Linear Regression (Hardcover, 1st ed. 2017)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	April 2017
First published:	2017
Authors:	David J. Olive
Dimensions:	235 x 155 x 32mm (L x W x T)
Format:	Hardcover
Pages:	494
Edition:	1st ed. 2017
ISBN-13:	978-3-319-55250-7
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
LSN:	3-319-55250-3
Barcode:	9783319552507

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