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| Regression |
Introduction
Regression techniques enable you to investigate the relationship between a dependent variable (also called a response variable) and one or more explanatory variables (also called predictor, or independent, variables). In linear regression, the dependent variable is modeled as a linear function of the quantitative independent variables. For example, you can write the simple linear regression equation as
- Y = b0 + b1 X
where Y represents the single dependent variable, X is the explanatory variable, and b0 and b1 are regression coefficients.
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Figure 11.1: Regression Menu
The Analyst Application enables you to perform simple linear regression, multiple linear regression and logistic regression. In the Simple linear regression task, you model your dependent variable using a single explanatory variable. In the Linear regression task, you model your dependent variable using one or more explanatory variables. In the Logistic regression task, the dependent variable is discrete, and you model the variable using one or more explanatory variables.
The examples in this chapter demonstrate how you can use the Analyst Application to perform simple linear regression, multiple linear regression, and logistic regression.
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