Linear regression is one of the oldest and most widely used methods in data analysis. It helps identify a relationship between a dependent variable, such as revenue or sales volume, and one or more explanatory factors, such as price, seasonality, or marketing budget. This is referred to as a simple predictive model, as the goal is to extract a clear trend from observed data without delving into the complexity of advanced artificial intelligence algorithms.

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In business, linear regression is used whenever it is necessary to understand how a change takes place and when the goal is to estimate (predict) a value. For example, it can help anticipate growth, assess the impact of a commercial action, or forecast demand based on measured trends.

How linear regression works ?

The principle relies on an intuitive idea: when one variable changes, it is often possible to observe a more or less regular effect on another. Linear regression seeks to formalize this link by drawing a trend line that best describes the relationship between the two elements.

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Imagine that an increase in the advertising budget generally leads to higher sales. Rather than relying on a simple visual impression or intuition, linear regression determines a general trend that minimizes the gap between this predicted line and the values actually observed. The line thus becomes a reference point that makes it possible to estimate future results: if the planned marketing budget is known, the probable effect on sales can be anticipated.

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This process is based on the assumption that past data can shed light on the future. The algorithm examines historical series, identifies the most consistent relationship, and uses it to project new scenarios. The more extensive, reliable, and representative the data, the more accurate the model becomes and the more credible the forecasts.
In practice, this means that the company can test different scenarios and measure potential consequences before making a decision. For example, it can simulate the impact of a price increase, evaluate the potential return of a marketing campaign, or anticipate seasonal demand. Unlike more complex machine learning approaches, linear regression retains a level of clarity that makes results easier for managers and consultants to interpret and apply.

Several factors explain the widespread adoption of linear regression in professional settings. Its simplicity of interpretation is a decisive advantage: results appear as clear trends that decision-makers can immediately use. Speed of implementation also plays an important role, as it only requires relevant historical data to quickly build a simple and operational predictive model.

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This method is also characterized by great versatility. It can be applied to sales forecasting, financial analysis, logistics optimization, or human resource management. Finally, it serves as a starting point for more sophisticated approaches: many companies begin with linear regression before moving on to advanced machine learning techniques, making it an essential foundation of any data analysis strategy.

Linear regression is particularly suitable when relationships between variables appear relatively stable and logical. It enables decision-makers to understand the effect of a key factor on a specific indicator and to rely on reliable forecasts without requiring heavy technical infrastructure. This approach is useful when a company seeks quick, actionable, and clear results to guide strategic choices.

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It offers an objective analytical framework that complements intuition and human experience. Rather than relying solely on impressions or occasional comparisons, linear regression provides a quantified estimate of the expected impact of an action or a market evolution. In this way, it becomes a genuine decision-support tool.