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Linear regression

Olive, David J.

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Linear regression

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Linear regression/ by David J. Olive.
作者:	Olive, David J.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	xiv, 494 p. :ill., digital ;24 cm.
內容註:	Introduction -- Multiple Linear Regression -- Building an MLR Model -- WLS and Generalized Least Squares -- One Way Anova -- The K Way Anova Model -- Block Designs -- Orthogonal Designs -- More on Experimental Designs -- Multivariate Models -- Theory for Linear Models -- Multivariate Linear Regression -- GLMs and GAMs -- Stuff for Students.
Contained By:	Springer eBooks
標題:	Regression analysis. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-55252-1
ISBN:	9783319552521

Linear regression
Olive, David J.

Linear regression[electronic resource] /by David J. Olive. - Cham :Springer International Publishing :2017. - xiv, 494 p. :ill., digital ;24 cm.

Introduction -- Multiple Linear Regression -- Building an MLR Model -- WLS and Generalized Least Squares -- One Way Anova -- The K Way Anova Model -- Block Designs -- Orthogonal Designs -- More on Experimental Designs -- Multivariate Models -- Theory for Linear Models -- Multivariate Linear Regression -- GLMs and GAMs -- Stuff for Students.

This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response transformations for multiple linear regression or experimental design models. This text is for graduates and undergraduates with a strong mathematical background. The prerequisites for this text are linear algebra and a calculus based course in statistics.

ISBN: 9783319552521

Standard No.: 10.1007/978-3-319-55252-1doiSubjects--Topical Terms:

529831
Regression analysis.

LC Class. No.: QA278.2

Dewey Class. No.: 519.536

Linear regression
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505 0 $a Introduction -- Multiple Linear Regression -- Building an MLR Model -- WLS and Generalized Least Squares -- One Way Anova -- The K Way Anova Model -- Block Designs -- Orthogonal Designs -- More on Experimental Designs -- Multivariate Models -- Theory for Linear Models -- Multivariate Linear Regression -- GLMs and GAMs -- Stuff for Students.
520 $a This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response transformations for multiple linear regression or experimental design models. This text is for graduates and undergraduates with a strong mathematical background. The prerequisites for this text are linear algebra and a calculus based course in statistics.
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