That is impossible.If dependent variable is continuous,that is not necessary to use logistic distribution to map discrete value to infinite(negative and positive ).If dependent variable is continuous ,GLM is suited for that.
The appearance of logistic model is just to solve the problem of discrete value of dependent variable.
While logistic regression involves a binomial or multinomial response, it is still reasonable to fit a logistic curve to a continuous response that ranges from 0 to 1. This can be done by fitting a nonlinear model such as p=1/(1+exp(-xbeta)), where xbeta is a linear function of your predictors. Also, Collett (2003, pp 329-330) suggests a quasi-likelihood approach that fortunately is similar to using events/trials syntax in PROC LOGISTIC with the proportions as the events variable, with the trials variable equal to 1, and with the SCALE=DEVIANCE option in the MODEL statement.