國立高雄大學圖資館 |

Language: English

Back

Linear regression

Olive, David J.

Linear regression

Record Type:	Electronic resources : Monograph/item
Title/Author:	Linear regressionby David J. Olive.
Author:	Olive, David J.
Published:	Cham :Springer International Publishing :2017.
Description:	xiv, 494 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Regression analysis.
Online resource:	http://dx.doi.org/10.1007/978-3-319-55252-1
ISBN:	9783319552521$q(electronic bk.)

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$q(electronic bk.)

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

181872
Regression analysis.

LC Class. No.: QA278.2

Dewey Class. No.: 519.536

Linear regression
LDR:02515nmm a2200313 a 4500 001 512197
003 DE-He213
005 20170418084506.0
006 m d
007 cr nn 008maaau
008 171226s2017 gw s 0 eng d
020 $a 9783319552521$q(electronic bk.)
020 $a 9783319552507$q(paper)
024 7 $a 10.1007/978-3-319-55252-1 $2 doi
035 $a 978-3-319-55252-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278.2
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.536 $2 23
090 $a QA278.2 $b .O48 2017
100 1 $a Olive, David J. $3 681715
245 1 0 $a Linear regression $h [electronic resource] / $c by David J. Olive.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xiv, 494 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Regression analysis. $3 181872
650 1 4 $a Statistics. $3 182057
650 2 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Statistics and Computing/Statistics Programs. $3 275710
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-55252-1
950 $a Mathematics and Statistics (Springer-11649)