Database Reference
In-Depth Information
Key Concepts
Categorical Variable
Linear Regression
Logistic Regression
Ordinary Least Squares (OLS)
Receiver Operating Characteristic (ROC) Curve
Residuals
In general, regression analysis attempts to explain the influence that a set of
variables has on the outcome of another variable of interest. Often, the outcome
variable is called a
dependent variable
because the outcome depends on the
other variables. These additional variables are sometimes called the
input
variables
or the
independent variables.
Regression analysis is useful for
answering the following kinds of questions:
• What is a person's expected income?
• What is the probability that an applicant will default on a loan?
Linear regression is a useful tool for answering the first question, and logistic
regression is a popular method for addressing the second. This chapter examines
these two regression techniques and explains when one technique is more
appropriate than the other.
Regression analysis is a useful explanatory tool that can identify the input variables
that have the greatest statistical influence on the outcome. With such knowledge
and insight, environmental changes can be attempted to produce more favorable
values of the input variables. For example, if it is found that the reading level of
10-year-old students is an excellent predictor of the students' success in high school
and a factor in their attending college, then additional emphasis on reading can be
considered, implemented, and evaluated to improve students' reading levels at a
younger age.
