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# Question 6 of 10 ## 2 Points Which regression equation best fits these data? \begin{array}{cccc} x & y \\ - 4 & 8 \\ - 3 & 12 \\ - 2 & 14 \\ - 1 & 16 \\ 1 & 15 \\ 2 & 12 \\ 3 & 9 \\ 4 & 5 \end{array} - A. y = 0.58x^2 + 0.43x + 15.75 - B. y = - 0.58x^2 - 0.43x + 15.75 - C. y = 10.72 \cdot 0.95^2 - D. y = - 0.43x + 11.34
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Answer

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Step 1:
: Identify the type of regression equation needed.

In this case, the given data points show a curve, so we need to determine if it is a quadratic or exponential curve.

Step 2:
: Calculate the sum of squares of residuals (SSR) for both quadratic and exponential models to compare their fits.

Step 3:
: Quadratic Model:

So, the quadratic model is $$y = 0.58x^2 + 0.43x + 15.75$$.
Using the given data points, we can set up a system of linear equations as follows: a(- 4)^2 + b(- 4) + c &= 8 \ a(- 3)^2 + b(- 3) + c &= 12 \ a(- 2)^2 + b(- 2) + c &= 14 \ a(- 1)^2 + b(- 1) + c &= 16 \ a(1)^2 + b(1) + c &= 15 \ a(2)^2 + b(2) + c &= 12 \ a(3)^2 + b(3) + c &= 9 \ a(4)^2 + b(4) + c &= 5 \end{cases}

Step 4:
: Exponential Model:

So, the exponential model is $$y = 10.72 \cdot 0.95^x$$.
Using the given data points, we can set up a system of nonlinear equations as follows: a(0.95^{- 4}) &= 8 \ a(0.95^{- 3}) &= 12 \ a(0.95^{- 2}) &= 14 \ a(0.95^{- 1}) &= 16 \ a(0.95^{1}) &= 15 \ a(0.95^{2}) &= 12 \ a(0.95^{3}) &= 9 \ a(0.95^{4}) &= 5 \end{cases}

Step 5:
: Calculate the SSR for both models.

SSR\_E = \sum\_{i=1}^{n} (y\_i - (ab^{x\_i}))^2
Quadratic Model: Exponential Model:

Step 6:
: Compare the SSR values for both models.

Whichever model has the smaller SSR value fits the data better.

Step 7:
: Based on the comparison, choose the best-fitting regression equation.

Final Answer

To determine which one is the best fit, follow steps 3 - 6.