# Linearized Estimates

Menu location: **Analysis_Regression and Correlation_Linearized Estimates**.

This section provides simple (one predictor) regression estimates for three linearized functions (exponential, geometric and hyperbolic) by an unweighted least squares method.

These functions should be used only to indicate that a more robust fit of the selected model is worth investigating with your data.

__Exponential__

Data are linearized by logarithmic transformation of the predictor (x) variable. Simple linear regression of Y vs. ln(x) gives a = ln(intercept) and b = slope for the function:

__Geometric__

Data are linearized by logarithmic transformation of both variables. Simple linear regression of ln(Y) vs. ln(x) gives a = ln(intercept) and b = slope for the function:

__Hyperbolic__

Data are linearized by reciprocal transformation of both variables. Simple linear regression of 1/Y vs. 1/x gives a = slope and b = intercept for the function:

The standard error of the estimate is given for each of these regressions but please note that the errors of your outcome/response variable might not be from a normal distribution.

This section of StatsDirect is intended only for those who are familiar with regression modelling and who use these linearized estimates as a springboard for further modelling. Generic modelling software such as R, S+ or GLIMM can be used for further modelling, which is best done by a Statistician.