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Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data fr
Social sciences --- Regression analysis. --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Statistical methods.
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Regression Analysis --- 519.233 --- Regression analysis --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Parametric methods --- Regression analysis. --- 519.233 Parametric methods --- Statistique mathematique --- Regression
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Mathematical Statistics --- Mathematics --- Physical Sciences & Mathematics --- Regression analysis. --- Robust statistics. --- Statistics, Robust --- Distribution (Probability theory) --- Mathematical statistics --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling
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"This book offers a practical, concise introduction to regression analysis for upper-level undergraduate students of diverse disciplines including, but not limited to statistics, the social and behavioral sciences, MBA, and vocational studies. The book's overall approach is strongly based on an abundant use of illustrations, examples, case studies, and graphics. It emphasizes major statistical software packages, including SPSS(r), Minitab(r), SAS(r), R, and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided on a specially prepared and maintained author web site. Select software output appears throughout the text. To help readers understand, analyze, and interpret data and make informed decisions in uncertain settings, many of the examples and problems use real-life situations and settings. The book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, Bayesian modeling, and time series and forecasting. New to this edition are more exercises, simplification of tedious topics (such as checking regression assumptions and model building), elimination of repetition, and inclusion of additional topics (such as variable selection methods, further regression diagnostic tests, and autocorrelation tests)"--
Regression analysis --- Statistics --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling
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"Spatial Regression Analysis Using Eigenvector Spatial Filtering provides both theoretical foundations and guidance on practical implementation for the eigenvector spatial filtering (ESF) technique. ESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in georeferenced data analyses. With its flexible structure, ESF can be easily applied to generalized linear regression models. The book discusses ESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, and spatial interaction models. In addition, it provides a tutorial for ESF model specification and interfaces, including author developed, user-friendly software"--
Spatial analysis (Statistics) --- Regression analysis. --- Eigenvectors. --- Matrices --- Vector spaces --- Eigenfactor --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Analysis, Spatial (Statistics) --- Correlation (Statistics) --- Spatial systems
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Nonlinear theories. --- Regression analysis. --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Nonlinear problems --- Nonlinearity (Mathematics) --- Calculus --- Mathematical analysis --- Mathematical physics
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Regression analysis. --- Social sciences --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Statistical methods. --- Methods in social research (general)
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Learn the art of regression analysis with Python About This Book Become competent at implementing regression analysis in Python Solve some of the complex data science problems related to predicting outcomes Get to grips with various types of regression for effective data analysis Who This Book Is For The book targets Python developers, with a basic understanding of data science, statistics, and math, who want to learn how to do regression analysis on a dataset. It is beneficial if you have some knowledge of statistics and data science. What You Will Learn Format a dataset for regression and evaluate its performance Apply multiple linear regression to real-world problems Learn to classify training points Create an observation matrix, using different techniques of data analysis and cleaning Apply several techniques to decrease (and eventually fix) any overfitting problem Learn to scale linear models to a big dataset and deal with incremental data In Detail 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. Style and approach This is a practical tutorial-based book. You will be given an example problem and then supplied with the relevant code and how to walk through it. The details are provided in a step by step manner, followed by a thorough explanation of the math underlying the solution. This approach will help you leverage your own data using the same techniques.
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Quantitative methods (economics) --- Regression analysis. --- Economics --- Economic statistics --- Econometrics --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Statistical methods.
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