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A mathematical technique used to explain and/or predict. The general form is Y = a + bX + u, where Y is the variable that we are trying to predict; X is the variable that we are using to predict Y, a is the intercept; b is the slope, and u is the regression residual. The a and b are chosen in a way to minimize the squared sum of the residuals. The ability to fit or explain is measured by the R-square.

Regression Analysis

In statistics, the analysis of variables that are dependent on other variables. Regression analysis often uses regression equations, which show the value of a dependent variable as a function of an independent variable. For example, a regression could take the form:

y = a + bx

where y is the dependent variable and x is the independent variable. In this case, the slope is equal to b and a is the intercept. When plotted on a graph, y is determined by the value of x. Regression equations are charted as a line and are important in calculating economic data and stock prices.


(1) A statistical technique for creating a mathematical equation to explain the relationship between known variables so that the model can be used to predict other variables when one has insufficient data. Multiple regression analysis is the basis of computerized automatic valuation models (AVM) employed instead of appraisals by many mortgage lenders. (2) An appraisal principle that if properties of relatively unequal value are located near each other, the one with the lower value will depress the value of the other. (3) A withdrawal of the sea from the land due to an uplift of the land or a drop in sea level.

References in periodicals archive ?
Hand Book of Regression and Modeling: Applications for the Clinical and Pharmaceutical Industries, pp.
However, correlation coefficients found to be strong between pairs of body measurements may lead to a difficult case, like multicollinearity problem in multiple linear regressions and, thus the biased regression coefficient found for each of body measurements may induce inadvisable results and interpretation in the prediction of body weight (Eyduran et al.
The information one sees during regression comes from his or her subconscious mind, which the person may not be aware of in a fully awake state.
In the construction of the decision or regression tree diagram, CART, CHAID and Exhaustive CHAID algorithms are non-parametric techniques applied for performing the statistical analysis of nominal, ordinal and scale (continuous) variables (Ali et al.
9 The functional relation between the two random variables can be non-linear, also called non-linear regression (polynomial of degree 2, 3; exponential; logarithmical, etc.
The task will be solved by means of multiple regression model [10], [11].
A simple answer to this question is the different assumptions between the univariate and multiple regression models.
Miu and Risa, two young tea-shop workers, are quickly introduced before Risa agrees to help her friend understand regression analysis.
Hence, two separate regression analyses were made to accommodate the two dependent variables.
Researchers conducted the meta-analysis of 14 studies in 10,098 patients who had histologic regression of a primary melanoma and whose sentinel lymph node status was available.