Regression analysis measures the strength of a relationship between a variable you try to explain (e.g. overall customer satisfaction) and one or more explaining variables (e.g. satisfaction with product quality and price).While correlation provides a single numeric summary of a relation (called the correlation coefficient), regression analysis results in a "prediction" equation.

The equation describes the relation between the variables. If the relationship is strong (expressed by the Rsquare value), it can be used to predict values of one variable given the other variables have known values e.g. how will the overall satisfaction score change if satisfaction with product quality goes up from 6 to 7?Regression analysis is typically used. For customer satisfaction and employee satisfaction studies to answer questions such as "which product dimensions contribute most to someone's overall satisfaction or loyalty to the brand?". This is often referred to as Key Drivers Analysis.

To simulate the outcome when actions are taken. e.g. what will happen to the satisfaction score when product availability is improved?

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