Applied regression analysis / Norman R. Draper, Harry Smith.
Material type:
TextPublication details: Islamabad : NBF, 1998Edition: 3rd edDescription: xvii, 706 p. : ill. ; 26 cm. +ISBN: - 0471170828 (acidfree paper)
- 519.536 21 D765
| Item type | Current library | Call number | Status | Date due | Barcode | |
|---|---|---|---|---|---|---|
Books
|
UE-Central Library | 519.536 D765 (Browse shelf(Opens below)) | Available | T12522 | ||
|
Books
|
UE-Central Library | 519.536 D765 (Browse shelf(Opens below)) | Available | T12523 | ||
|
Books
|
UE-Central Library | 519.536 D765 (Browse shelf(Opens below)) | Available | T284D | ||
|
Books
|
UE-Central Library | 519.536 D765 (Browse shelf(Opens below)) | Available | T545D | ||
|
Books
|
UE-Central Library | 519.536 D765 (Browse shelf(Opens below)) | Available | T318D | ||
|
Books
|
UE-Central Library | 519.536 D765 (Browse shelf(Opens below)) | Available | T311D | ||
|
Books
|
UE-Central Library | 519.536 D765 (Browse shelf(Opens below)) | Available | T310D |
Browsing UE-Central Library shelves Close shelf browser (Hides shelf browser)
| 519.536 D765 Applied regression analysis / | 519.536 D765 Applied regression analysis / | 519.536 D765 Applied regression analysis / | 519.536 D765 Applied regression analysis / | 519.6 An134 Analysis and simulation of coronavirus model | 519.703 C45616 Applications of numerical techniques with C | 519.703 P298 Numerical computational methods |
"A Wiley-Interscience publication."
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 of 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 R-2. 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. Indexes.
There are no comments on this title.
