model
#> # A tibble: 1 x 11
#> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.718 0.709 66.9 76.5 9.38e-10 2 -179. 364. 368.
#> # … with 2 more variables: deviance <dbl>, df.residual <int>
coefficients
#> # A tibble: 2 x 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 581. 41.7 13.9 1.26e-14
#> 2 mpg -17.4 1.99 -8.75 9.38e-10
observations
#> # A tibble: 32 x 10
#> .rownames disp mpg .fitted .se.fit .resid .hat .sigma .cooksd
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Mazda RX4 160 21 215. 12.0 -54.9 0.0320 67.2 1.15e-2
#> 2 Mazda RX… 160 21 215. 12.0 -54.9 0.0320 67.2 1.15e-2
#> 3 Datsun 7… 108 22.8 183. 13.0 -75.5 0.0378 66.5 2.60e-2
#> 4 Hornet 4… 258 21.4 208. 12.1 50.1 0.0328 67.3 9.83e-3
#> 5 Hornet S… 360 18.7 255. 12.1 105. 0.0330 65.0 4.35e-2
#> 6 Valiant 225 18.1 265. 12.5 -40.4 0.0348 67.6 6.82e-3
#> 7 Duster 3… 360 14.3 332. 16.5 28.4 0.0610 67.8 6.22e-3
#> 8 Merc 240D 147. 24.4 156. 14.6 -8.91 0.0477 68.0 4.68e-4
#> 9 Merc 230 141. 22.8 183. 13.0 -42.7 0.0378 67.5 8.32e-3
#> 10 Merc 280 168. 19.2 246. 12.0 -78.6 0.0320 66.4 2.36e-2
#> # … with 22 more rows, and 1 more variable: .std.resid <dbl>