Jump quickly to results on these stores:
Statistical Inference | Statistical Inference | Asymptotic Statistics | Bayesian Data Analysis | Categorical Data Analysis | Advanced R | An Introduction to Statistical Learning | Linear Models with R | A Probability Path
With coverage steadily progressing in complexity, the text first provides examples of the general linear model, including multiple regression models, one-way ANOVA, mixed-effects models, and time series models. It then introduces the basic algebra and geometry of the linear least squares problem, before delving into estimability and the GaussâMarkov model. After presenting the statistical tools of hypothesis tests and confidence intervals, the author analyzes mixed models, such as two-way mixed ANOVA, and the multivariate linear model. The appendices review linear algebra fundamentals and results as well as Lagrange multipliers.
This book enables complete comprehension of the material by taking a general, unifying approach to the theory, fundamentals, and exact results of linear models.
About: A Primer on Linear Models presents a unified, thorough, and rigorous development of the theory behind the statistical methodology of regression and analysis of variance (ANOVA).
Pricing is shown for items sent to or within the U.S., excluding shipping and tax. Please consult the store to determine exact fees. No warranties are made express or implied about the accuracy, timeliness, merit, or value of the information provided. Information subject to change without notice. isbn.nu is not a bookseller, just an information source.