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disa [49]
1 year ago
10

Which exponential function has the greatest average rate of change over the interval [ 0 , 2 ]

Mathematics
1 answer:
PIT_PIT [208]1 year ago
5 0

The exponential function which has the greatest average rate of change over the interval [0, 2] is option B.

<h3>How to calculate the average rate of change?</h3>

Mathematically, the average rate of change between two (2) points can be calculated by using this expression;

Slope = \frac{Change\;in\;y\;axis}{Change\;in\;x\;axis}\\\\Slope = \frac{y_2\;-\;y_1}{x_2\;-\;x_1}

For option A, we have:

when x = 0;             when x = 2;

y = 3(1.6)^x               y = 3(1.6)^x

y = 3(1.6)^0              y = 3(1.6)^2

y = 3.                        y = 7.68

ΔA = (7.68 - 3)/(2 -0) = 2.34.

For option B, we have:

ΔA = (9 - 4)/(2 -0) = 2.5.

For option C, the exponential function has a common ratio of 2.

ΔA = 2.

For option D, we have:

ΔA = (10 - 8)/(1 -0) = 2.

In conclusion, we can logically deduce that the exponential function which has the greatest average rate of change over the interval [0, 2] is option B i.e 2.5.

Read more on average rate of change here: brainly.com/question/3493733

#SPJ1

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Step-by-step explanation:

Hello!

The given data corresponds to the variables

Y:  Annual Maintenance  Expense ($100s)

X: Weekly Usage  (hours)

n= 10

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a)

To estimate the slope and y-intercept you have to apply the following formulas:

b= \frac{sumXY-\frac{(sumX)(sumY)}{n} }{sumX^2-\frac{(sumX)^2}{n} } = \frac{9668.5-\frac{253*346.5}{10} }{7347-\frac{(253)^2}{10} }= 0.95

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b)

H₀: β = 0

H₁: β ≠ 0

α:0.05

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To decide using the p-value you have to compare it against the level of significance:

If p-value ≤ α, reject the null hypothesis.

If p-value > α, do not reject the null hypothesis.

The decision is to reject the null hypothesis.

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b= 0.95 $100s/hours is the variation of the estimated average annual maintenance expense of the computer wheel alignment and balancing machine is modified when the weekly usage increases one hour.

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c)

The value that determines the % of the variability of the dependent variable that is explained by the response variable is the coefficient of determination. You can calculate it manually using the formula:

R^2 = \frac{b^2[sumX^2-\frac{(sumX)^2}{n} ]}{[sumY^2-\frac{(sumY)^2}{n} ]} = \frac{0.95^2[7347-\frac{(253)^2}{10} ]}{[13010.75-\frac{(346.50)^2}{10} ]} = 0.86

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d)

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