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cestrela7 [59]
4 years ago
5

This description assumes the elliptic object is centered at (0, 0). However, since the elliptic object is not fixed, initially i

n the animation it is placed at (0, H) and starts to fall down due to gravity. It then hits the supporter and eventually reaches a stable equilibrium. When the elliptic object settles stably on the supporter, its center position is at (0, h). Derive a formula to compute h using the elliptic parameters a and b.

Mathematics
1 answer:
yKpoI14uk [10]4 years ago
5 0

Complete Question

The complete question is shown on the first and second uploaded image

Answer:

The derived h = \frac{b^{2} - 2a^{2}  }{2a^{2}}

Step-by-step explanation:

Step One : Consider the ellipse in equilibrium.

Looking at the ellipse in equilibrium i.e when the ellipse has settled down on the concave support (represented by a parabola ) as shown on the third uploaded image.

Step Two : Consider the ellipse equation.

Generally the equation of the ellipse is given as

                       \frac{x^{2}}{a^{2}} + \frac{(y-h)^{2}}{b^{2}}\\

Also the base on which it rest at equilibrium i.e the parabola is represented by     y = x^{2} -1

Substituting the value of y in the ellipse equation we have

        \frac{x^{2}}{a^{2}}+ \frac{(x^{2}-1-h)^{2}}{b^{2}}=1

Let x^{2} = t

So the equation becomes

                 \frac{t}{a^{2}} + \frac{(t -1 -h)^{2}}{b^{2}} =1

Rearranging, we get :

       a^{2} t^{2} + (b^{2}- 2a^{2}(h +1))t + a^{2}((h +1 )^{2} -b^{2} =0

This equation above is a quadratic equation or a bi-quadratic equation in x as t = x^{2}

Step Three : Relate the equation an the graph on the third uploaded image  

We can see that from the graph , if A and B are the two values of x for which the points is made , then A + B = 0 (because they are symmetric in nature)

From Vieta's Roots(Vieta's formula  is a formula that shows the relationship between the coefficients of a polynomial and the sum of its roots )

ax^{2} + bx + c =0

with A and B as roots

A+B = \frac{-b}{a}

But A + B = 0

So  \frac{-b}{a}   = 0

 or we can say that

              \frac{b^{2} - 2a^{2}(h +1 )}{a^{2}}

Rearranging we get

                        h = \frac{b^{2}- 2a^{2}}{2a^{2}}

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Let X and Y have the joint density f(x, y) = e −y , for 0 ≤ x ≤ y. (a) Find Cov(X, Y ) and the correlation of X and Y . (b) Find
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a. I assume the following definitions for covariance and correlation:

\mathrm{Cov}[X,Y]=E[(X-E[X])(Y-E[Y])]=E[XY]-E[X]E[Y]

\mathrm{Corr}[X,Y]=\dfrac{\mathrm{Cov}[X,Y]}{\sqrt{\mathrm{Var}[X]\mathrm{Var}[Y]}}

Recall that

E[g(X,Y)]=\displaystyle\iint_{\Bbb R^2}g(x,y)f_{X,Y}(x,y)\,\mathrm dx\,\mathrm dy

where f_{X,Y} is the joint density, which allows us to easily compute the necessary expectations (a.k.a. first moments):

E[XY]=\displaystyle\int_0^\infty\int_0^yxye^{-y}\,\mathrm dx\,\mathrm dy=3

E[X]=\displaystyle\int_0^\infty\int_0^yxe^{-y}\,\mathrm dx\,\mathrm dy=1

E[Y]=\displaystyle\int_0^\infty\int_0^yye^{-y}\,\mathrm dx=2

Also, recall that the variance of a random variable X is defined by

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

We use the previous fact to find the second moments:

E[X^2]=\displaystyle\int_0^\infty\int_0^yx^2e^{-y}\,\mathrm dx\,\mathrm dy=2

E[Y^2]=\displaystyle\int_0^\infty\int_0^yy^2e^{-y}\,\mathrm dx\,\mathrm dy=6

Then the variances are

\mathrm{Var}[X]=2-1^2=1

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Putting everything together, we find the covariance to be

\mathrm{Cov}[X,Y]=3-1\cdot2\implies\boxed{\mathrm{Cov}[X,Y]=1}

and the correlation to be

\mathrm{Corr}[X,Y]=\dfrac1{\sqrt{1\cdot2}}\implies\boxed{\mathrm{Corr}[X,Y]=\dfrac1{\sqrt2}}

b. To find the conditional expectations, first find the conditional densities. Recall that

f_{X,Y}=f_{X\mid Y}(x\mid y)f_Y(y)=f_{Y\mid X}(y\mid x)f_X(x)

where f_{X\mid Y} is the conditional density of X given Y, and f_X is the marginal density of X.

The law of total probability gives us a way to obtain the marginal densities:

f_X(x)=\displaystyle\int_x^\infty e^{-y}\,\mathrm dy=\begin{cases}e^{-x}&\text{for }x\ge0\\0&\text{otherwise}\end{cases}

f_Y(y)=\displaystyle\int_0^ye^{-y}\,\mathrm dx=\begin{cases}ye^{-y}&\text{for }y\ge0\\0&\text{otherwise}\end{cases}

Then it follows that the conditional densities are

f_{X\mid Y}(x\mid y)=\begin{cases}\frac1y&\text{for }0\le x

f_{Y\mid X}(y\mid x)=\begin{cases}e^{x-y}&\text{for }0\le x

Then the conditional expectations are

E[X\mid Y=y]=\displaystyle\int_0^y\frac xy\,\mathrm dy\implies\boxed{E[X\mid Y=y]=\frac y2}

E[Y\mid X=x]=\displaystyle\int_x^\infty ye^{x-y}\,\mathrm dy\implies\boxed{E[Y\mid X=x]=x+1}

c. I don't know which theorems are mentioned here, but it's probably safe to assume they are the laws of total expectation (LTE) and variance (LTV), which say

E[X]=E[E[X\mid Y]]

\mathrm{Var}[X]=E[\mathrm{Var}[X\mid Y]]+\mathrm{Var}[E[X\mid Y]]

We've found that E[X\mid Y]=\frac Y2 and E[Y\mid X]=X+1, so that by the LTE,

E[X]=E[E[X\mid Y]]=E\left[\dfrac Y2\right]\implies E[Y]=2E[X]

E[Y]=E[E[Y\mid X]]=E[X+1]\implies E[Y]=E[X]+1

\implies2E[X]=E[X]+1\implies\boxed{E[X]=1}

Next, we have

\mathrm{Var}[X\mid Y]=E[X^2\mid Y]-E[X\mid Y]^2=\dfrac{Y^2}3-\left(\dfrac Y2\right)^2\implies\mathrm{Var}[X\mid Y]=\dfrac{Y^2}{12}

where the second moment is computed via

E[X^2\mid Y=y]=\displaystyle\int_0^y\frac{x^2}y\,\mathrm dx=\frac{y^2}3

In turn, this gives

E\left[\dfrac{Y^2}{12}\right]=\displaystyle\int_0^\infty\int_0^y\frac{y^2e^{-y}}{12}\,\mathrm dx\,\mathrm dy\implies E[\mathrm{Var}[X\mid Y]]=\frac12

\mathrm{Var}[E[X\mid Y]]=\mathrm{Var}\left[\dfrac Y2\right]=\dfrac{\mathrm{Var}[Y]}4\implies\mathrm{Var}[E[X\mid Y]]=\dfrac12

\implies\mathrm{Var}[X]=\dfrac12+\dfrac12\implies\boxed{\mathrm{Var}[X]=1}

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