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Mathematics --- Mathematics --- Mathematics --- Statistics --- Statistics --- Statistics --- Uncertainty --- Uncertainty --- Uncertainty --- QA 273 Probabilities --- QA 273 Probabilities --- QA 273 Probabilities

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Statistics --- Computer Graphics --- Social sciences --- QA 276.4 Mathematical statistics. Data processing --- 681.3*G --- Mathematics of computing --- 681.3*G Mathematics of computing --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Statistics&delete& --- Graphic methods&delete& --- Computer programs --- Stata. --- Sciences sociales --- Graphic methods --- Computer programs. --- Méthodes graphiques --- Statistiques. --- Logigrammes --- Méthodes statistiques.

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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 analysisuntil 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.

Mathematical statistics --- QA 278.2 .H67 2000 Regression analysis. Correlation analysis --- Regression analysis --- Nonparametric statistics --- #SBIB:303H10 --- #PBIB:2003.3 --- Analysis, Regression --- Linear regression --- Regression modeling --- Multivariate analysis --- Structural equation modeling --- Distribution-free statistics --- Statistics, Distribution-free --- Statistics, Nonparametric --- Methoden en technieken: algemene handboeken en reeksen --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Nonparametric statistics. --- Regression analysis. --- Regression Analysis

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This new edition of this classic incorporates the most recent thinking on methodology and software, as well as the latest literature on multilevel statistical models. Topics covered by multilevel models have increased in recent years, and the methods are widely applied in the social sciences as well as in areas such as epidemiology, geography, education, surveys, and medicine. This third edition includes chapters on meta analysis, factor analysis and structural equation models, and has expanded sections on MCMC methods.

Mathematical statistics --- Social sciences --- Educational tests and measurements --- Mathematical models --- Research --- Methodology --- 519.2 --- #PBIB:2003.3 --- Probability. Mathematical statistics --- Mathematical models. --- Methodology. --- 519.2 Probability. Mathematical statistics --- Behavioral sciences --- Human sciences --- Sciences, Social --- Social science --- Social studies --- Civilization --- Research&delete& --- Social sciences - Mathematical models --- Social sciences - Research - Methodology --- Educational tests and measurements - Mathematical models --- H61.25 Mathematics. Mathematical models --- Methods

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This book is aimed at researchers, practitioners and graduate students who have some exposure to traditional methods of survival analysis. The emphasis is on semiparametric methods based on the proportional hazards model. The inclusion of examples with SAS and S-PLUS code will make the book accessible to most working statisticians.

Biomathematics. Biometry. Biostatistics --- Medicine --- Survival analysis (Biometry) --- Médecine --- Analyse de la survie (Biométrie) --- Research --- Statistical methods --- Mathematical models --- Recherche --- Méthodes statistiques --- Modèles mathématiques --- Mathematical models. --- Statistical methods. --- Survival analysis (Biometry). --- Médecine --- Analyse de la survie (Biométrie) --- Méthodes statistiques --- Modèles mathématiques --- Probabilities. --- Statistics . --- Probability Theory and Stochastic Processes. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Mathematical Modeling and Industrial Mathematics. --- Models, Mathematical --- Simulation methods --- Statistical analysis --- Statistical data --- Statistical science --- Mathematics --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Statistics. --- Medicine - Research - Statistical methods --- Medicine - Mathematical models --- Survival Analysis --- Analysis, Survival --- Analyses, Survival --- Survival Analyses --- Analysis, Survival (Biometry) --- Survivorship analysis (Biometry) --- Biometry --- Failure time data analysis --- Health Workforce --- Research&delete& --- AA / International- internationaal --- 303.0 --- 306.5 --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken) --- Statistische analyse (methodologie) --- WA 950 Theory or methods of medical statistics