library(dplyr)
library(broom)

iris.regressao <- iris %>%
  group_by(Species) %>%
  do(regressao =
    lm(Sepal.Length ~ Sepal.Width + Petal.Length + Petal.Width, data=.))

tidy(iris.regressao, regressao)

# A tibble: 12 x 6
# Groups:   Species [3]
      Species         term   estimate  std.error  statistic      p.value
       <fctr>        <chr>      <dbl>      <dbl>      <dbl>        <dbl>
 1     setosa  (Intercept)  2.3518898 0.39286751  5.9864707 3.034183e-07
 2     setosa  Sepal.Width  0.6548350 0.09244742  7.0833236 6.834434e-09
 3     setosa Petal.Length  0.2375602 0.20801921  1.1420107 2.593594e-01
 4     setosa  Petal.Width  0.2521257 0.34686362  0.7268727 4.709870e-01
 5 versicolor  (Intercept)  1.8955395 0.50705524  3.7383295 5.112246e-04
 6 versicolor  Sepal.Width  0.3868576 0.20454490  1.8913091 6.488965e-02
 7 versicolor Petal.Length  0.9083370 0.16543248  5.4906811 1.666695e-06
 8 versicolor  Petal.Width -0.6792238 0.43538206 -1.5600639 1.255990e-01
 9  virginica  (Intercept)  0.6998830 0.53360089  1.3116227 1.961563e-01
10  virginica  Sepal.Width  0.3303370 0.17432873  1.8949086 6.439972e-02
11  virginica Petal.Length  0.9455356 0.09072204 10.4223360 1.074269e-13
12  virginica  Petal.Width -0.1697527 0.19807243 -0.8570233 3.958750e-01