AE 06: Prediction for MLR

Houses in Levittown

Important

Go to the course GitHub organization and locate your ae-06- to get started.

The AE is due on GitHub by Thursday, September 29 at 11:59pm.

library(tidyverse)
library(tidymodels)
library(knitr)

The data set contains the sales price and characteristics of 85 homes in Levittown, NY that sold between June 2010 and May 2011. Levittown was built right after WWII and was the first planned suburban community built using mass production techniques.

levittown <- read_csv("data/homeprices.csv")

The variables used in this analysis are

• bedrooms: Number of bedrooms
• bathrooms: Number of bathrooms
• living_area: Total living area of the house (in square feet)
• lot_size: Total area of the lot (in square feet)
• year_built: Year the house was built
• property_tax: Annual property taxes (in USD)
• sale_price: Sales price (in USD)

The goal of the analysis is to use the characteristics of a house to understand variability in the sales price.

Linear model

price_fit <- linear_reg() |>
  set_engine("lm") |>
  fit(sale_price ~ bedrooms + bathrooms + living_area + lot_size +
        year_built + property_tax, data = levittown)

tidy(price_fit) |>
  kable(digits = 3)

term	estimate	std.error	statistic	p.value
(Intercept)	-7148818.957	3820093.694	-1.871	0.065
bedrooms	-12291.011	9346.727	-1.315	0.192
bathrooms	51699.236	13094.170	3.948	0.000
living_area	65.903	15.979	4.124	0.000
lot_size	-0.897	4.194	-0.214	0.831
year_built	3760.898	1962.504	1.916	0.059
property_tax	1.476	2.832	0.521	0.604

Prediction

What is the predicted sale price for an individual house in Levittown, NY with 4 bedrooms, 2 bathrooms, 1,800 square feet of living area, 6,000 square foot lot size, built in 1947 with $7,403 in property taxes?

Report the predicted value and appropriate interval.

Note

Fill in the code, then make #| eval: true before rendering the document.

# create tibble for new observation 
new_house <- tibble(
  bedrooms = ____, 
  bathrooms = ____, 
  _____
  )

# prediction + interval
prediction(_________)

• Interpret the interval in the context of the data.

Important

To submit the AE:

• Render the document to produce the PDF with all of your work from today’s class.
• Push all your work to your ae-06- repo on GitHub. (You do not submit AEs on Gradescope).