From the results of this regression, we can look at some observations to begin to make sense of the predictive model. Each row represents an attribute of an employee, and each column represents a different statistical variable. One of the important variables to observe is the p-value, which is found in the last column Pr(>|z|). A p-value that is less than 0.05 means that the attribute is statistically significant.
From the grid, the smallest p-value belongs to the Age attribute- this means that age is significant when determining if an employee will leave. This makes sense, as an older employee may have been at the company longer and will be significantly less likely to quit than a younger employee who may have been there for only a short time. This rationale can be extended to the problem at hand by applying the logic to potential hires. If a potential hire has a work history where multiple recent positions were only held for a few months to a year, while it is common knowledge this is a strong indicator if the candidate will be likely for turnover, it is shown here there is also statistical evidence to support this.
Attributes of interest can be assessed and scored to determine what makes both potential and current employees likely to leave. Some factors that could influence if a current employee may leave may be
By identifying at-risk employees, management can proactively address the issues with these employees as well as any underlying problems. At a broader scope, this enhanced insight can also help management boost morale and improve training, employee engagement and satisfaction, and hiring practices.