In our first technically-focused episode, co-hosts Ron Landis and Jennifer Miller deconstruct a common statistical technique called linear regression. They focus on how regression can be used to better understand the relations between key drivers of important outcomes.
In this episode, they had conversations around these questions:
- What is linear regression?
- How are regression analyses used in organizational contexts?
- How can linear regression be used to drive optimal business decisions?
- What are some steps an organization can take to more effectively utilize linear regression models?
Key Takeaways:
- Linear Regression is a technique used to model relations between variables of interest and to use these relations to forecast future states. For example, in a simple linear regression, we might be interested in predicting a key outcome variable such as sales from other predictor variables such as number of customers. This kind of statistical technique can be used when the underlying relation between the predictor and outcome is linear (I.e., when the predictor and outcome is plotted, it follows a relatively straight line).
- 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.
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