**Introduction**

ETA squared is a measure of effect size that Stata can easily estimate after **regression** or **ANOVA** procedures. In this blog, we’ll show you how to use Stata to calculate ETA squared for entire models and individual predictor variables.

**Load Data**

Let’s load Stata’s automobile dataset:

sysuse auto

describe

**ETA Squared after Regression **

Try the following code:

regress mpg weight i.foreign

Adjusted R squared tells you that the regression explains 65.32% of the variation in mpg, but there aren’t separate effect sizes for the predictors of weight and foreign origin. Now try:

estat esize

Helpfully, we learn that over 60% of the variation in mpg is explained by the weight of the car; the foreign origin of the car is much less explanatorily powerful. One of the advantages of the ETA squared postestimation command is the availability of ETA squared for each predictor.

**ETA Squared after ANOVA**

Let’s try an ETA squared postestimation after an ANOVA conducted on another dataset:

sysuse voter

anova pop candidat inc

Here, we are trying to model population as a function of an area’s preferred candidate and income level:

Next, enter:

estat esize

And you get back:

So the variation in population is roughly equally explained by candidate selection and income.

BridgeText can help you with all of your **statistical analysis needs**.