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Regression analysis Definition

A multivariate statistical technique applied to data to determine, for predictive purposes, the degree of correlation of a dependent variable with one or more independent variables. In other words, a technique to see if there is a strong or weak cause and effect relationship between two or more things.

Regression analysis is a statistical method used to examine the relationship between one dependent variable and one or more independent variables. It helps determine how changes in predictor variables impact outcomes such as sales, satisfaction or brand preference.

Who relies on regression analysis in market research?

Data analysts, marketing strategists, product managers, pricing teams and consumer insights professionals rely on regression analysis to inform decision-making and identify key drivers of behavior.

What are the key aspects of regression analysis in market research?

• Measures strength and direction of variable relationships.
• Includes linear and multiple regression models.
• Can identify causation or correlation.
• Often visualized through scatter plots or regression lines.
• Requires clean, quantitative data for accuracy.

Why is regression analysis important in market research?

It allows researchers to isolate the impact of individual variables, make predictions and uncover actionable insights. This supports evidence-based strategy and resource allocation.

How do market researchers use regression analysis?

Researchers use regression to determine which factors most influence customer satisfaction, forecast demand, analyze price sensitivity, model brand equity and test campaign effectiveness. It's also used in segmentation and targeting strategies.