Regression Analysis Explained

Image ©2025 ux-qa.com

What is Regression Analysis?

Regression analysis is a statistical tool for understanding relationships between variables and making data-driven decisions.

In the context of UX research, it helps identify how design elements, user behaviors, or external factors influence user outcomes, such as satisfaction, engagement, and conversion rates.

Regression Analysis

Before you get scared of statistics, rest assured that you can look up all of these formulas online and copy/paste them into programs like Excel.

I aim to post Python templates you can  copy/paste in the weeks to come.

Regression analysis is used for modeling the relationship between variables.

The simplest form, known as linear regression, assumes a straight-line relationship between the dependent and independent variables.

Dependent Variable
The outcome you’re trying to predict or explain.

In UX, this could be things like user engagement or satisfaction, or a typical workflow.

Independent Variables
The factors you believe influence the outcome.

In a website UX study, these could be elements like page load time, color scheme, or the number of clicks required to complete a task, cognitive load reading text, connectivity issues, and so on.

Regression Analysis in UX Research

Applications

Predicting User Behavior

By analyzing factors like page load time, interaction speed, and interface complexity, regression models can help predict how these elements influence user satisfaction, retention, and conversion rates.

Identifying Key Influences

Regression analysis can be used to pinpoint which elements of a product or website most strongly correlate with positive or negative user experiences. For example, a regression model might reveal that content clarity and ease of navigation are far more impactful on user satisfaction than visual design.

Optimizing Performance

For product managers and UX researchers, regression models help prioritize changes that will lead to the greatest improvements in user experience. If regression analysis shows that reducing load time by just a few seconds could significantly improve user engagement, teams can focus on optimizing that specific aspect of the product.

Regression Analysis in Research

Benefits

Quantitative Insights

Regression provides objective, quantitative insights into how variables are interrelated. This allows researchers to make more informed, data-driven decisions.

Predictive Power

Once a regression model is built and validated, it can be used to make predictions. For example, in UX research, regression analysis can predict how changing one aspect of a design will impact overall user satisfaction.

Actionable Data

Unlike qualitative methods (like interviews or surveys), regression analysis offers concrete data that can guide decisions, such as prioritizing changes to the user interface or identifying which user actions most strongly impact outcomes.

Regression Analysis

Limitations

Correlation Isn’t Causation

Just because two variables are related doesn’t mean one causes the other. For instance, a regression analysis might show a strong correlation between website load time and user satisfaction, but it doesn’t prove that one causes the other.

Overfitting

If a regression model is too complex or uses too many variables, it may fit the training data too closely and perform poorly on new data. This is known as overfitting, and it can reduce the model's predictive accuracy.

Assumptions

Regression analysis relies on several assumptions (e.g., linearity, normal distribution of errors, and independence of observations). Violating these assumptions can impact the validity of the results.