How should one interpret the standard errors in the output of regression analysis?

Maribel Vang

Maribel Vang

Answered question

2022-10-30

How should one interpret the standard errors in the output of regression analysis?

Answer & Explanation

Reese Hobbs

Reese Hobbs

Beginner2022-10-31Added 13 answers

Let σ be the standard deviation. Then the standard error is defined as
where n is the number of observations in your sample.
So clearly the standard error linearly depends on the standard deviation, but is not quite the same. The more data you gather, the smaller the standard error for given standard deviation.
The standard error is a measure of variance of your estimate (like a mean or an OLS coefficient). For small variance in the data (small σ) or a lot of independent observations (large n), the standard error becomes very small, so you are confident the true parameter value is in a small range around your estimate. This makes sense: As you get more data (larger n), idiosyncratic noise will have less and less of an effect on your estimate as it gets averaged out over a lot of independent draws, so the standard error decreases.

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