Intermediate Regression in R
Richie Cotton
Data Evangelist at DataCamp
Coefficient of determination (R-squared): how well the linear regression line fits the observed values.
Residual standard error (RSE): the typical size of the residuals.
library(dplyr)
library(broom)
mdl_mass_vs_length %>%
glance() %>%
pull(r.squared)
0.8226
mdl_mass_vs_species %>%
glance() %>%
pull(r.squared)
0.7163
mdl_mass_vs_both %>%
glance() %>%
pull(r.squared)
0.9694
glance()
, it's the adj.r.squared
element.library(dplyr)
library(broom)
mdl_mass_vs_length %>%
glance() %>%
select(r.squared, adj.r.squared)
r.squared adj.r.squared
<dbl> <dbl>
1 0.8226 0.8212
mdl_mass_vs_species %>%
glance() %>%
select(r.squared, adj.r.squared)
r.squared adj.r.squared
<dbl> <dbl>
1 0.7163 0.7072
mdl_mass_vs_both %>%
glance() %>%
select(r.squared, adj.r.squared)
r.squared adj.r.squared
<dbl> <dbl>
1 0.9694 0.9682
library(dplyr)
library(broom)
mdl_mass_vs_length %>%
glance() %>%
pull(sigma)
152.1
mdl_mass_vs_species %>%
glance() %>%
pull(sigma)
313.6
mdl_mass_vs_both %>%
glance() %>%
pull(sigma)
103.4
Intermediate Regression in R