Linear regression is a fundamental statistical technique used to understand the relationship between two variables: the independent variable (X) and the dependent variable (Y). By fitting a linear equation to observed data, we can predict the value of the dependent variable based on the independent variable.
The linear regression equation is typically represented as:
ŷ = b0 + b1x
where,
ŷ => represents the predicted or estimated value of the dependent variable (Y).
b0 =>is the y-intercept, the constant value where the regression line crosses the y-axis.
b1 => is the slope of the regression line, quantifying the effect of the independent variable (X) on the dependent variable (Y).
x is the value of the independent variable for which we want to predict the corresponding dependent variable value.
Example:
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