# Get error value from linear regression function lm

I have a linear regression problem that I solved with:

```
m=lm(value ~ mean, data=d)
```

and from this value i you can get R2 and the regression equation.

but I want to get a standard error (installation error). i was able to see the value, but i don't know how to get it to store it in the dataframe.

I get the value using `summary(m)`

and the result looks something like this:

```
Call:
lm(formula = value ~ mean, data = d)
Residuals:
Min 1Q Median 3Q Max
-25.000 -15.909 -2.124 14.596 44.697
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.500e+01 1.064e+00 23.49 <2e-16 ***
mean -1.759e-06 1.536e+00 0.00 1
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 16.85 on 1298 degrees of freedom
Multiple R-squared: 1.01e-15, Adjusted R-squared: -0.0007704
F-statistic: 1.311e-12 on 1 and 1298 DF, p-value: 1
```

so the question is, how can I access these values?

Thank you

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The function `summary`

simply returns a list R.

```
##Generate some dummy data
x = runif(10);y = runif(10)
m = summary(lm(y ~ x))
```

We can use the normal list syntax to retrieve what we want. For example,

```
m[[4]]
```

Returns the model data frame fits

```
R> m[[4]]
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.44265 0.2443 1.8123 0.1075
x 0.07066 0.4460 0.1584 0.8781
```

and `m[[6]]`

returns`Residual standard error`

```
R> m[[6]]
[1] 0.2928
```

There are several handy functions such as `coefficients(m)`

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