Linear Regression

Linear Regression

Washington and ManneringChapter 3

Assumptions• Linear relationship between a continuous

dependent variable with one or more independent variables

Inference in Regression analysis

Variables significance

Standardized regression

Indicator variables

Ordered response

Regression assumptionsLinearity

Nomality?

Outliers

Quantifying outliers

Logistic Regression

The goal of logistic regression, much like linear regression, is to identify the best fitting model that describes the relationship between a binary dependent variable and a set of independent or explanatory variables. In contrast to linear regression, the dependent variable is the populationproportion or probability (P) that the resulting outcome is equal to 1. It is important to note that the parameters obtained for the independent variable can be used to estimate odds ratios for each of the independent variables in the model.