(a) Regression Intercept Term: The constant value of the dependent variable when the independent variable x is set to zero-in other words, the point where the function crosses over the y-axis.(y) Dependent Variable: This variable depends on the other measured factors to the right of the equals sign. Mathematically, linear regression uses the easy-to-interpret formula listed below, followed by a detailed breakdown of what each of the formula’s variables represents: The goal is to make predictions based on historical data points by calculating and plotting out the trendline. Our example dataset aligns average daily temperatures with average daily sports drink sales during a given time period. Based on this initial information, you could assign “beverage sales” to the y (dependent) variable-the target, or value you’re trying to predict-and “temperature” to the x (independent) variable. The hotter the temperature, the more beverage units sold-in other words, regional beverage sales depend on the temperature of the given region. This ability serves a wide range of purposes in the business world.įor example, a sports drink company makes the reasonable assumption that its beverage sales are directly linked to the outdoor temperature of a given geographic sales region. Linear regression is used for quantifying the relationship between an independent variable and a dependent variable, which enables you to make predictions for how real world scenarios might unfold. Bottom Line: Excel-based Linear Regression For Easy Desktop Predictions.
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