John Fox – författare
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Anthropologist John Fox sets off on a worldwide adventure to thefarthest reaches of the globe and the deepest recesses of our ancientpast to answer a question inspired by his sports-loving son:
"Why do we play ball?"
From Mexican jungles to the small-town gridirons of Ohio, frommedieval villages and royal courts to modern soccer pitches andbaseball parks, The Ball explores the little-known origins ofour favorite sports across the centuries, and traces how a simpleinvention like the ball has come to stake an unrivaled claim on ourpassions, our money, and our lives. Equal parts history and travelogue,The Ball removes us from the scandals and commercialism of today''ssports world to uncover the true reasons we play ball, helping us reclaimour universal connection to the games we love.
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Nonparametric Simple Regression
Smoothing Scatterplots
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While regression analysis traces the dependence of the distribution of a response variable to see if it bears a particular (linear) relationship to one or more of the predictors, nonparametric regression analysis makes minimal assumptions about the form of relationship between the average response and the predictors. This makes nonparametric regression a more useful technique for analyzing data in which there are several predictors that may combine additively to influence the response. (An example could be something like birth order/gender/and temperament on achievement motivation).
Unfortunately, researchers have not had accessible information on nonparametric regression analysis, until now. Beginning with presentation of nonparametric regression based on dividing the data into bins and averaging the response values in each bin, Fox introduces readers to the techniques of kernel estimation, additive nonparametric regression, and the ways nonparametric regression can be employed to select transformations of the data preceding a linear least-squares fit. The book concludes with ways nonparametric regression can be generalized to logit, probit, and Poisson regression.
Multiple and Generalized Nonparametric Regression
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This book builds on John Fox's previous volume in the QASS Series, Non Parametric Simple Regression. In this monograph readers learn to estimate and plot smooth functions when there are multiple independent variables. While regression analysis traces the dependence of the distribution of a response variable to see if it bears a particular (linear) relationship to one or more of the predictors, nonparametric regression analysis makes minimal assumptions about the form of relationship between the average response and the predictors. This makes nonparametric regression a more useful technique for analyzing data in which there are several predictors that may combine additively to influence the response. (An example could be something like birth order/gender/and temperament on achievement motivation).
Unfortunately, researchers have not had accessible information on nonparametric regression analysis, until now. Beginning with presentation of nonparametric regression based on dividing the data into bins and averaging the response values in each bin, Fox introduces readers to the techniques of kernel estimation, additive nonparametric regression, and the ways nonparametric regression can be employed to select transformations of the data preceding a linear least-squares fit. The book concludes with ways nonparametric regression can be generalized to logit, probit, and Poisson regression.
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