Predicția scorului de predare în funcție de gen

Modelare cu date în Tidyverse

Albert Y. Kim

Assistant Professor of Statistical and Data Sciences

Mediile de grup ca predicții

library(ggplot2)
library(dplyr)
library(moderndive)

evals %>%
  group_by(gender) %>%
  summarize(mean_score = mean(score), sd_score = sd(score))

# A tibble: 2 x 3
  gender mean_score sd_score
  <fct>       <dbl>    <dbl>
1 female       4.09    0.564
2 male         4.23    0.522

Calcularea valorilor prezise și a reziduurilor

# Fit regression model:
model_score_3 <- lm(score ~ gender, data = evals)

# Get information on each point
get_regression_points(model_score_3)

# A tibble: 463 x 5
      ID score gender score_hat residual
   <int> <dbl> <fct>      <dbl>    <dbl>
 1     1   4.7 female      4.09    0.607
 2     2   4.1 female      4.09    0.007
 3     3   3.9 female      4.09   -0.193
 4     4   4.8 female      4.09    0.707
 5     5   4.6 male        4.23    0.366
 6     6   4.3 male        4.23    0.066

Histograma reziduurilor

# Fit regression model
model_score_3 <- lm(score ~ gender, data = evals)

# Get regression points
model_score_3_points <- get_regression_points(model_score_3)
model_score_3_points

# Plot residuals
ggplot(model_score_3_points, aes(x = residual)) +
  geom_histogram() +
  labs(x = "residuals",
       title = "Residuals from score ~ gender model")

Histograma reziduurilor

Să exersăm!

Modelare cu date în Tidyverse