You can use the following methods to extract the residual standard error along with the standard error of the individual regression coefficients from the lm() function in R:

**Method 1: Extract Residual Standard Error**

#extract residual standard error of regression model summary(model)$sigma

**Method 2: Extract Standard Error of Individual Regression Coefficients**

#extract standard error of individual regression coefficients sqrt(diag(vcov(model)))

The following example shows how to use each method in practice.

**Example: Extract Standard Errors from lm() in R**

Suppose we fit the following multiple linear regression model in R:

#create data frame df <- data.frame(rating=c(67, 75, 79, 85, 90, 96, 97), points=c(8, 12, 16, 15, 22, 28, 24), assists=c(4, 6, 6, 5, 3, 8, 7), rebounds=c(1, 4, 3, 3, 2, 6, 7)) #fit multiple linear regression model model <- lm(rating ~ points + assists + rebounds, data=df)

We can use the **summary()** function to view the entire summary of the regression model:

#view model summary summary(model) Call: lm(formula = rating ~ points + assists + rebounds, data = df) Residuals: 1 2 3 4 5 6 7 -1.5902 -1.7181 0.2413 4.8597 -1.0201 -0.6082 -0.1644 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 66.4355 6.6932 9.926 0.00218 ** points 1.2152 0.2788 4.359 0.02232 * assists -2.5968 1.6263 -1.597 0.20860 rebounds 2.8202 1.6118 1.750 0.17847 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 3.193 on 3 degrees of freedom Multiple R-squared: 0.9589, Adjusted R-squared: 0.9179 F-statistic: 23.35 on 3 and 3 DF, p-value: 0.01396

The residual standard error of the model is 3.193 and each of the standard errors for the individual regression coefficients can be seen in the **Std. Error** column of the output.

To only extract the residual standard error for the model, we can use the following syntax:

#extract residual standard error of regression model summary(model)$sigma [1] 3.19339

And to only extract the standard errors for each of the individual regression coefficients, we can use the following syntax:

#extract standard error of individual regression coefficients sqrt(diag(vcov(model))) (Intercept) points assists rebounds 6.6931808 0.2787838 1.6262899 1.6117911

Notice that these values match the values that we saw earlier in the entire regression output summary.

**Related: **How to Interpret Residual Standard Error

**Additional Resources**

The following tutorials explain how to perform other common tasks in R:

How to Perform Simple Linear Regression in R

How to Perform Multiple Linear Regression in R

How to Create a Residual Plot in R