Isbn: 9781412974837 - logistic regression: from introductory to advanced concepts and applications (16 resultados)

hardcover. Condición: New. Brand new gift quality hardcover in glossy boards. Please email for photos. Larger books or sets may require additional shipping charges. Books sent via US Postal.

Condición: New.

hardcover. Condición: new.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Hardback. Condición: New. In this text, author Scott Menard provides coverage of not only the basic logistic regression model but also advanced topics found in no other logistic regression text. The book keeps mathematical notation to a minimum, making it accessible to those with more limited statistics backgrounds, while including advanced topics of interest to more statistically sophisticated readers. Not dependent on any one software package, the book discusses limitations to existing software packages and ways to overcome them. Key Features    Examines the logistic regression model in detailIllustrates concepts with applied examples to help readers understand how concepts are translated into the logistic regression model Helps readers make decisions about the criteria for evaluating logistic regression models through detailed coverage of how to assess overall models and individual predictors for categorical dependent variables Offers unique coverage of path analysis with logistic regression that shows readers how to examine both direct and indirect effects using logistic regression analysis Applies logistic regression analysis to longitudinal panel data, helping students understand the issues in measuring change with dichotomous, nominal, and ordinal dependent variablesShows readers how multilevel change models with logistic regression are different from multilevel growth curve models for continuous interval or ratio-scaled dependent variablesLogistic Regression is intended for courses such as Regression and Correlation, Intermediate/Advanced Statistics, and Quantitative Methods taught in departments throughout the behavioral, health, mathematical, and social sciences, including applied mathematics/statistics, biostatistics, criminology/criminal justice, education, political science, public health/epidemiology, psychology, and sociology.…

Condición: New.

Condición: As New. Unread book in perfect condition.

Hardcover. Condición: Brand New. 416 pages. 9.25x7.50x1.00 inches. In Stock.

Hardback. Condición: New. In this text, author Scott Menard provides coverage of not only the basic logistic regression model but also advanced topics found in no other logistic regression text. The book keeps mathematical notation to a minimum, making it accessible to those with more limited statistics backgrounds, while including advanced topics of interest to more statistically sophisticated readers. Not dependent on any one software package, the book discusses limitations to existing software packages and ways to overcome them. Key Features    Examines the logistic regression model in detailIllustrates concepts with applied examples to help readers understand how concepts are translated into the logistic regression model Helps readers make decisions about the criteria for evaluating logistic regression models through detailed coverage of how to assess overall models and individual predictors for categorical dependent variables Offers unique coverage of path analysis with logistic regression that shows readers how to examine both direct and indirect effects using logistic regression analysis Applies logistic regression analysis to longitudinal panel data, helping students understand the issues in measuring change with dichotomous, nominal, and ordinal dependent variablesShows readers how multilevel change models with logistic regression are different from multilevel growth curve models for continuous interval or ratio-scaled dependent variablesLogistic Regression is intended for courses such as Regression and Correlation, Intermediate/Advanced Statistics, and Quantitative Methods taught in departments throughout the behavioral, health, mathematical, and social sciences, including applied mathematics/statistics, biostatistics, criminology/criminal justice, education, political science, public health/epidemiology, psychology, and sociology.…

Condición: New. Print on Demand pp. 392.

Condición: New. Print on Demand pp. 392.

Condición: New. PRINT ON DEMAND pp. 392.

Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Logistic Regression is designed for readers who have a background in statistics at least up to multiple linear regression, who want to analyze dichotomous, nominal, and ordinal dependent variables cross-sectionally and longitudinally.&Uumlber den A.…

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Logistic Regression is designed for readers who have a background in statistics at least up to multiple linear regression, who want to analyze dichotomous, nominal, and ordinal dependent variables cross-sectionally and longitudinally. The book begins by showing how logistic regression combines aspects of multiple linear regression and loglinear analysis to overcome problems both techniques have with the analysis of dichotomous dependent variables with continuous predictors. The logistic regression model is then examined in detail, including how to evaluate the overall model and how to evaluate the impact of the different predictors in the model for different types of research questions. Unique to this book is the extensive consideration qualitative (prediction tables) as well as quantitative indices of how well the model predicts the dependent variable. The book then examines what can go wrong with the model and how to detect and correct it; the use of logistic regression in path analysis; nominal and ordinal dependent variables; modifications to the logistic regression model when the cases are not completely independent of one another; the use of logistic regression models for longitudinal data with few and with many repeated measurements; and alternatives to logistic regression.…