# Link between different standard error formulas

I am currently going through "Introductory Econometrics - A modern approach" by Wooldridge and have a question about the standard error formula.

The textbook gives the following equations as an estimator of the standard error in the case of simple linear regression where $$y_i = \beta_0 + \beta_1 x$$:

$$SE(\hat\beta_1) = \frac{\hat \sigma }{\sqrt {SST_x}}$$

where:

$$\hat \sigma = \sqrt \frac{SSR}{n-2}$$

$$SST_x = \sum_{i=1}^n (x_i - \bar x)^2$$

By using the above formulas I was able to derive the correct estimate of the standard error as reported by stats packages, however I came across another formula, which is:

$$SE = \frac{\sigma}{\sqrt n}$$

Can someone please explain the link between the two, as I was not able to find any material related to their dependency, only separate explanations.

• Is it possible that last formula is giving the population standard error. $\sigma$ has no hat. $\hat{\sigma}$ would denote an estimated standard deviation, $\sigma$ could denote the population (true / unknown) standard error). Dec 2, 2019 at 14:24
• Please say more about where you found the second formula, with a link to it if possible. It looks like the formula for the standard error of the mean of $n$ observations rather than the standard error for a coefficient in linear regression.
– EdM
Dec 2, 2019 at 14:55
• @Edm yes, the second formula represents the standard error of the mean. I was wondering if there is any relation between the two. Dec 2, 2019 at 14:59

Any statistic, a "quantity computed from values in a sample", can have a standard error. The standard error of a statistic is "the standard deviation of its sampling distribution or an estimate of that standard deviation".* That is, if you repeated the same experiment a large number of times, the standard error provides a measure of the reproducibility of the value of the computed statistic.

The last formula you wrote, $$SE = \frac{\sigma}{\sqrt n}$$, is most strictly the standard error of the mean value (SEM) for samples of size $$n$$ of a single variable that has a true standard deviation $$\sigma$$ of its values in the population from which you are sampling. More typically, you have an estimate $$s$$ of the standard deviation based on your sample,** and calculate $$SEM=\frac{s}{\sqrt n}$$. (I prefer to use $$SEM$$ for standard errors of mean values of single variables, and reserve $$SE$$ for standard errors of other statistics.)

In a simple linear regression as in your first equation you have two variables of interest, whose jointly observed values provide the statistic of the estimated slope, $$\hat \beta_1$$ in your nomenclature. You can write this sample-based estimate as proportional to the ratio of the standard errors of the means of the y and x values, with the proportionality constant equal to their sample correlation coefficient.

With respect to the standard error of the slope estimate, note that you could choose to write $$\sqrt {SST_x}$$ as $$\sqrt n SEM_x$$ (where $$SEM_x$$ is the standard error of the mean of the $$x$$ values). Then you could write:

$$SE(\hat\beta_1) = \frac{\sqrt {SSR}}{\sqrt {n (n-2)} SEM_x}$$

which shows that (at constant SSR, sum of squares of residuals) the standard error of your estimate of the slope is lower if the distribution of $$x$$ values, represented by $$SEM_x$$, is wider. (That's why in experimental design it can be helpful to arrange for a wide range of values for the independent variable $$x$$.) Other than that, however, there is no simple general dependency between the standard error of the estimate of the slope in simple linear regression and the standard errors of the $$x$$ or $$y$$ values separately. What matters is the linear relationship between $$y$$ and $$x$$ and how successfully that relationship leads to small residuals, as represented by $$SSR$$.

*Sometimes you need to read carefully to infer whether an author is describing a true population value or a sample-based estimate.

**Sample-based estimates are often distinguished by a "hat" symbol, like $$\hat \sigma$$, but $$s$$ has long-standing use to represent a sample-based standard deviation for values of a single variable.

Apart from the hat over the $$\sigma$$ in the first instance, these are examples of a common formula. In both cases there is a square root of a fraction; the numerator is a variance $$\sigma^2$$ or its estimated value $$\hat \sigma^2;$$ and the denominator--as it turns out--can be understood as the squared length of a vector of explanatory values in the simplest kind of regression model. Compare the red equalities in the two highlighted equations below.

Consider the model $$y_i = \beta x_i + \varepsilon_i\tag{1}$$ where $$\beta$$ is to be estimated from data $$(x_i,y_i)$$ and the $$\varepsilon_i$$ are assumed to be uncorrelated zero mean random variables all of variance $$\sigma^2$$ (which is not known). The Ordinary Least Squares estimate is

$$\hat \beta = \frac{\sum_i x_i y_i}{\sum_i x_i x_i} = \frac{\sum_i x_i y_i}{|x|^2}$$

(using a simplified vector notation for the sum of squares of the $$x_i$$ in the denominator, which we may interpret as the squared Euclidean length of the vector $$(x_i)$$). Because

$$\operatorname{Var}(y_i) = \operatorname{Var}(\beta x_i + \varepsilon_i) = \operatorname{Var}(\varepsilon_i) = \sigma^2$$

and the covariances of distinct $$y_i$$ and $$y_j$$ are zero, compute that

$$\operatorname{Var}(\hat\beta) = \operatorname{Var}\left(\frac{\sum_i x_i y_i}{\sum_i x_i x_i}\right) = \sum_i \left(\frac{x_i}{|x|^2}\right)^2 \sigma^2 = \frac{|x|^2}{\left(|x|^2\right)^2}\sigma^2 = \frac{\sigma^2}{|x|^2}.\tag{2}$$

In the special case where $$x_i=1$$ for all $$i,$$ $$|x|^2 = \sum_i 1^2 = n$$ and the model is

$$y_i = \beta + \varepsilon_i$$

with

$$\hat \beta = \frac{\sum_i (1)y_i}{|x|^2} = \frac{\sum_i y_i}{n} = \bar y,$$

whence

$$\operatorname{Var}(\bar y) = \color{red}{\operatorname{Var}(\hat\beta) = \frac{\sigma^2}{|x|^2}} = \frac{\sigma^2}{n}.$$

Taking square roots gives the second formula in the question. Bear in mind the origin of the denominator $$n:$$ it is the squared length of the vector of explanatory variables $$(x_i = 1).$$

The first formula arises by fitting the model

$$y_i = \alpha + \beta x_i + \varepsilon_i = \alpha z_i + \beta x_i + \varepsilon_i$$

(where $$z_i=1$$ for all $$i$$) in two steps. In the first step, both $$y$$ and $$x$$ are fit to $$z$$ using the simple model $$(1)$$ and then are replaced by their residuals. (Please see https://stats.stackexchange.com/a/46508/919 and https://stats.stackexchange.com/a/113207/919 for the justification and explanations of this fundamental step, which is called "controlling for" or "taking out the effect of" the variable $$z.$$)

In other words, $$y_i$$ is replaced by $$y_{\cdot i}=y_i - \bar y$$ and $$x_i$$ is replaced by $$x_{\cdot i}=x_i - \bar x.$$ Because this removes all the discernible effects of $$z,$$ $$\alpha$$ is no longer needed and we are left to fit the model

$$y_{\cdot i} = y_i - \bar y = \beta (x_i - \bar x) + \varepsilon_i =\beta x_{\cdot i} + \varepsilon_i.$$

This, too is in the form of model $$(1).$$ Formula $$(2)$$ states

$$\color{red}{\operatorname{Var}(\hat \beta) = \frac{\sigma^2}{|x_\cdot|^2}} = \frac{\sigma^2}{\sum_i \left(x_i - \bar x\right)^2}.$$

Taking square roots gives the first formula in the question, except here we are using $$\sigma$$ instead of $$\hat \sigma.$$

This leads us to the last unresolved issue: when you know (or assume the value of) $$\sigma,$$ there's nothing left to do: we have our standard errors of estimate. But when you don't know $$\sigma,$$ about the only thing you can do (short of an infinite regress where you try to estimate the standard error of $$\hat \sigma$$ and so on) is to replace the occurrence of $$\sigma^2$$ in formula $$(2)$$ by its estimate $$\hat\sigma^2.$$