Course detail

LCE5801 - Regression and Covariance

Credit hours

In-class work
per week
per week
15 weeks
120 hours

Clarice Garcia Borges Demetrio
Silvio Sandoval Zocchi
Taciana Villela Savian

The aim of the course of regression and covariance is to provide a theoretical basis of regression and
applications in many scientific areas. At the end of the course the student will be able to use the
methods for parameter estimation in regression and covariance models, perform diagnostic and
residuals analysis, use the variable selection methods, perform hypothesis tests about regression
parameters and obtain confidence and prediction intervals.

Simple linear regression model: statistical model, least squares estimation, properties of estimators,
hypothesis tests and confidence intervals of the parameters, prediction intervals. Multiple linear
regression: statistical model, least squares estimation, properties of estimators, hypothesis tests and
confidence intervals of the parameters. Residual analysis and diagnostics. Maximum likelihood
estimation of the bivariate normal distribution parameters. Simple, partial and multiple correlation
coefficients: estimation, hypothesis tests and confidence intervals. Orthogonal polynomial regression.
Variables selection. Parallelism tests. Analysis of covariance.

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.
CHATTERJEE, S.; A. S. HAD. Regression Analysis by Example.. Edição 5, Ed. John Wiley & Sons, 2013,
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.
FARAWAY, J. J. Linear Models with R. CRC Press; 2nd ed. Edição. 2014. 286p.
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.; E. A. PECK, G. G. VINING. Introduction to Linear Regression Analysis. 6a ed. Ed.
John Wiley & Sons, 2021, 704p.
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.
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
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.
Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer Science & Business
Media. 2 edition. 2011. 512p.
WEISBERG, S. Applied Linear Regression. John Wiley & Sons. 2013. 368p. 4a. Edição