Course detail

LCE5801 - Regression and Covariance


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 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.

Content
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.

Bibliography
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Collinearity. John Wiley & Sons, 2005. 310p.
CHATTERJEE, S.; A. S. HAD. Regression Analysis by Example.. Edição 5, Ed. John Wiley & Sons, 2013,
424p.
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2009. 280p.
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