Applied Linear Regression Models By Kutner Nachtsheim And Neter

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Práxedes Jamal

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Aug 3, 2024, 5:57:21 PM8/3/24

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1. Understanding the context for simple linear regression.
2. How to evaluate simple linear regression models
3. How a simple linear regression model is used to estimate and predict likely values
4. Understanding the assumptions that need to be met for a simple linear regression model to be valid
5. How multiple predictors can be included into a regression model
6. Understanding the assumptions that need to be met when multiple predictors are included in the regression model for the model to be valid
7. How a multiple linear regression model is used to estimate and predict likely values
8. Understanding how categorical predictors can be included into a regression model
9. How to transform data to deal with problems identified in the regression model
10. Alternative methods for estimating a regression line besides using ordinary least squares
11. Understanding regression models in time dependent contexts
12. Understanding regression models in non-linear contexts

The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications (with and without software), questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component is geared towards solving problems and exercises, including discussion and interpretation of results. A set of exercises to be completed independently in extra-classroom context is also proposed.