|
|
|
|
| LEADER |
06231cam a2200613 i 4500 |
| 001 |
d04c4f8c-40df-4cc4-be86-f3d24e99a20a |
| 005 |
20230816000000.0 |
| 008 |
130717s2014 njua ob 001 0 eng d |
| 040 |
|
|
|a IDEBK
|b eng
|e rda
|e pn
|c IDEBK
|d OCLCQ
|d OCLCO
|d OCLCF
|d AU@
|d YDXCP
|d DEBBG
|d OCLCQ
|d COO
|d UUM
|d CEF
|d YDX
|d INT
|d COF
|d OCLCQ
|d UKAHL
|d VT2
|d OCLCO
|d OCLCQ
|
| 066 |
|
|
|c (S
|
| 019 |
|
|
|a 891546524
|a 906225638
|a 1148157430
|
| 020 |
|
|
|a 1306731445
|q (ebk)
|
| 020 |
|
|
|a 9781306731447
|q (ebk)
|
| 020 |
|
|
|a 9781118594858
|
| 020 |
|
|
|a 1118594851
|
| 020 |
|
|
|a 9781118789551
|
| 020 |
|
|
|a 1118789555
|
| 020 |
|
|
|a 9781118386088
|q (hardback)
|
| 020 |
|
|
|a 1118386086
|q (hardback)
|
| 029 |
1 |
|
|a AU@
|b 000051762070
|
| 029 |
1 |
|
|a DEBBG
|b BV042989742
|
| 029 |
1 |
|
|a AU@
|b 000052923217
|
| 035 |
|
|
|a (OCoLC)879424010
|z (OCoLC)891546524
|z (OCoLC)906225638
|z (OCoLC)1148157430
|
| 037 |
|
|
|a 604395
|b MIL
|
| 050 |
|
4 |
|a QA278.2
|b .W44 2014eb
|
| 082 |
0 |
4 |
|a 519.5/36
|2 23
|
| 084 |
|
|
|a MAT029030
|a MAT029000
|a MAT003000
|2 bisacsh
|
| 049 |
|
|
|a PSFA
|
| 100 |
1 |
|
|a Weisberg, Sanford,
|d 1947-
|e author.
|
| 245 |
1 |
0 |
|a Applied linear regression /
|c Sanford Weisberg, School of Statistics, University of Minnesota, Minneapolis, MN.
|
| 250 |
|
|
|a Fourth edition.
|
| 264 |
|
1 |
|a Hoboken, New Jersey :
|b Wiley,
|c 2014.
|
| 300 |
|
|
|a 1 online resource (xvii, 340 pages) :
|b illustrations
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 504 |
|
|
|a Includes bibliographical references (pages 317-328) and indexes.
|
| 588 |
0 |
|
|a Print version record.
|
| 505 |
0 |
|
|a Scatterplots and regression -- Simple linear regression -- Multiple regression -- Interpretation of main effects -- Complex regressors -- Testing and analysis of variance -- Variances -- Transformations -- Regression diagnostics -- Variable selection -- Nonlinear regression -- Binomial and Poisson regression -- Appendix.
|
| 520 |
|
|
|a The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illustrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. While maintaining the accessible appeal of each previous edition, Applied Linear Regression, Fourth Edition features: graphical methods stressed in the initial exploratory phase, analysis phase, and summarization phase of an analysis; in-depth coverage of parameter estimates in both simple and complex models, transformations, and regression diagnostics; newly added material on topics including testing, ANOVA, and variance assumptions; updated methodology, such as bootstrapping, cross-validation binomial and Poisson regression, and modern model selection methods.
|
| 520 |
|
|
|a Applied Linear Regression, Fourth Edition is an excellent textbook for upper-undergraduate and graduate-level students, as well as an appropriate reference guide for practitioners and applied statisticians in engineering, business administration, economics, and the social sciences.
|
| 650 |
|
0 |
|a Regression analysis.
|
| 650 |
|
0 |
|a Linear models (Statistics)
|
| 650 |
|
6 |
|a Analyse de régression.
|
| 650 |
|
7 |
|a MATHEMATICS
|x Applied.
|2 bisacsh
|
| 650 |
|
7 |
|a MATHEMATICS
|x Probability & Statistics
|x Regression Analysis.
|2 bisacsh
|
| 650 |
|
7 |
|a MATHEMATICS
|x Probability & Statistics
|x General.
|2 bisacsh
|
| 650 |
|
7 |
|a Linear models (Statistics)
|2 fast
|0 (OCoLC)fst00999084
|
| 650 |
|
7 |
|a Regression analysis.
|2 fast
|0 (OCoLC)fst01432090
|
| 650 |
|
7 |
|a Regressionsanalys.
|2 sao
|
| 775 |
0 |
8 |
|i Revision of:
|a Weisberg, Sanford, 1947-
|t Applied linear regression.
|b Third edition.
|d Hoboken, N.J. : Wiley-Interscience, ß2005
|z 0471663794
|
| 776 |
0 |
8 |
|i Print version:
|z 1118386086
|w (DLC) 2013026538
|
| 880 |
0 |
|
|6 505-00/(S
|a Cover -- Title page -- Copyright page -- Dedication -- Contents -- Preface to the Fourth Edition -- CHAPTER 1: Scatterplots and Regression -- 1.1 Scatterplots -- 1.2 Mean Functions -- 1.3 Variance Functions -- 1.4 Summary Graph -- 1.5 Tools for Looking at Scatterplots -- 1.5.1 Size -- 1.5.2 Transformations -- 1.5.3 Smoothers for the Mean Function -- 1.6 Scatterplot Matrices -- 1.7 Problems -- CHAPTER 2: Simple Linear Regression -- 2.1 Ordinary Least Squares Estimation -- 2.2 Least Squares Criterion -- 2.3 Estimating the Variance σ2 -- 2.4 Properties of Least Squares Estimates -- 2.5 Estimated Variances -- 2.6 Confidence Intervals and t-Tests -- 2.6.1 The Intercept -- 2.6.2 Slope -- 2.6.3 Prediction -- 2.6.4 Fitted Values -- 2.7 The Coefficient of Determination, R2 -- 2.8 The Residuals -- 2.9 Problems -- CHAPTER 3: Multiple Regression -- 3.1 Adding a Regressor to a Simple Linear Regression Model -- 3.1.1 Explaining Variability -- 3.1.2 Added-Variable Plots -- 3.2 The Multiple Linear Regression Model -- 3.3 Predictors and Regressors -- 3.4 Ordinary Least Squares -- 3.4.1 Data and Matrix Notation -- 3.4.2 The Errors e -- 3.4.3 Ordinary Least Squares Estimators -- 3.4.4 Properties of the Estimates -- 3.4.5 Simple Regression in Matrix Notation -- 3.4.6 The Coefficient of Determination -- 3.4.7 Hypotheses Concerning One Coefficient -- 3.4.8 t-Tests and Added-Variable Plots -- 3.5 Predictions, Fitted Values, and Linear Combinations -- 3.6 Problems -- CHAPTER 4: Interpretation of Main Effects -- 4.1 Understanding Parameter Estimates -- 4.1.1 Rate of Change -- 4.1.2 Signs of Estimates -- 4.1.3 Interpretation Depends on Other Terms in the Mean Function -- 4.1.4 Rank Deficient and Overparameterized Mean Functions -- 4.1.5 Collinearity -- 4.1.6 Regressors in Logarithmic Scale -- 4.1.7 Response in Logarithmic Scale -- 4.2 Dropping Regressors.
|
| 999 |
1 |
0 |
|i d04c4f8c-40df-4cc4-be86-f3d24e99a20a
|l ocn879424010
|s US-PLOR
|m applied_linear_regression__________________________________________________2014____4__wileya________________________________________weisberg__sanford__________________e
|