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aksik [14]
3 years ago
12

What is the rate of change in the number of students

Mathematics
2 answers:
Tatiana [17]3 years ago
8 0
B.) 50 because ten years time gap, 50 x 5 is 250
marissa [1.9K]3 years ago
7 0
If there were 12000 students in 2005, and 12250 students in 2010, we know that in a 5 year period, the student total increased by 250.

So the rate of change in students per year would be 250/5 years
Which is 50/yr, so the answer is B.

Hope this helped
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jarptica [38.1K]
D) The article without a graph is probably more interesting.
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WILL GIVE BRAINLIEST
Andrei [34K]

Answer:

Hello, the answer would be  A. ∠2

Step-by-step explanation:

Hope this helps!

Brainliest please.

6 0
4 years ago
3-3(x-2) <br><br> simplify the expression
arsen [322]

Answer:

-3-3x

Step-by-step explanation:

3-3(x-2)

<em>3</em>-3x+<em>6</em>

-<em>3</em>-3x

5 0
3 years ago
Read 2 more answers
Let Y1, Y2, . . . , Yn be independent, uniformly distributed random variables over the interval [0, θ]. Let Y(n) = max{Y1, Y2, .
Anettt [7]

Answer:

a) F(y) = 0, y

F(y) = \frac{y}{\theta} , 0 \leq y \leq \theta

F(y)= 1, y>1

b) f_{Y_{(n)}} = \frac{d}{dy} (\frac{y}{\theta})^n = n \frac{y^{n-1}}{\theta^n}, 0 \leq y \leq \theta

f_{Y_{(n)}} =0 for other case

c) E(Y_{(n)}) = \frac{n}{\theta^n} \frac{\theta^{n+1}}{n+1}= \theta [\frac{n}{n+1}]

Var(Y_{(n)}) =\theta^2 [\frac{n}{(n+1)(n+2)}]

Step-by-step explanation:

We have a sample of Y_1, Y_2,...,Y_n iid uniform on the interval [0,\theta] and we want to find the cumulative distribution function.

Part a

For this case we can define the CDF for Y_i , i =1,2.,,,n like this:

F(y) = 0, y

F(y) = \frac{y}{\theta} , 0 \leq y \leq \theta

F(y)= 1, y>1

Part b

For this case we know that:

F_{Y_{(n)}} (y) = P(Y_{(n)} \leq y) = P(Y_1 \leq y,....,Y_n \leq y)

And since are independent we have:

F_{Y_{(n)}} (y) = P(Y_1 \leq y) * ....P(Y_n \leq y) = (\frac{y}{\theta})^n

And then we can find the density function calculating the derivate from the last expression and we got:

f_{Y_{(n)}} = \frac{d}{dy} (\frac{y}{\theta})^n = n \frac{y^{n-1}}{\theta^n}, 0 \leq y \leq \theta

f_{Y_{(n)}} =0 for other case

Part c

For this case we can find the mean with the following integral:

E(Y_{(n)}) = \frac{n}{\theta^n} \int_{0}^{\theta} y y^{n-1} dy

E(Y_{(n)}) = \frac{n}{\theta^n} \int_{0}^{\theta} y^n dy

E(Y_{(n)}) = \frac{n}{\theta^n} \frac{y^{n+1}}{n+1} \Big|_0^{\theta}

And after evaluate we got:

E(Y_{(n)}) = \frac{n}{\theta^n} \frac{\theta^{n+1}}{n+1}= \theta [\frac{n}{n+1}]

For the variance first we need to find the second moment like this:

E(Y^2_{(n)}) = \frac{n}{\theta^n} \int_{0}^{\theta} y^2 y^{n-1} dy

E(Y^2_{(n)}) = \frac{n}{\theta^n} \int_{0}^{\theta} y^{n+1} dy

E(Y^2_{(n)}) = \frac{n}{\theta^n} \frac{y^{n+2}}{n+2} \Big|_0^{\theta}

And after evaluate we got:

E(Y^2_{(n)}) = \frac{n}{\theta^n} \frac{\theta^{n+2}}{n+2}= \theta^2 [\frac{n}{n+2}]

And the variance is given by:

Var(Y_{(n)}) = E(Y^2_{(n)}) - [E(Y_{(n)})]^2

And if we replace we got:

Var(Y_{(n)}) =\theta^2 [\frac{n}{n+2}] -\theta^2 [\frac{n}{n+1}]^2

Var(Y_{(n)}) =\theta^2 [\frac{n}{n+2} -(\frac{n}{n+1})^2]

And after do some algebra we got:

Var(Y_{(n)}) =\theta^2 [\frac{n}{(n+1)(n+2)}]

3 0
3 years ago
If an x-coordinate repeats, can a relation still be a function?<br><br>I need help right now
Gre4nikov [31]

Answer:

No

Step-by-step explanation:

if you see any duplicates or repetitions in the x-values, the relation is not a function.

8 0
3 years ago
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