Linear Regression by David J. Olive (PDF)

7

 

Ebook Info

  • Published: 2017
  • Number of pages: 508 pages
  • Format: PDF
  • File Size: 3.07 MB
  • Authors: David J. Olive

Description

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.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

Keywords

Free Download Linear Regression in PDF format
Linear Regression PDF Free Download
Download Linear Regression 2017 PDF Free
Linear Regression 2017 PDF Free Download
Download Linear Regression PDF
Free Download Ebook Linear Regression

Previous articleSuper-Real Fields: Totally Ordered Fields with Additional Structure (London Mathematical Society Monographs, 14) 1st Edition by H. Garth Dales (PDF)
Next articleStatistical Theory and Inference 2014th Edition by David J. Olive (PDF)