The equation of a simple linear regression model can be represented as:
y=β0+β1x+ϵ
Where:
- y is the dependent variable (the variable you're trying to predict).
- x is the independent variable (the variable you're using to make predictions).
- β0 is the intercept term, representing the value of y when x is 0.
- β1 is the coefficient for the independent variable x, representing the change in y for a unit change in x.
- ϵ is the error term, accounting for the variability in y that is not explained by the model.
In simpler terms, the equation describes a straight line that best fits the relationship between the dependent variable y and the independent variable x. The goal of linear regression is to find the values of 0β0 and β1 that minimize the sum of the squared differences between the actual y values and the predicted y values from the equation. This line can then be used to make predictions for y based on given values of x.
