Explaining house price with size & condition

Modeling with Data in the Tidyverse

Albert Y. Kim

Assistant Professor of Statistical and Data Sciences

Refresher: Exploratory data analysis

library(dplyr)
library(moderndive)

# log transform variables
house_prices <- house_prices %>%
  mutate(
    log10_price = log10(price),
    log10_size = log10(sqft_living)
  )

Refresher: Exploratory data analysis

# Group mean & sd of log10_price and counts
house_prices %>% 
  group_by(condition) %>% 
  summarize(mean = mean(log10_price), 
            sd = sd(log10_price), n = n())

# A tibble: 5 x 4
  condition  mean    sd     n
  <fct>     <dbl> <dbl> <int>
1 1          5.42 0.293    30
2 2          5.45 0.233   172
3 3          5.67 0.224 14031
...

House price, size, and condition

Parallel slopes model

House price, size, and condition relationship

# Fit regression model using formula of form: y ~ x1 + x2
model_price_3 <- lm(log10_price ~ log10_size + condition,
                    data = house_prices)

# Output regression table
get_regression_table(model_price_3)

# A tibble: 6 x 7
  term       estimate std_error statistic p_value lower_ci...
  <chr>         <dbl>     <dbl>     <dbl>   <dbl>    <dbl>...
1 intercept     2.88      0.036     80.0    0        2.81...
2 log10_size    0.837     0.006    134.     0        0.825...
3 condition2   -0.039     0.033     -1.16   0.246   -0.104...
4 condition3    0.032     0.031      1.04   0.3     -0.028...
...

Let's practice!

Modeling with Data in the Tidyverse