![]() We can include a dummy variable as a predictor in a regression analysis as shown below. Let’s use the variable yrrnd as an example of a dummy variable. Asking for robust standard errors does not amount to robust regression in the sense just discussed, and outliers and long tails in any of the variables will have exactly the effect they have otherwise on coefficient estimates the difference is that your standard errors may differ, affecting any quantities that depend on them. The simplest example of a categorical predictor in a regression analysis is a 0/1 variable, also called a dummy variable. The Stata option ( not command) robust implements robust (Huber-White-sandwich) standard errors that are offered as more honest standard errors in the face, principally, of heteroscedasticity. The thread just cited offers an independent and authoritative opinion in agreement. Use the following command to load the dataset: s ysuse auto. It remains visible as a matter of continuity but even among Stata programs it has (in my opinion) been superseded long since by alternatives. For this example we will use the Stata built-in dataset called auto. Li's regression: which should I use, and when?. ![]() We can tell stata to treat group as a categorical variable by adding i. Its regression output is highly informative and it is one of the most. What it does is well documented in the Stata manuals and also discussed elsewhere in this forum at When I analysed this data, using regress Y x, I got the following outputa. Stata is a statistical software used for data analysis, management and visualization. The Stata command rreg implements one flavour of robust regression that is (in a very limited sense) robust to outliers in the data. You are confusing quite different things, but the main reason for your confusion is that terminology in statistical science is indeed inconsistent here.
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