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Multiple Linear Regression Analysis

The focus of this chapter is the development of procedures to fit multiple linear regression models.

# Topics Covered:

- Multiple linear regression models
- Estimation of regression coefficients
- Estimation of regression coefficients using matrix notation
- Properties of the least-squares estimators
- Analysis of variance approach to regression analysis
- Discussion of inferences about the regression parameters
- Multiple linear regression model that use qualitative or categorical predictor variables
- Standardized regression coefficients, and multicollinearity and its consequences
- Building regression type prediction models
- Residual analysis
- Certain criteria for model selection
- Basic concepts of logistic regression

# Learning Outcomes:

After studying this chapter, the reader will be able to

- Use the least-squares method to estimate the regression coefficients in a multiple regression model and carry out hypothesis testing to determine which regression coefficients are significant.
- Fit multiple linear regression models to a given set of data when using two or more predictor variables and perform residual analysis to check the validity of the models under consideration.
- Fit multiple linear regression models to a given set of data involving qualitative or categorical predictor variables.
- Determine the presence and possible elimination of multicollinearity.
- Use various criteria such as the coefficient of multiple determination, adjusted coefficient of multiple ...