Pre-Calc/Chapter 2: Polynomial, Power, and Rational Functions/2.1 Linear//Quadratic Functions and Modeling/2.1.1 Linear Functions Use regression to solve the problem. Round numbers to the nearest hundredth.T... Use regression to solve the problem. Round numbers to the nearest hundredth.

The ages and lengths of several animals of the same species are recorded in the following table:



Find the linear regression equation.]]> 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 1.0000000 0.3333333 0 true true abc y = 2.18x - 1.03 Incorrect y = 1.03x - 2.18 Correct y = 0.93x - 1.18 Incorrect y = 0.93x + 2.18 Incorrect