Course detail

LCE5801 - Regressão e Covariância


Credit hours

In-class work
per week
Practice
per week
Credits
Duration
Total
3
1
8
15 weeks
120 hours

Instructor
Clarice Garcia Borges Demetrio
Silvio Sandoval Zocchi
Taciana Villela Savian

Objective
The discipline of regression and covariance is intended to provide students with a solid foundation of the theory of regression as well as applications in various areas. At the end of the course the student will be able to adequately use the methods of estimation of parameters of the models of regression and covariance, perform an analysis for diagnosis and analysis of residues, use the methods of selection of variables, perform tests of hypotheses about the parameters of the models and obtain the confidence intervals and prediction.

Content
Simple linear regression: statistical model, estimated by the method of least squares, properties of the estimators, hypothesis testing and confidence intervals for the parameters, interval estimates. Generalization by the method information display. Multiple linear regression analysis: statistical model, estimated by the method of least squares, properties of the estimators, hypothesis testing and confidence interval for parameters, selection of variables. Orthogonal polynomials. Analysis of residues and diagnostics. Estimation of parameters in normal distribution bivariate analysis by the method of maximum likelihood. Correlation coefficients, simple, partial and multiple: pets, hypothesis testing and confidence intervals. Tests of parallelism of straight. Exponential regression. Logistic regression. Regression of mitscherlich. Analysis of covariance. Introduction to generalized linear models.

Bibliography
ATKINSON, A.C. Plots, Transformations and Regression: An Introduction to Graphical Methods and Diagnostic Regression Analysis. Clarendon Press, Oxford. 1985. 282p.
BELSLEY, D.A.; KUH, E.; WELSCH, R.E. Regression Diagnostics: Identifying Data and Source of Collinearity. John Wiley & Sons, 2005. 310p.
CHATERJEE, S. e B. PRICE. Regression Analysis by Example. John Wiley, Nova Iorque. 1977. 228p
COOK, R.D.; S. WEISBERG. An Introduction to Regression Graphics. John Wiley & Sons, Nova Iorque. 2009. 280p.
DEMÉTRIO, C.G.B. Modelos Lineares Generalizados na Experimentação Agronômica. 9º SEAGRO e 49ª Reunião Anual da RBRAS, Piracicaba. 2001. 113p.
DRAPER, N. e H. SMITH. Applied Regression Analysis. John Wiley, Nova Iorque. 1981. 709p.
FOX, J. Applied Regression Analysis and Generalized Linear Models. SAGE Publications, 2008. 665p. Edição Ilustrada.
KLEINBAUM; D. G.; KUPPER, L.L.; NIZAM, A.; MULLER, K. E. Applied Regression Analysis and Other Multivariable Methods. Cengage Learning, 2013. 1072p. 5ª Edição.
MONTGOMERY, D.C.; PECK, E.A.; VINING, G.G. Introduction to Linear Regression Analysis. John Wiley, Nova Iorque. 2012. 662p. 5ª Edição.
PINHEIRO, J.C.; BATES, D.M. Mixed-Effects Models in S and S-Plus. Springer Science & Business Media. 2010. 548p.
SEBER, G.A.F.; LEE, A.J. Linear Regression Analysis. John Wiley & Sons, Nova Iorque. 2012. 582p. 2ª Edição.
STEEL, R.G.D.; TORRIE, J.H.; DICKEY, D.A. Studyguide for Principles and Procedures of Statistics. A Biometrical Approach. Cram101 Incorporated. 2006. 156p.
VENABLES, W.N.; RIPLEY, B.D. Statistics and Computing. Springer-Verlag. 2002. 495p.
VITTINGHOFF, E., GLIDDEN, D.V., SHIBOSKI, S.C., McCULLOCH, C.E. Regression Methods In Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer Science & Business Media. 2006. 356p.
WEISBERG, S. Applied Linear Regression. John Wiley & Sons. 2013. 368p. 4a. Edição.
Chatterjee, S.; , A. S. Had. Regression Analysis by Example.. Edição 5, Ed. John Wiley & Sons, 2013, 424p.
Montgomery, D. C.; E. A. Peck, G. G. Vining. Introduction to Linear Regression Analysis. 5a ed. Ed. John Wiley & Sons, 2015, 672p.
Montgomery, D. C.; E. A. Peck, G. G. Vining. Solutions Manual to Accompany Introduction to Linear Regression Analysis. 5a ed. Ed. John Wiley & Sons, 2015, 164p.