Linear regression equation: ŷ = β0 + β1X
Regression analysis
Linear regression equation: ŷ = β0 + β1X
Linear regression aims to predict the dependent variable (ŷ) using the independent variable (X) and its relationship with the dependent variable. The equation includes a y-intercept (β0) and a slope (β1), which represent the starting point and the rate of change, respectively.
Example
If we want to predict a person's weight (ŷ) based on their height (X), the linear regression equation could be ŷ = 50 + 2X, where 50 is the y-intercept and 2 is the slope.
Remember this
Understanding the linear regression equation is crucial for making accurate predictions and interpreting the relationship between variables in statistical modeling.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
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L2 normalization equation: x_i' = x_i / ||x||_2
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Mean squared error (MSE) formula: MSE = (1/n) * Σ(y_i - ŷ_i)²
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