{"product_id":"quantitative-methods-extensions-of-ordinary-regression-wiley-series-in-probability-and-statistics-9780471455059","title":"Quantitative Methods Extensions of Ordinary","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eResponding to threats such as super diseases and bioterrorism, measuring the health of populations is regarded as an issue of paramount importance. This title addresses the analysis of a population's health for non statisticians such as epidemiologists and health services researchers.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"I enjoyed reading this book and I recommend…[it].\" (\u003ci\u003eJournal of Statistical Computation and Simulation\u003c\/i\u003e, July 2005)  \u003cp\u003e\"The book is well written…a timely book that appears to cover a gap in existing literature.\" (\u003ci\u003eJournal of the American Statistical Association\u003c\/i\u003e, June 2005)\u003c\/p\u003e \u003cp\u003e“…provides an accessible guide for students in an applied statistics sequence as well as for practising researchers and professionals...” (\u003ci\u003eZentralblatt Math\u003c\/i\u003e, Vol.1038, No.13, 2004)\u003c\/p\u003e \u003cp\u003e\"It is highly recommended for academic and research libraries supporting programs of demography, public health, and other interdisciplinary programs related to population health.” (\u003ci\u003eE-STREAMS\u003c\/i\u003e, August 2004)\u003c\/p\u003e \u003cp\u003e“...assembles the information...investigators need most often in the course of several long-term population-based observational studies.” (\u003ci\u003eQuarterly of Applied Mathematics\u003c\/i\u003e, Vol. LXII, No. 1, March 2004)\u003c\/p\u003e \u003cp\u003e\"...this book...provides the most pages of illustrations relative to pages of text of any book that I can recall...a fantastic book for practitioners...\" (\u003ci\u003eTechnometrics\u003c\/i\u003e, Vol. 46, No. 1, February 2004)\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eAcknowledgments.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAcronyms.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eI.1 Newborn Lung Project.\u003c\/p\u003e \u003cp\u003eI.2 Wisconsin Diabetes Registry.\u003c\/p\u003e \u003cp\u003eI.3 Wisconsin Sleep Cohort Study.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSuggested Reading.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Review of Ordinary Linear Regression and Its Assumptions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The Ordinary Linear Regression Equation and Its Assumptions.\u003c\/p\u003e \u003cp\u003e1.1.1 Straight-Line Relationship.\u003c\/p\u003e \u003cp\u003e1.1.2 Equal Variance Assumption.\u003c\/p\u003e \u003cp\u003e1.1.3 Normality Assumption.\u003c\/p\u003e \u003cp\u003e1.1.4 Independence Assumption.\u003c\/p\u003e \u003cp\u003e1.2 A Note on How the Least-Squares Estimators are Obtained.\u003c\/p\u003e \u003cp\u003eOutput Packet I: Examples of Ordinary Regression Analyses.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Maximum Likelihood Approach to Ordinary Regression.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Maximum Likelihood Estimation.\u003c\/p\u003e \u003cp\u003e2.2 Example.\u003c\/p\u003e \u003cp\u003e2.3 Properties of Maximum Likelihood Estimators.\u003c\/p\u003e \u003cp\u003e2.4 How to Obtain a Residual Plot with PROC MIXED.\u003c\/p\u003e \u003cp\u003eOutput Packet II: Using PROC MIXED and Comparisons to PROC RE G.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Reformulating Ordinary Regression Analysis in Matrix Notation.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Writing the Ordinary Regression Equation in Matrix Notation.\u003c\/p\u003e \u003cp\u003e3.1.1 Example.\u003c\/p\u003e \u003cp\u003e3.2 Obtaining the Least-Squares Estimator \u003ci\u003eβ\u003c\/i\u003e in Matrix Notation.\u003c\/p\u003e \u003cp\u003e3.2.1 Example: Matrices in Regression Analysis.\u003c\/p\u003e \u003cp\u003e3.3 List of Matrix Operations to Know.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Variance Matrices and Linear Transformations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Variance and Correlation Matrices.\u003c\/p\u003e \u003cp\u003e4.1.1 Example.\u003c\/p\u003e \u003cp\u003e4.2 How to Obtain the Variance of a Linear Transformation.\u003c\/p\u003e \u003cp\u003e4.2.1 Two Variables.\u003c\/p\u003e \u003cp\u003e4.2.2 Many Variables.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Variance Matrices of Estimators of Regression Coefficients.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Usual Standard Error of Least-Squares Estimator of Regression Slope in Nonmatrix Formulation.\u003c\/p\u003e \u003cp\u003e5.2 Standard Errors of Least-Squares Regression Estimators in Matrix Notation.\u003c\/p\u003e \u003cp\u003e5.2.1 Example.\u003c\/p\u003e \u003cp\u003e5.3 The Large Sample Variance Matrix of Maximum Likelihood Estimators.\u003c\/p\u003e \u003cp\u003e5.4 Tests and Confidence Intervals.\u003c\/p\u003e \u003cp\u003e5.4.1 Example-Comparing PROC REG and PROC MIXED.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Dealing with Unequal Variance Around the Regression Line.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Ordinary Least Squares with Unequal Variance.\u003c\/p\u003e \u003cp\u003e6.1.1 Examples.\u003c\/p\u003e \u003cp\u003e6.2 Analysis Taking Unequal Variance into Account.\u003c\/p\u003e \u003cp\u003e6.2.1 The Functional Transformation Approach.\u003c\/p\u003e \u003cp\u003e6.2.2 The Linear Transformation Approach.\u003c\/p\u003e \u003cp\u003e6.2.3 Standard Errors of Weighted Regression Estimators.\u003c\/p\u003e \u003cp\u003eOutput Packet III: Applying the Empirical Option to Adjust Standard Errors.\u003c\/p\u003e \u003cp\u003eOutput Packet IV: Analyses with Transformation of the Outcome Variable to Equalize Residual Variance.\u003c\/p\u003e \u003cp\u003eOutput Packet V: Weighted Regression Analyses of GHb Data on Age.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Application of Weighting with Probability Sampling and Nonresponse.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Sample Surveys with Unequal Probability Sampling.\u003c\/p\u003e \u003cp\u003e7.1.1 Example.\u003c\/p\u003e \u003cp\u003e7.2 Examining the Impact of Nonresponse.\u003c\/p\u003e \u003cp\u003e7.2.1 Example (of Reweighting as Well as Some SAS Manipulations).\u003c\/p\u003e \u003cp\u003e7.2.2 A Few Comments on Weighting by a Variable Versus Including it in the Regression Model.\u003c\/p\u003e \u003cp\u003eOutput Packet VI: Survey and Missing Data Weights.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Principles in Dealing with Correlated Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Analysis of Correlated Data by Ordinary Unweighted Least-Squares Estimation.\u003c\/p\u003e \u003cp\u003e8.1.1 Example.\u003c\/p\u003e \u003cp\u003e8.1.2 Deriving the Variance Estimator.\u003c\/p\u003e \u003cp\u003e8.1.3 Example.\u003c\/p\u003e \u003cp\u003e8.2 Specifying Correlation and Variance Matrices.\u003c\/p\u003e \u003cp\u003e8.3 The Least-Squares Equation Incorporating Correlation.\u003c\/p\u003e \u003cp\u003e8.3.1 Another Application of the Spectral Theorem.\u003c\/p\u003e \u003cp\u003e8.4 Applying the Spectral Theorem to the Regression Analysis of Correlated Data.\u003c\/p\u003e \u003cp\u003e8.5 Analysis of Correlated Data by Maximum Likelihood.\u003c\/p\u003e \u003cp\u003e8.5.1 Non equal Variance.\u003c\/p\u003e \u003cp\u003e8.5.2 Correlated Errors.\u003c\/p\u003e \u003cp\u003e8.5.3 Example.\u003c\/p\u003e \u003cp\u003eOutput Packet VII: Analysis of Longitudinal Data in Wisconsin Sleep Cohort.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 A Further Study of How the Transformation Works with Correlated Data.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Why Would \u003ci\u003e?\u003c\/i\u003e\u003ci\u003eW\u003c\/i\u003e and \u003ci\u003e?\u003c\/i\u003e\u003ci\u003eB\u003c\/i\u003e Differ?\u003c\/p\u003e \u003cp\u003e9.2 How the Between- and Within-Individual Estimators are Combined.\u003c\/p\u003e \u003cp\u003e9.3 How to Proceed in Practice.\u003c\/p\u003e \u003cp\u003e9.3.1 Example.\u003c\/p\u003e \u003cp\u003eOutput Packet VIII: Investigating and Fitting Within- and Between-Individual Effects.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Random Effects.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Random Intercept.\u003c\/p\u003e \u003cp\u003e10.1.1 Example.\u003c\/p\u003e \u003cp\u003e10.1.2 Example.\u003c\/p\u003e \u003cp\u003e10.2 Random Slopes.\u003c\/p\u003e \u003cp\u003e10.2.1 Example.\u003c\/p\u003e \u003cp\u003e10.3 Obtaining “The Best” Estimates of Individual Intercepts and Slopes.\u003c\/p\u003e \u003cp\u003e10.3.1 Example.\u003c\/p\u003e \u003cp\u003eOutput Packet IX: Fitting Random Effects Models.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 The Normal Distribution and Likelihood Revisited.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 PROC GENMOD.\u003c\/p\u003e \u003cp\u003e11.1.1 Example.\u003c\/p\u003e \u003cp\u003eOutput Packet X: Introducing PROC GENMOD.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 The Generalization to Non-normal Distributions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 The Exponential Family.\u003c\/p\u003e \u003cp\u003e12.1.1 The Binomial Distribution.\u003c\/p\u003e \u003cp\u003e12.1.2 The Poisson Distribution.\u003c\/p\u003e \u003cp\u003e12.1.3 Example.\u003c\/p\u003e \u003cp\u003e12.2 Score Equations for the Exponential Family and the Canonical Link.\u003c\/p\u003e \u003cp\u003e12.3 Other Link Functions.\u003c\/p\u003e \u003cp\u003e12.3.1 Example.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Modeling Binomial and Binary Outcomes.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 A Brief Review of Logistic Regression.\u003c\/p\u003e \u003cp\u003e13.1.1 Example: Review of the Output from PROC LOGIST.\u003c\/p\u003e \u003cp\u003e13.2 Analysis of Binomial Data in the Generalized Linear Models Framework.\u003c\/p\u003e \u003cp\u003e13.2.1 Example of Logistic Regression with Binary Outcome.\u003c\/p\u003e \u003cp\u003e13.2.2 Example with Binomial Outcome.\u003c\/p\u003e \u003cp\u003e13.2.3 Some More Examples of Goodness-of-Fit Tests.\u003c\/p\u003e \u003cp\u003e13.3 Other Links for Binary and Binomial Data.\u003c\/p\u003e \u003cp\u003e13.3.1 Example.\u003c\/p\u003e \u003cp\u003eOutput Packet XI: Logistic Regression Analysis with PROC LOGIST and PROC GENMOD.\u003c\/p\u003e \u003cp\u003eOutput Packet XII: Analysis of Grouped Binomial Data.\u003c\/p\u003e \u003cp\u003eOutput Packet XIII: Some Goodness-of-Fit Tests for Binomial Outcome.\u003c\/p\u003e \u003cp\u003eOutput Packet XIV: Three Link Functions for Binary Outcome.\u003c\/p\u003e \u003cp\u003eOutput Packet XV: Poisson Regression.\u003c\/p\u003e \u003cp\u003eOutput Packet XVI: Dealing with Overdispersion in Rates.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Modeling Poisson Outcomes—The Analysis of Rates.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Review of Rates.\u003c\/p\u003e \u003cp\u003e14.1.1 Relationship Between Rate and Risk.\u003c\/p\u003e \u003cp\u003e14.2 Regression Analysis.\u003c\/p\u003e \u003cp\u003e14.3 Example with Cancer Mortality Rates.\u003c\/p\u003e \u003cp\u003e14.3.1 Example with Hospitalization of Infants.\u003c\/p\u003e \u003cp\u003e14.4 Overdispersion.\u003c\/p\u003e \u003cp\u003e14.4.1 Fitting a Dispersion Parameter.\u003c\/p\u003e \u003cp\u003e14.4.2 Fitting a Different Distribution.\u003c\/p\u003e \u003cp\u003e14.4.3 Using Robust Standard Errors.\u003c\/p\u003e \u003cp\u003e14.4.4 Applying Adjustments for Over Dispersion to the Examples.\u003c\/p\u003e \u003cp\u003eOutput Packet XV: Poisson Regression.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Modeling Correlated Outcomes with Generalized Estimating Equations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 A Brief Review and Reformulation of the Normal Distribution, Least Squares and Likelihood.\u003c\/p\u003e \u003cp\u003e15.2 Further Developments for the Exponential Family.\u003c\/p\u003e \u003cp\u003e15.3 How are the Generalized Estimating Equations Justified?\u003c\/p\u003e \u003cp\u003e15.3.1 Analysis of Longitudinal Systolic Blood Pressure by PROC MIXED and GENMOD.\u003c\/p\u003e \u003cp\u003e15.3.2 Analysis of Longitudinal Hypertension Data by PROC GENMOD.\u003c\/p\u003e \u003cp\u003e15.3.3 Analysis of Hospitalizations Among VLBW Children Up to Age 5.\u003c\/p\u003e \u003cp\u003e15.4 Another Way to Deal with Correlated Binary Data.\u003c\/p\u003e \u003cp\u003eOutput Packet XVII: Mixed Versus GENMOD for Longitudinal SBP and Hypertension Data.\u003c\/p\u003e \u003cp\u003eOutput Packet XVIII: Longitudinal Analysis of Rates.\u003c\/p\u003e \u003cp\u003eOutput Packet XIX: Conditional Logistic Regression of Hypertension Data.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix: Matrix Operations.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Adding Matrices.\u003c\/p\u003e \u003cp\u003eA.2 Multiplying Matrices by a Number.\u003c\/p\u003e \u003cp\u003eA.3 Multiplying Matrices by Each Other.\u003c\/p\u003e \u003cp\u003eA.4 The Inverse of a Matrix.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49402598326615,"sku":"9780471455059","price":130.45,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780471455059.jpg?v=1730480915","url":"https:\/\/bookcurl.com\/products\/quantitative-methods-extensions-of-ordinary-regression-wiley-series-in-probability-and-statistics-9780471455059","provider":"Book Curl","version":"1.0","type":"link"}