What is the algebraic formula used to create predictions in a linear regression model?

• A. y=mx+b
• B. c=mx+b
• C. y=bx+m
• D. y=mx-b

Answer: (A)

In simple linear regression, predictions come from a straight-line equation. The predicted value is given by ŷ = m x + b, where x is the input feature, m is the slope (how much y changes when x increases by one unit), and b is the intercept (the value of y when x is zero). This form directly maps an input x to a predicted y by multiplying x by the slope and adding the intercept.

This is the standard way to express a linear relationship between two variables, and in many texts the intercept and slope are written as β0 and β1, giving ŷ = β0 + β1 x. The other forms are not correct for predicting y: they either misplace the dependent variable, swap the roles of the slope and intercept, or use subtraction instead of addition.