a type of
regression analysis used when a
dependent variable with values above or below a certain threshold takes on the value of that threshold; that is, the exact value of the variable is unknown or ignored (see
censored data). For example, in an analysis of aptitude in college students, the dependent variable could be a score on a standardized aptitude test that has an upper limit of 500 (the threshold value). Students who answer all questions on the test correctly receive a score of 500, even though these students may not have equal aptitude. In such a case, the influence of
independent variables, such as reading and math scores, on the dependent variable of academic aptitude is more appropriately studied with Tobit regression analysis than with ordinary
least squares regression because the threshold values would bias the slope of the obtained
regression line. Also called
censored regression;
Tobit model. [named by analogy with probit analysis, after James Tobin (1918–2002), U.S. economist]