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MORE ABOUT THIS BOOK
Main description:
Regression analysis of cause effect relationships is increasingly the core of medical and health research. This work is a 2nd edition of a 2017 pretty complete textbook and tutorial for students as well as recollection / update bench and help desk for professionals.
It came to the authors' attention, that information of history, background, and purposes, of the regression methods addressed were scanty. Lacking information about all of that has now been entirely covered.
The editorial art work of the first edition, however pretty, was less appreciated by some readerships, than were the original output sheets from the statistical programs as used. Therefore, the editorial art work has now been systematically replaced with original statistical software tables and graphs for the benefit of an improved usage and understanding of the methods.
In the past few years, professionals have been flooded with big data. The Covid-19 pandemic gave cause for statistical software companies to foster novel analytic programs better accounting outliers and skewness. Novel fields of regression analysis adequate for such data, like sparse canonical regressions and quantile regressions, have been included.
Contents:
Preface
Chapter 1.
Continuous Outcome Regressions
Chapter 2.
Dichotomous Outcome Regressions
Chapter 3.
Confirmative Regressions
Chapter 4.
Dichotomous Regressions Other than Logistic and Cox
Chapter 5.
Polytomous Outcome Regressions
Chapter 6.
Time to Event Regressions other than Traditional Cox
Chapter 7.
Analysis of Variance (ANOVA)
Chapter 8.
Repeated Outcomes Regression Methods
Chapter 9.
Methodologies for Better Fit of Categorical Predictors
Chapter 10.
Laplace Regressions, Multi- instead of Mono-Exponential Models
Chapter 11.
Regressions For Making Extrapolations.
Chapter 12.
Standardized Regression Coefficients
Chapter 13.
Multivariate Analysis of Variance and Canonical Regression
Chapter 14.
More on Poisson Regressions
Chapter 15.
Regression Trend Testing
Chapter 16.
Optimal Scaling and Automatic Linear Regression
Chapter 17.
Spline Regressions
Chapter 18.
More on Nonlinear Regressions
Chapter 19.
Special Forms of Continuous Outcome Regressions
Chapter 20.
Regressions for Quantitative Diagnostic Testing
Chapter 21.
Regressions, a Panacee or at Least a Widespread Help for Data Analyses
Chapter 22.
Regression Trees
Chapter 23.
Regressions with Latent Variables
Chapter 24.
Partial Correlations
Chapter 25.
Functional Data Analysis Basis
Chapter 26.
Functional Data Analysis Advanced
Chapter 27.
Quantile Regression
Index
PRODUCT DETAILS
Publisher: Springer (Springer Nature Switzerland AG)
Publication date: March, 2022
Pages: 475
Weight: 747g
Availability: Available
Subcategories: General Issues