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Initial question:

What types of business situations or problems might best lend themselves to multiple linear regression? What types may not? When do you anticipate using a multiple linear regression model in your postgraduate, professional experience? Explain.

Business situations that would use multiple linear regression would be when they want to examine the relationship between two or more predictor variables and one outcome variable. This concept is distinct from a simple linear regression. In the case of simple linear regression, we have one predictor variable and one outcome variable.

A time in my professional experience which I would anticipate using a multiple linear regression model would be in a situation where I’m conducting research in a mental health agency and I’ve developed an instrument that measures how well people adapt to changes in their job, so an adaptability level geared toward career. I would want to be able to predict this adaptability with available continuous level predictor variables. Let’s say the variables I have from these participants are their age, IQ score and how many hours they’ve spent studying how to make adaptations in work environments. With these three variables I want to see how well I can predict that adaptability level or skill level.

It may be that those variable don’t predict adaptability at all; none, 1, 2 or all 3 of the variables may or may not predict that outcome variable. So with these types of data it would not be unusual for me to perform a multiple linear regression. The regression would produce a line of best fit based on the least squares method.

Reference

Render, B., Stair, R. M., Hanna, M., & Hale, T. (2015). Quantitative analysis for management (12th ed.). Upper Saddle River, NJ: Pearson

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