In a logistic regression model, we want to predict customers churn (drop-out) probability based on the following independent variables:

- customer's income (nominal with three levels: low, medium, high): income

- customer's marital status ( nominal with two levels: single, married): status

- The number of years the customer has been with the company: n_years

- The number of stores where customer lives: n_stores

The below table shows the coefficients of this logistic regression model:

	coefficient	p-value
intercept	-0.421	0.000
income_low	0.14	0.02
income_high	0.21	0.031
status_single	0.07	0.042
n_years	-0.32	0.003
n_stores	-0.02	0.34

1. Which statement is true about the coefficient on n_years?

   		The churn probability drops by 32% per additional year that a customer stays with the business
   		The odds of churning drops by 32% per additional year that a customer stays with the business
   		The odds of churning drops by 27% per additional year that a customer stays with the business
   		The churn probability drops by 27% per additional year that a customer stays with the business