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Glossary > R > "Regression" Definition

Regression

Usually linear regression is 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-squared.

Linear regression is used to explain and/or predict. The general form is:

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Regression \ Usually linear regression is 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-squared.

Linear regression is used to explain and/or predict. The general form is:

Regression / usually linear regression is 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-squared.

linear regression is used to explain and/or predict. the general form is: