LEADER 05516nam 22006854a 450 001 9910876900803321 005 20200520144314.0 010 $a1-280-34400-8 010 $a9786610344000 010 $a0-470-24688-X 010 $a0-471-46798-7 010 $a0-471-46797-9 035 $a(CKB)111087027131652 035 $a(EBL)162765 035 $a(OCoLC)475872970 035 $a(SSID)ssj0000231211 035 $a(PQKBManifestationID)11193164 035 $a(PQKBTitleCode)TC0000231211 035 $a(PQKBWorkID)10216963 035 $a(PQKB)10145144 035 $a(MiAaPQ)EBC162765 035 $a(EXLCZ)99111087027131652 100 $a20030404d2003 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aQuantitative methods in population health $eextensions of ordinary regression /$fMari Palta 210 $aHoboken, N.J. $cJohn Wiley$dc2003 215 $a1 online resource (339 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-45505-9 320 $aIncludes bibliographical references and index. 327 $aQuantitative Methods in Population Health; List of Figures; List of Tables; Contents; Preface; Acknowledgments; Acronyms; Introduction; I.1 Newborn Lung Project; I.2 Wisconsin Diabetes Registry; I.3 Wisconsin Sleep Cohort Study; Suggested Reading; 1 Review of Ordinary Linear Regression and Its Assumptions; 1.1 The Ordinary Linear Regression Equation and Its Assumptions; 1.1.1 Straight-Line Relationship; 1.1.2 Equal Variance Assumption; 1.1.3 Normality Assumption; 1.1.4 Independence Assumption; 1.2 A Note on How the Least-Squares Estimators are Obtained 327 $aOutput Packet I: Examples of Ordinary Regression Analyses2 The Maximum Likelihood Approach to Ordinary Regression; 2.1 Maximum Likelihood Estimation; 2.2 Example; 2.3 Properties of Maximum Likelihood Estimators; 2.4 How to Obtain a Residual Plot with PROC MIXED; Output Packet II: Using PROC MIXED and Comparisons to PROC REG; 3 Reformulating Ordinary Regression Analysis in Matrix Notation; 3.1 Writing the Ordinary Regression Equation in Matrix Notation; 3.1.1 Example; 3.2 Obtaining the Least-Squares Estimator b in Matrix Notation; 3.2.1 Example: Matrices in Regression Analysis 327 $a3.3 List of Matrix Operations to Know4 Variance Matrices and Linear Transformations; 4.1 Variance and Correlation Matrices; 4.1.1 Example; 4.2 How to Obtain the Variance of a Linear Transformation; 4.2.1 Two Variables; 4.2.2 Many Variables; 5 Variance Matrices of Estimators of Regression Coefficients; 5.1 Usual Standard Error of Least-Squares Estimator of Regression Slope in Nonmatrix Formulation; 5.2 Standard Errors of Least-Squares Regression Estimators in Matrix Notation; 5.2.1 Example; 5.3 The Large Sample Variance Matrix of Maximum Likelihood Estimators 327 $a5.4 Tests and Confidence Intervals5.4.1 Example-Comparing PROC REG and PROC MIXED; 6 Dealing with Unequal Variance Around the Regression Line; 6.1 Ordinary Least Squares with Unequal Variance; 6.1.1 Examples; 6.2 Analysis Taking Unequal Variance into Account; 6.2.1 The Functional Transformation Approach; 6.2.2 The Linear Transformation Approach; 6.2.3 Standard Errors of Weighted Regression Estimators; Output Packet III: Applying the Empirical Option to Adjust Standard Errors; Output Packet IV: Analyses with Transformation of the Outcome Variable to Equalize Residual Variance 327 $aOutput Packet V: Weighted Regression Analyses of GHb Data on Age7 Application of Weighting with Probability Sampling and Nonresponse; 7.1 Sample Surveys with Unequal Probability Sampling; 7.1.1 Example; 7.2 Examining the Impact of Nonresponse; 7.2.1 Example (of Reweighting as Well as Some SAS Manipulations); 7.2.2 A Few Comments on Weighting by a Variable Versus Including it in the Regression Model; Output Packet VI: Survey and Missing Data Weights; 8 Principles in Dealing with Correlated Data; 8.1 Analysis of Correlated Data by Ordinary Unweighted Least-Squares Estimation; 8.1.1 Example 327 $a8.1.2 Deriving the Variance Estimator 330 $aEach topic starts with an explanation of the theoretical background necessary to allow full understanding of the technique and to facilitate future learning of more advanced or new methods and softwareExplanations are designed to assume as little background in mathematics and statistical theory as possible, except that some knowledge of calculus is necessary for certain parts.SAS commands are provided for applying the methods. (PROC REG, PROC MIXED, and PROC GENMOD)All sections contain real life examples, mostly from epidemiologic researchFirst chapter includes a SAS refresher 410 0$aWiley series in probability and statistics. 606 $aMedical statistics 606 $aRegression analysis 606 $aPopulation$xHealth aspects$xStatistical methods 606 $aHealth surveys$xStatistical methods 615 0$aMedical statistics. 615 0$aRegression analysis. 615 0$aPopulation$xHealth aspects$xStatistical methods. 615 0$aHealth surveys$xStatistical methods. 676 $a614.4/2/0727 700 $aPalta$b Mari$f1948-$01757653 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910876900803321 996 $aQuantitative methods in population health$94195570 997 $aUNINA