Let m be the number of independent variables, `ivs.' This function returns a vector of length m which are the coefficients of a linear equation that best predicts the dependent variable, `dv,' in the least squares sense. It also returns, as the second value, the sum of squared deviations of the data from the fitted model, aka SSE, aka chi-square. The third value is the number of degrees of freedom for the chi-square, if you want to test the fit. This function returns an intermediate amount of information. Consider using the sibling functions -minimal and -verbose if you want less or more information.