Pre-Calc/Chapter 2: Polynomial, Power, and Rational Functions/2.1 Linear//Quadratic Functions and Modeling/2.1.1 Linear Functions Describe the strength and direction of the linear correlation. Describe the strength and direction of the linear correlation.

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 1.0000000 0.3333333 0 true true abc Strong positive linear correlation Correct Weak negative linear correlation Incorrect Strong negative linear correlation Incorrect Little or no linear correlation Incorrect Describe the strength and direction of the linear correlation. Describe the strength and direction of the linear correlation.

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 1.0000000 0.3333333 0 true true abc Weak positive linear correlation Incorrect Strong positive linear correlation Incorrect Little or no linear correlation Incorrect Strong negative linear correlation Correct Describe the strength and direction of the linear correlation. Describe the strength and direction of the linear correlation.

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 1.0000000 0.3333333 0 true true abc Little or no linear correlation Incorrect Weak negative linear correlation Incorrect Strong negative linear correlation Incorrect Weak positive linear correlation Correct If the following is a polynomial function, then state its degree and leading ... If the following is a polynomial function, then state its degree and leading coefficient. If it is not, then state this fact.

f(x) = 5x^-6 + 15]]> 1.0000000 0.3333333 0 true true abc Degree: 5; leading coefficient: -6 Incorrect Degree: 6; leading coefficient: 5 Incorrect Degree: -6; leading coefficient: 5 Incorrect Not a polynomial function Correct If the following is a polynomial function, then state its degree and leading ... If the following is a polynomial function, then state its degree and leading coefficient. If it is not, then state this fact.

f(x) = -11 - 4x⁴ - 5x - 7x³ - 17x²]]> 1.0000000 0.3333333 0 true true abc Not a polynomial function. Incorrect Degree: 4; leading coefficient: -4 Correct Degree: -11; leading coefficient: -4 Incorrect Degree: 4; leading coefficient: -11 Incorrect If the following is a polynomial function, then state its degree and leading ... If the following is a polynomial function, then state its degree and leading coefficient. If it is not, then state this fact.

f(x) =

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 Write an equation for the linear function f satisfying the given conditions.... Write an equation for the linear function f satisfying the given conditions.

f(-1) = -5 and f(5) = 7

]]> 1.0000000 0.3333333 0 true true ABCD f(x) = -3x - 8

]]> Incorrect

]]> f(x) = 4x - 1

]]> Incorrect

]]> f(x) = 2x - 3

]]> Correct

]]> f(x) = 3x - 8

]]> Incorrect

]]> f(x)= 3x - 2

]]> Write an equation for the linear function f satisfying the given conditions.f... Write an equation for the linear function f satisfying the given conditions.

f(3) = 5 and f(6) = 6]]> 1.0000000 0.3333333 0 true true abc x - 2]]> 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 Incorrect x - 2]]> 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 Incorrect x + 3]]> 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 Incorrect x + 4]]> 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 Correct Write an equation for the linear function f satisfying the given conditions.f... Write an equation for the linear function f satisfying the given conditions.

f(-3) = 4 and f(1) = 1]]> 1.0000000 0.3333333 0 true true abc x +