{"product_id":"quantitative-methods-in-population-health-extensions-of-ordinary-regression-hardback-9780471455059","title":"Quantitative Methods in Population Health; Extensions of Ordinary Regression (Hardback) 9780471455059","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eQuantitative Methods in Population Health\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eExtensions of Ordinary Regression\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eMari Palta (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780471455059, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 15 August 2003\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages, Tables: 24 B\u0026amp;W, 0 Color; Graphs: 3 B\u0026amp;W, 0 Color\u003cbr\u003e24.2 x 16.2 x 2.2 cm, 0.614 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\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\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eEach 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 software\u003cbr\u003e Explanations 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.\u003cbr\u003e SAS commands are provided for applying the methods. (PROC REG, PROC MIXED, and PROC GENMOD)\u003cbr\u003e All sections contain real life examples, mostly from epidemiologic research\u003cbr\u003e First chapter includes a SAS refresher\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\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\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Interscience","offers":[{"title":"Brand New","offer_id":52293481922840,"sku":"9780471455059","price":104.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780471455059.jpg?v=1781641541","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/quantitative-methods-in-population-health-extensions-of-ordinary-regression-hardback-9780471455059","provider":"Freshly Printed Books","version":"1.0","type":"link"}