Regression analysis is a statistical method that allows you to examine the relationship between two or more variables of interest. Regression Analysis form of statistical test is only possible with interval or ratio data. If an independent variable and a dependent variable are placed on the two axis of a graph with the actual data then scattered on the graph, it is possible to draw a line through the resulting points in a way that minimizes the distance between the points. The resulting line, which may be straight or curved, is a regression line. Any particular value for the dependent variable can then be predicted by multiplying the value of independent variable by the regression coefficient, a number which determines the slope of the line. Regression is a measure of association between two quantitative variables.

Regression analysis is done for one of two purposes: In order to predict the value of the dependent variable for individuals for whom some information concerning the explanatory variables is available, or in order to estimate the effect of some explanatory variable on the dependent variable.

Regression analysis is used in stats to find trends in data. You might guess that there's a connection between how much you eat and how much you weigh; regression analysis can help in quantifying that. In genetics, regression is the tendency of parents who are exceptional in respect of some partially inherited character to produce offspring in which this character is closer to the mean value for the general population. Frequently referred to as regression to the mean. In psychology, regression is the process of returning or a tendency to return to an earlier stage of development through hypnosis, psychoanalysis, mental illness.

**
On Dummy Variable
Regression Analysis - A Description and Illustration of the Method **

Jerry L.L. Miller, Maynard L. Erickson.

This paper is concerned with the description of a specialized form of linear regression
analysis commonly known as "dummy variable" regression analysis. To show the
relationship between "dummy variable" regression analysis and other multivariate analysis techniques. (1) to give
illustrations and examples of problems to which this type of multiple-regression analysis
might be applied; (2) to show how "dummy variable" regression analysis is both
similar to and different from other multivariate techniques.

**
Confronting Sociological Theory with Data: Regression Analysis,
Goodman's Log-Linear Models and Comparative Research** - Bernice A.
Pescosolido, Jonathan Kelley

Goodman's log-linear procedure has been advocated as a `better' way of dealing with
certain types of comparisons. There is some question as to their applicability in
answering theoretical questions typically posed in comparative sociological research.
Using a Monte Carlo simulation, we set up a typical but hypothetical set of data that
would be appropriate for testing comparative theories of socioeconomic achievement or
intergenerational mobility.

**Comparison of Multiple Regression Analysis and Configural Analysis Techniques for
Developing Base Expectancy Tables**

Dean V. Babst, Don M. Gottfredson, Kelley B. Ballard, JR, Uniform Parole Reports Project,
National Parole Institutes, NCCD, 1966-67.

This study compares two statistical techniques, multiple regression and configural
analysis, used in developing parole prediction tables, according to their ability to (1)
differentiate be tween offenders who violate parole and those who do not, (2) predict
violators from among a new group of parolees, and (3) assist administrators and
researchers.

J. Scott Long and Jeremy Freese's (2003) **Regression Models for Categorical
Dependent Variables Using Stata**, Revised Edition.