Properties Data for Chi-square Test of Independence

Contains the type of properties and the buyer types. Buyer categories are: "single male", "single female", "married couple" and "family".

The type of property these buyers purchased were sorted into four categories: "flat", "bungalow" (i.e., a one-storey home), "detached house" and "terrace" (i.e., a block of adjoining houses).

Chi-square test of independence can be used to assess the association between the type of buyer who purchases a property and the type of property that is purchased.

For a worked tutorial on this analysis, see the Datanovia lesson “Chi-Square Test of Independence in R: Are Two Variables Associated?” (https://www.datanovia.com/learn/biostatistics/categorical/chi-square-test-of-independence-in-r).

data("properties")

Format

A data frame with 333 rows and 2 columns (stored as a tibble).

property_type

the type of property purchased: "flat", "bungalow", "detached house" or "terrace".

buyer_type

the type of buyer: "single male", "single female", "married couple" or "family".

Source

A simulated dataset for teaching the chi-square test of independence.

See also

Datanovia tutorial: Chi-Square Test of Independence in R: Are Two Variables Associated?.

Examples

data("properties")
head(properties)
#> # A tibble: 6 × 2
#>   property_type buyer_type 
#>   <fct>         <fct>      
#> 1 flat          single male
#> 2 flat          single male
#> 3 flat          single male
#> 4 flat          single male
#> 5 flat          single male
#> 6 flat          single male

# Chi-square test of independence between buyer type and property type
chisq.test(table(properties$property_type, properties$buyer_type))
#> 
#> 	Pearson's Chi-squared test
#> 
#> data:  table(properties$property_type, properties$buyer_type)
#> X-squared = 82.504, df = 9, p-value = 5.134e-14
#>