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Related to regressions: regression analysis, Regression test


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 ?
Summary: Regression is one of the favorite tools in applied statistics.
A total of five regressions are made (from Regression 6 to 10) to investigate the determinants of ROA for all 618 firm-year observations.
In the nine studies considered high quality, patients with regression had a 52% lower likelihood of having a positive sentinel lymph node, while those enrolled in the five studies considered low quality had a 27% lower likelihood of having a positive sentinel lymph node.
No multicollinearity problem for using factor and principal component scores together with multiple regression analysis with much lower RMSE values was detected because of the fact that all VIF values were equal to one, meaning that the most reliable results were taken without multicollinearity problem by comparison with stepwise and multiple regressions, with higher RMSE values.
Patient wait times may have unequal variation due to complex interactions between variables or unobserved exogenous noises that are not accounted for in the regression model.
Total spontaneous regression of advanced Merkel cell carcinoma after biopsy: review and a new case.
There are important limitations, including the necessity of observing regression assumptions, significant non-linear relationships and multiple callinearity between independent variables and the presence of genotype and environment interaction, also replication interactions within the environment (E1) in the combined analysis of variance in national uniform tests, led to be predicting the MLR model no n efficient [1].
The principle used for constructing regression equations which agree well with observed results.
Construct regression models for each dependent variable
The APS regression analyses yielded a model pertaining to the personal teaching efficacy scale (belief that one has the skills and abilities to induce student learning) for each preservice teacher group sample.