Analysis of variance, design, and regression : linear modeling for unbalanced data

Author(s)

• Christensen, Ronald

Bibliographic Information

Analysis of variance, design, and regression : linear modeling for unbalanced data

Ronald Christensen

（Texts in statistical science）

CRC Press, Taylor & Francis Group, c2016

2nd ed

• : hardback

Note

Includes bibliographical references (p. 599-603) and index

Description and Table of Contents

Description

Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data. New to the Second Edition Reorganized to focus on unbalanced data Reworked balanced analyses using methods for unbalanced data Introductions to nonparametric and lasso regression Introductions to general additive and generalized additive models Examination of homologous factors Unbalanced split plot analyses Extensions to generalized linear models R, Minitab (R), and SAS code on the author's website The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.

Table of Contents

Introduction. One Sample. General Statistical Inference. Two Samples. Contingency Tables. Simple Linear Regression. Model Checking. Lack of Fit and Nonparametric Regression. Multiple Regression: Introduction. Diagnostics and Variable Selection. Multiple Regression: Matrix Formulation. One-Way ANOVA. Multiple Comparison Methods. Two-Way ANOVA. ACOVA and Interactions. Multifactor Structures. Basic Experimental Designs. Factorial Treatments. Dependent Data. Logistic Regression: Predicting Counts. Log-Linear Models: Describing Count Data. Exponential and Gamma Regression: Time-to-Event Data. Nonlinear Regression. Appendices.

by "Nielsen BookData"

• NCID: BB26451323
• ISBN:
  • 9781498730143
  • 1498730140
• LCCN: 2016302032
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Boca Raton
• Pages/Volumes: xxv, 610 p.
• Size: 27 cm
• Classification:
  • LCC : QA279
• Subject Headings:
  • LCSH : Mathematical statistics
  • LCSH : Analysis of variance
  • LCSH : Regression analysis
  • LCSH : Experimental design
• Parent Bibliography ID:
  • BA22776704