03330nam 2200529z- 450 991096074270332120250219111929.097811185485091118548507(CKB)6190000000004969(MiAaPQ)EBC7103795(EXLCZ)99619000000000496920230424c2013uuuu -u- -engurcnu||||||||txtrdacontentcrdamediacrrdacarrierSolutions Manual to Accompany Introduction to Linear Regression Analysis, Fifth Edition5th ed.John Wiley & Sons (US)Somerset :John Wiley & Sons, Incorporated,2013.©2013.1 online resource (105 pages)Preface signed by Anne G. Ryan, Dana C. Krueger, Scott M. Kowalski.9781118471463 1118471466 Intro -- Half Title page -- Title page -- Copyright page -- Preface -- Chapter 2: Simple Linear Regression -- Chapter 3: Multiple Linear Regression -- Chapter 4: Model Adequacy Checking -- Chapter 5: Transformations and Weighting to Correct Model Inadequacies -- Chapter 6: Diagnostics for Leverage and Influence -- Chapter 7: Polynomial Regression Models -- Chapter 8: Indicator Variables -- Chapter 9: Multicollinearity -- Chapter 10: Variable Selection and Model Building -- Chapter 11: Validation of Regression Models -- Chapter 12: Introduction to Nonlinear Regression -- Chapter 13: Generalized Linear Models -- Chapter 14: Regression Analysis of Time Series Data -- Chapter 15: Other Topics in the Use of Regression Analysis.As the Solutions Manual, this book is meant to accompany the main title, Introduction to Linear Regression Analysis, Fifth Edition. Clearly balancing theory with applications, this book describes both the conventional and less common uses of linear regression in the practical context of today's mathematical and scientific research. Beginning with a general introduction to regression modeling, including typical applications, the book then outlines a host of technical tools that form the linear regression analytical arsenal, including: basic inference procedures and introductory aspects of model adequacy checking; how transformations and weighted least squares can be used to resolve problems of model inadequacy; how to deal with influential observations; and polynomial regression models and their variations. The book also includes material on regression models with autocorrelated errors, bootstrapping regression estimates, classification and regression trees, and regression model validation.Correlation (Statistics)MathematicsRegression analysisCorrelation (Statistics)Mathematics.Regression analysis.519.5/36Montgomery Douglas C9293Peck Elizabeth A20687Vining G Geoffrey266109Ryan Ann G1789315MiAaPQMiAaPQMiAaPQBOOK9910960742703321Solutions Manual to Accompany Introduction to Linear Regression Analysis, Fifth Edition4324811UNINA