# Applied regression analysis / Norman R. Draper, Harry Smith.

##### By: Draper, Norman Richard

##### Contributor(s): Smith, Harry

Material type: TextLanguage: English Series: Wiley-Interscience publicationWiley series in probability and statistics: Publisher: New York ; Chichester : John Wiley, c1998Edition: 3rd edDescription: xvii, 706 p. : ill. ; 26 cm. +ISBN: 0471170828 (hbk); 9788126531738; 9780471170822Subject(s): Regression analysisDDC classification: 519.536 LOC classification: QA278.2 | .D7 1998Online resources: WorldCat details | E-book FulltextItem type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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Text | EWU Library Circulation Section | Non-fiction | 519.536 DRA 2011 (Browse shelf) | C-3 | Available | 25685 |

Online version:

Draper, Norman Richard.

Applied regression analysis.

New York : Wiley, c1998

(OCoLC)765771922

Includes bibliographical references (p. 593-603) and indexes.

Table of contents Preface --

About the software --

Basic prerequisite knowledge --

Fitting a straight line by least squares --

Checking the straight line fit --Fitting straight lines: special topics --

Regression in matrix terms: Straight line case --

The general regression situation --

Extra sums of squares and tests for several parameters being zero --

Serial correlation in the residuals and the Durbin-Watson test --

More on checking fitted models --

Multiple regression: special topics --

Bias in regression estimates, and expected values of mean squares and sums of squares --

On worthwhile regressions, big F's, and R² --

Models containing functions of the predictors, including polynomial models --

Transformation of the response variable --

"Dummy" variables --

Selecting the "Best" regression equation --

Ill-conditioning in regression data --

Ridge regression --

Generalized linear models (GLIM) --

Mixture ingredients as predictor variables --

The geometry of least squares --

More geometry of least squares --

Orthogonal polynomials and summary data --

Multiple regression applied to analysis of variance problems --

An introduction to nonlinear estimation --

Robust regression --

Resampling procedures (Bootstrapping) --

Bibliography --

True/false questions --

Answers to exercises --

Tables --

Index of authors associate with exercises --

Index.

Applied Statistics

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