on regression focus on theory and the simplest of examples.Real statistical problems, however, are complex and subtle.This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference.Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques.It jumps right in to methods and computer code you can use immediately.Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies.They make a smooth transition to logistic regression and GLM.The emphasis is on computation in R and Stan rather than derivations, with code available online.Graphics and presentation aid understanding of the models and model fitting.
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Democracy and Brazil : Collapse and Regression
Democracy and Brazil: Collapse and Regression discusses the de-democratization process underway in contemporary Brazil. The relative political stability that characterized domestic politics in the 2000s ended with the sudden emergence of a series of massive protests in 2013, followed by the controversial impeachment of Dilma Rousseff in 2016 and the election of Jair Bolsonaro in 2018.In this new, more conservative period in Brazilian politics, a series of institutional reforms deepened the distance between citizens and representatives.Brazil's current political crisis cannot be understood without reference to the continual growth of right-wing and ultra-right discourse, on the one hand, and to the neoliberal ideology that pervades the minds of large parts of the Brazilian elite, on the other.Twenty experts on Brazil across different fields discuss the ongoing political turmoil in the light of distinct problems: geopolitics, gender, religion, media, indigenous populations, right-wing strategies, and new forms of coup, among others.Updated analyses enriched with historical perspective help to illuminate the intricate issues that will determine the country's fate in years to come.Democracy and Brazil: Collapse and Regression will interest students and scholars of Brazilian Politics and History, Latin America, and the broader field of democracy studies.
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Hypnotherapy Past Life Regression Diploma Course
Our online past life regression course shows you how to take clients back in time to past lives and childhood memories to clear any unwanted emotions, habits and blockages.
Price: 127 € | Shipping*: 0.00 €
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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.
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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.
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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.
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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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