LEADER 05433nam 2200685 a 450 001 9910782663303321 005 20220915111508.0 010 $a1-282-54017-3 010 $a9786612540172 010 $a0-08-052297-1 035 $a(CKB)1000000000707295 035 $a(EBL)534893 035 $a(OCoLC)635292744 035 $a(SSID)ssj0000426898 035 $a(PQKBManifestationID)11262015 035 $a(PQKBTitleCode)TC0000426898 035 $a(PQKBWorkID)10390524 035 $a(PQKB)10100123 035 $a(MiAaPQ)EBC534893 035 $a(Au-PeEL)EBL534893 035 $a(CaPaEBR)ebr10382847 035 $a(CaONFJC)MIL254017 035 $a(PPN)17025173X 035 $a(EXLCZ)991000000000707295 100 $a20051206d2006 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aRegression analysis $estatistical modeling of a response variable 205 $a2nd ed. /$bRudolf J. Freund, William J. Wilson, Ping Sa. 210 $aBurlington, MA $cElsevier Academic Press$dc2006 215 $a1 online resource (481 p.) 300 $aDescription based upon print version of record. 311 0 $a1-4933-0001-6 311 0 $a0-12-088597-2 320 $aIncludes bibliographical references (p. 445-447) and index. 327 $aFront Cover; Regression Analysis: Statistical Modeling of a Response Variable; Copyright Page; Contents; Preface; An Overview; Part I: The Basics; Chapter 1. The Analysis of Means: A Review of Basics and an Introduction to Linear Models; 1.1 Introduction; 1.2 Sampling Distributions; 1.3 Inferences on a Single Population Mean; 1.4 Inferences on Two Means Using Independent Samples; 1.5 Inferences on Several Means; 1.6 Summary; 1.7 Chapter Exercises; Chapter 2. Simple Linear Regression: Linear Regression with one Independent Variable; 2.1 Introduction; 2.2 The Linear Regression Model 327 $a2.3 Inferences on the Parameters ß0 and ß12.4 Inferences on the Response Variable; 2.5 Correlation and the Coefficient of Determination; 2.6 Regression through the Origin; 2.7 Assumptions on the Simple Linear Regression Model; 2.8 Uses and Misuses of Regression; 2.9 Inverse Predictions; 2.10 Summary; 2.11 Chapter Exercises; Chapter 3. Multiple Linear Regression; 3.1 Introduction; 3.2 The Multiple Linear Regression Model; 3.3 Estimation of Coefficients; 3.4 Interpreting the Partial Regression Coefficients; 3.5 Inferences on the Parameters 327 $a3.6 Testing a General Linear Hypothesis (Optional Topic)3.7 Inferences on the Response Variable in Multiple Regression; 3.8 Correlation and the Coefficient of Determination; 3.9 Getting Results; 3.10 Summary and a Look Ahead; 3.11 Chapter Exercises; Part II: Problems and Remedies; Chapter 4. Problems with Observations; 4.1 Introduction; 4.2 Outliers and Influential Observations; 4.3 Unequal Variances; 4.4 Robust Estimation; 4.5 Correlated Errors; 4.6 Summary; 4.7 Chapter Exercises; Chapter 5. Multicollinearity; 5.1 Introduction; 5.2 The Effects of Multicollinearity 327 $a5.3 Diagnosing Multicollinearity 5.4 Remedial Methods; 5.5 Summary; 5.6 Chapter Exercises; Chapter 6. Problems with the Model; 6.1 Introduction; 6.2 Specification Error; 6.3 Lack of Fit Test; 6.4 Overspecification: Too Many Variables; 6.5 Variable Selection Procedures; 6.6 Reliability of Variable Selection; 6.7 Usefulness of Variable Selection; 6.8 Variable Selection and Influential Observations; 6.9 Summary; 6.10 Chapter Exercises; Part III: Additional Uses of Regression; Chapter 7. Curve Fitting; 7.1 Introduction; 7.2 Polynomial Models with One Independent Variable 327 $a7.3 Segmented Polynomials with Known Knots 7.4 Polynomial Regression in Several Variables; Response Surfaces; 7.5 Curve Fitting without a Model; 7.6 Summary; 7.7 Chapter Exercises; Chapter 8. Introduction to Nonlinear Models; 8.1 Introduction; 8.2 Intrinsically Linear Models; 8.3 Intrinsically Nonlinear Models; 8.4 Summary; 8.5 Chapter Exercises; Chapter 9. Indicator Variables; 9.1 Introduction; 9.2 The Dummy Variable Model; 9.3 Unequal Cell Frequencies; 9.4 Empty Cells; 9.5 Models with Dummy and Continuous Variables; 9.6 A Special Application: The Analysis of Covariance 327 $a9.7 Heterogeneous Slopes in the Analysis of Covariance 330 $aThe book provides complete coverage of the classical methods of statistical analysis. It is designed to give students an understanding of the purpose of statistical analyses, to allow the student to determine, at least to some degree, the correct type of statistical analyses to be performed in a given situation, and have some appreciation of what constitutes good experimental design.* Examples and exercises contain real data and graphical illustration for ease of interpretation* Outputs from SAS 7, SPSS 7, Excel, and Minitab are used for illustration, but any major 606 $aRegression analysis 606 $aLinear models (Statistics) 615 0$aRegression analysis. 615 0$aLinear models (Statistics) 676 $a519.5/36 700 $aFreund$b Rudolf J$g(Rudolf Jakob),$f1927-2014.$044078 701 $aWilson$b William J.$f1940-$044079 701 $aSa$b Ping$01561723 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910782663303321 996 $aRegression analysis$93828718 997 $aUNINA