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The measure of standard error can also be applied to the parameter estimates resulting from linear regressions. For example, consider the following linear regression equation that describes the relationship between education and wage: WAGEi = EDUCi where WAGEi is the hourly wage of person i (i.e., any specific person) and EDUCi is the number of years of education for that same person. The residual Ei encompasses other factors that influence wage, and is assumed to be uncorrelated with education and have a mean of zero. Suppose that after collecting a cross-sectional data set, you run an OLS regression to obtain the following parameter estimates: WAG Ei -11.1 + 4.3 EDUCi If the standard error of the estimate of is 1, then the true value of 31 lies between and . As the number of observations in a data set grows, nu would expect this range to In size.