What Regression Analysis Does
Regression analysis quantifies the relationship between a dependent variable (the outcome you want to understand or predict) and one or more independent variables (factors that might influence that outcome). In market research, this commonly answers questions like: "Which product attributes most strongly drive purchase intent?" or "How much does price sensitivity vary by customer segment?"
Types of Regression Used in Market Research
Linear Regression
Models a continuous outcome (e.g., sales revenue, satisfaction score) as a function of one or more predictor variables. Produces a coefficient for each predictor showing its estimated effect size.
Logistic Regression
Models a binary outcome (e.g., purchased vs. did not purchase, churned vs. retained) and produces odds ratios indicating how each predictor affects the probability of the outcome.
Multiple Regression
Includes several independent variables simultaneously, allowing researchers to isolate the unique contribution of each factor while statistically controlling for the others — critical when multiple factors are correlated with each other.
Key Output: R-Squared
R-squared (R²) indicates what percentage of variance in the outcome is explained by the model, ranging from 0 to 1. An R² of 0.65 means the model explains 65% of the variation in the outcome — the remaining 35% is due to factors not included in the model or random variation.
Critical Caveat: Correlation vs Causation
Regression analysis reveals statistical association, not proof of causal relationship. A strong relationship between two variables may be driven by a third, unmeasured factor, or the direction of causation may be reversed from what seems intuitive. Experimental methods (like A/B testing) are needed to establish true causation.
Frequently Asked Questions
What is a good R-squared value?
Highly context-dependent. In controlled experimental settings, R² above 0.7 is strong. In messy real-world consumer behavior data, R² of 0.3-0.5 can still be meaningful and actionable, since human behavior has inherent unexplained variance.
How many variables can a regression model include?
Theoretically many, but practically limited by sample size — a common rule of thumb is at least 10-20 observations per predictor variable to avoid overfitting.
