# Analysis on Categorical, Continuous and Binary Variables

I have a dataset with the following variables:
-Branch (Categorical: Toronto, Montreal, Seattle, etc.)
-Attrition (Binary: Stay, Churn)
-Promotion (Binary: Promoted, Not Promoted)
-Sales Plan (Continuous, this is how much an employee must sell in that month)

I am not sure how best to analyze this. I have done a Fisher Test on the two binary variables, and got a very low p-value. Results below:

Fisher's Exact Test for Count Data
data:  dat2
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.764765 2.538665
sample estimates:
odds ratio
2.114975


But I am not sure how to read the results to say that promoted employees are less likely to leave than non-promoted employees.

Moreover, I'd also like to show that the higher the sales plan, the more likely an employee is to leave. I've been warned recently that I can't do simply corrs between continuous variables and binary variables. I would also like to see if region has any impact on attrition.