Episode 8: What is Logistic Regression?
In another technically-focused episode, co-hosts Ron Landis and Jennifer Miller deconstruct a statistical technique called logistic regression. They focus on how logistic models can be used to predict the likelihood of a particular outcome. Given the numerous organizational outcomes that are binary in nature (for example, turnover, absence, or promotion), logistic models can provide important insights as to the drivers of such variables.
In this podcast episode, we had conversations around these logistic regression questions:
What is logistic regression?
How is logistic regression used in organizational contexts?
How can logistic regression be used to drive optimal business decisions?
What are some steps an organization can take to more effectively utilize logistic regression models?
Link to Linear Regression Podcast Episode
Key Takeaways:
Logistic Regression is a technique used to model relations between variables of interest and predict the probability of an outcome. The focus in this episode is on outcomes that take on one of two possibilities. For example, let’s say we’re interested in predicting whether an individual leaves an organization. Our outcome variable is turnover which we can define as either someone leaving or staying with the company. We also have characteristics about those individuals that we can include in the model as predictors to predict the outcome variable. The model will give information on the likelihood of an individual either staying or leaving the organization.
At the end of the episode, Jennifer and Ron recommend steps for folks just starting out in this space all the way to the more advanced HR professional.
