In this blog we want to explain what regression analysis is, how it works, and how to interpret its results in a practical business context. Regression is one of the most widely used techniques in analytics because it allows us to understand relationships between variables and make predictions based on data rather than guesswork.
Regression analysis helps answer questions like how much does marketing spend affect sales or how strongly does customer age influence purchase frequency. It measures how one variable changes when another one changes, while controlling for other factors. In simple terms, it shows how connected two or more things are, and how much one of them contributes to explaining the other.
Imagine a retail company that wants to forecast monthly sales. Several factors might influence sales volume: advertising spend, seasonality, product price, and store promotions. Regression analysis allows us to combine all these factors in one model to estimate how each one contributes to overall performance. For example, the model may reveal that every extra 10,000 SEK in marketing spend increases sales by 2 percent, while price discounts have a much stronger effect during holiday periods.
The basic idea is straightforward. Regression finds the line or curve that best fits the data points. The mathematical formula of that line helps explain how changes in one or more independent variables influence the dependent variable. Once built, the model can be used to predict outcomes for future scenarios, test business strategies, or evaluate campaign impact.
Interpreting regression results correctly is just as important as building the model. The coefficients show how much change is expected in the outcome when one variable changes by one unit, holding others constant. The p-value indicates whether that relationship is statistically significant or could have happened by chance. The R-squared value measures how well the model fits the data, showing what proportion of variation in the outcome is explained by the model.
Common regression key performance indicators include:
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R-squared: Measures the strength of the model’s explanatory power. Higher values indicate better fit.
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Adjusted R-squared: Adjusts for the number of variables, useful when comparing models with different inputs.
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P-value: Tests if each variable’s effect is statistically significant.
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Standard error: Indicates the reliability of each coefficient estimate.
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F-statistic: Tests whether the model as a whole explains the variation significantly better than a model with no predictors.
Understanding these metrics helps separate a useful model from one that only looks good on paper. A high R-squared is not always a sign of quality if the model overfits the data or includes irrelevant variables. Business context must always guide interpretation.
In this blog we want to highlight that regression is not just about prediction but about understanding relationships. It helps reveal which levers drive outcomes and by how much, guiding teams toward smarter resource allocation.
Practical tips for effective regression analysis
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Always start with a clear question and identify which variable you want to predict.
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Clean and prepare data carefully; outliers or missing values can distort results.
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Include variables that make sense in the business context, not just those that improve model fit.
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Check multicollinearity to ensure variables are not too closely related.
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Interpret coefficients in plain language so non-technical stakeholders understand the impact.
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Validate the model with new data to test if it generalizes beyond the training sample.
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Use visualization to communicate relationships and model predictions clearly.
Regression analysis turns raw data into actionable insight. It quantifies relationships, supports decision making, and builds a foundation for evidence-based business strategies. When done correctly, it becomes one of the most powerful tools in any analyst’s toolkit.
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