How trend calculation works: the math behind linear regression explained

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A tutorial published on DEV Community walks through the mathematical foundations of trend calculation, a key tool in data science. The goal of trend analysis is to find a function that best fits a set of data points plotted over time, such as a company's revenue at different intervals. Because the relationship between time and measured values is stochastic, only an approximation — typically a linear function — can be fitted to the data. The quality of this fit is measured using SSE (Sum of Squared Errors) and MSE (Mean Squared Error), which quantify the difference between actual data points and the fitted line. To find the optimal parameters for the line, calculus is applied: the SSE is minimized by taking partial derivatives with respect to each parameter and setting them to zero.

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