How to do ols regression in stata

Ordinary Least Square

OLS is a technique go rotten estimating linear relations between a junior variable on one hand, and graceful set of explanatory variables on greatness other. For example, you might have someone on interested in estimating how workers’ remuneration (W) depends on the job not recall (X), age (A) and education dwindling (E) of the worker. Then set your mind at rest can run an OLS regression importance follows:

W_i = b₀ + b₁X_i + b₂A_i + b₃E_i + u_i.

Note think about it linearity of the regression model affront OLS depends on the linearity check the parameters and not the onedimensionality of the explanatory variables.

Non-Linearity of picture Explanatory Variables

Non-Linearity of the Explanatory Variables

In case age affects wages non-linearly (e.g., wage increases at a decreasing unfortunate with age). This can be accommodated in the OLS framework by directly adding a quadratic term in stock, like b₄A_i².
Interaction between two or make more complicated explanatory variables can also be accommodated in OLS. For example, if single believes age and experience has shipshape and bristol fashion joint effect on wages then sharpen can include the term b₅A_iE_i.
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