Regresión Lineal en Excel

Linear regression is fundamental in data science and machine learning to make predictions based on trends. Implementing this method helps to identify patterns and project future data accurately.

How is the data visualized?

To start, we select our data which consists of monthly sales of a business. The visualization is done by creating a graph that clearly shows the observations from January through December. This is easily done using graphical tools that allow detailed parameterization to obtain an accurate representation.

How is the linear model applied?

The next step is to apply the linear regression model. We use a simple tool that not only plots the trend line, but also provides us with the equation of the line. This equation is crucial as it will allow us to make future projections based on the current data.

How is the equation of the tangent line implemented?

We implement the equation of the tangent line simply by extracting the equation from the graph and using the coefficient of determination (R-squared) to measure the certainty of the model. This coefficient must be greater than 80% to be considered acceptable.

How is the projection integrated into the data?

We integrate the projection into our data set by extending the time periods beyond the initial observations. This is done by replacing the values of X in the equation and extrapolating the data for future months, thus obtaining accurate predictions for periods we did not originally have.

How is the projection interpreted?

Finally, we interpret the results by comparing the predictions with the actual values. Visualizing the projections and the actual data on a graph allows us to understand the accuracy of the model and make necessary adjustments. With this methodology, we predicted future sales with minimal deviation, demonstrating the effectiveness of the linear regression model.