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NikAS [45]
3 years ago
15

The x- intercepts of a parabola are (0,-6) and (0,4). The parabola crosses the y- axis at -120. Lucas said that an equation for

the parabola is y=5x^2+10x-120 and that the coordinates of the vertex are (-1, -125). Do you agree or disagree? List why?
Mathematics
1 answer:
astraxan [27]3 years ago
5 0

Given:

The x- intercepts of a parabola are (0,-6) and (0,4).

The parabola crosses the y- axis at -120.

Lucas said that an equation for the parabola is y=5x^2+10x-120 and that the coordinates of the vertex are (-1, -125).

To find:

Whether Lucas is correct or not.

Solution:

The x- intercepts of a parabola are (0,-6) and (0,4). It means (x+6) and (x-4) are the factors of the equation of the parabola.

y=a(x+6)(x-4)             ...(i)

The parabola crosses the y- axis at -120. It means the equation of the parabola must be true for (0,-120).

-120=a(0+6)(0-4)

-120=a(6)(-4)

-120=-24a

Divide both sides by -24.

\dfrac{-120}{-24}=a

5=a

Substituting a=5 in (i), we get

y=5(x+6)(x-4)

y=5(x^2+6x-4x-24)

y=5(x^2+2x-24)

y=5x^2+10x-120

So, the equation of the parabola is y=5x^2+10x-120.

The vertex of a parabola f(x)=ax^2+bx+c is:

Vertex=\left(-\dfrac{b}{2a},f(-\dfrac{b}{2a})\right)

In the equation of the parabola, a=5,b=10,c=-120.

-\dfrac{b}{2a}=-\dfrac{10}{2(5)}

-\dfrac{b}{2a}=-\dfrac{10}{10}

-\dfrac{b}{2a}=-1

Putting x=-1 in the equation of the parabola, we get

y=5(-1)^2+10(-1)-120

y=5-10-120

y=-125

So, the vertex of the parabola is at point (-1,-125).

Therefore, Lucas is correct.

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I'll leave the computation via R to you. The W_i are distributed uniformly on the intervals [0,10i], so that

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each with mean/expectation

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and variance

\mathrm{Var}[W_i]=E[(W_i-E[W_i])^2]=E[{W_i}^2]-E[W_i]^2

We have

E[{W_i}^2]=\displaystyle\int_{-\infty}^\infty w^2f_{W_i}(w)\,\mathrm dw=\int_0^{10i}\frac{w^2}{10i}\,\mathrm dw=\frac{100i^2}3

so that

\mathrm{Var}[W_i]=\dfrac{25i^2}3

Now,

E[W_1+W_2+W_3]=E[W_1]+E[W_2]+E[W_3]=5+10+15=30

and

\mathrm{Var}[W_1+W_2+W_3]=E\left[\big((W_1+W_2+W_3)-E[W_1+W_2+W_3]\big)^2\right]

\mathrm{Var}[W_1+W_2+W_3]=E[(W_1+W_2+W_3)^2]-E[W_1+W_2+W_3]^2

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(W_1+W_2+W_3)^2={W_1}^2+{W_2}^2+{W_3}^2+2(W_1W_2+W_1W_3+W_2W_3)

E[(W_1+W_2+W_3)^2]

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E[(W_1+W_2+W_3)^2]=\dfrac{100}3+\dfrac{400}3+300+2(50+75+150)=\dfrac{3050}3

giving a variance of

\mathrm{Var}[W_1+W_2+W_3]=\dfrac{3050}3-30^2=\dfrac{350}3

and so the standard deviation is \sqrt{\dfrac{350}3}\approx\boxed{116.67}

# # #

A faster way, assuming you know the variance of a linear combination of independent random variables, is to compute

\mathrm{Var}[W_1+W_2+W_3]

=\mathrm{Var}[W_1]+\mathrm{Var}[W_2]+\mathrm{Var}[W_3]+2(\mathrm{Cov}[W_1,W_2]+\mathrm{Cov}[W_1,W_3]+\mathrm{Cov}[W_2,W_3])

and since the W_i are independent, each covariance is 0. Then

\mathrm{Var}[W_1+W_2+W_3]=\mathrm{Var}[W_1]+\mathrm{Var}[W_2]+\mathrm{Var}[W_3]

\mathrm{Var}[W_1+W_2+W_3]=\dfrac{25}3+\dfrac{100}3+75=\dfrac{350}3

and take the square root to get the standard deviation.

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