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leonid [27]
4 years ago
14

PLSSS HELP MARKING BRAINLIST QUESTION BELOW

Mathematics
1 answer:
Nadya [2.5K]4 years ago
5 0

Answer:

The third answer. ( John's boat was farther from the deck at the beginning but Roberta's boat traveled more quickly)

Step-by-step explanation:

You already know that in the beginning John's boat begins 20 miles  from the dock. Now you need to find Roberta! When each boat begins from the dock, it means when x=0. So lets insert that! y= 6(0)+15. You get 15. Now you know that its either the 3rd or 4th answer.  The second piece of info you have for John is that it is 45 miles from the dock after 5 hours. Again, in the beginning it started with 20 miles. 45-20= 25 miles TRAVELED in 5 hours.  For Roberta, we need to insert 5 in the equation for x. y=6(5)+15. You get: 45 again! But... Roberta started at 15 miles. So 45-15 is 30 miles. She traveled 30 miles in 5 hours. The only answer that is matching to our problem solving is the third answer. Hope this helps!

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Let $$X_1, X_2, ...X_n$$ be uniformly distributed on the interval 0 to a. Recall that the maximum likelihood estimator of a is $
Solnce55 [7]

Answer:

a) \hat a = max(X_i)  

For this case the value for \hat a is always smaller than the value of a, assuming X_i \sim Unif[0,a] So then for this case it cannot be unbiased because an unbiased estimator satisfy this property:

E(a) - a= 0 and that's not our case.

b) E(\hat a) - a= \frac{na}{n+1} - a = \frac{na -an -a}{n+1}= \frac{-a}{n+1}

Since is a negative value we can conclude that underestimate the real value a.

\lim_{ n \to\infty} -\frac{1}{n+1}= 0

c) P(Y \leq y) = P(max(X_i) \leq y) = P(X_1 \leq y, X_2 \leq y, ..., X_n\leq y)

And assuming independence we have this:

P(Y \leq y) = P(X_1 \leq y) P(X_2 \leq y) .... P(X_n \leq y) = [P(X_1 \leq y)]^n = (\frac{y}{a})^n

f_Y (Y) = n (\frac{y}{a})^{n-1} * \frac{1}{a}= \frac{n}{a^n} y^{n-1} , y \in [0,a]

e) On this case we see that the estimator \hat a_1 is better than \hat a_2 and the reason why is because:

V(\hat a_1) > V(\hat a_2)

\frac{a^2}{3n}> \frac{a^2}{n(n+2)}

n(n+2) = n^2 + 2n > n +2n = 3n and that's satisfied for n>1.

Step-by-step explanation:

Part a

For this case we are assuming X_1, X_2 , ..., X_n \sim U(0,a)

And we are are ssuming the following estimator:

\hat a = max(X_i)  

For this case the value for \hat a is always smaller than the value of a, assuming X_i \sim Unif[0,a] So then for this case it cannot be unbiased because an unbiased estimator satisfy this property:

E(a) - a= 0 and that's not our case.

Part b

For this case we assume that the estimator is given by:

E(\hat a) = \frac{na}{n+1}

And using the definition of bias we have this:

E(\hat a) - a= \frac{na}{n+1} - a = \frac{na -an -a}{n+1}= \frac{-a}{n+1}

Since is a negative value we can conclude that underestimate the real value a.

And when we take the limit when n tend to infinity we got that the bias tend to 0.

\lim_{ n \to\infty} -\frac{1}{n+1}= 0

Part c

For this case we the followng random variable Y = max (X_i) and we can find the cumulative distribution function like this:

P(Y \leq y) = P(max(X_i) \leq y) = P(X_1 \leq y, X_2 \leq y, ..., X_n\leq y)

And assuming independence we have this:

P(Y \leq y) = P(X_1 \leq y) P(X_2 \leq y) .... P(X_n \leq y) = [P(X_1 \leq y)]^n = (\frac{y}{a})^n

Since all the random variables have the same distribution.  

Now we can find the density function derivating the distribution function like this:

f_Y (Y) = n (\frac{y}{a})^{n-1} * \frac{1}{a}= \frac{n}{a^n} y^{n-1} , y \in [0,a]

Now we can find the expected value for the random variable Y and we got this:

E(Y) = \int_{0}^a \frac{n}{a^n} y^n dy = \frac{n}{a^n} \frac{a^{n+1}}{n+1}= \frac{an}{n+1}

And the bias is given by:

E(Y)-a=\frac{an}{n+1} -a=\frac{an-an-a}{n+1}= -\frac{a}{n+1}

And again since the bias is not 0 we have a biased estimator.

Part e

For this case we have two estimators with the following variances:

V(\hat a_1) = \frac{a^2}{3n}

V(\hat a_2) = \frac{a^2}{n(n+2)}

On this case we see that the estimator \hat a_1 is better than \hat a_2 and the reason why is because:

V(\hat a_1) > V(\hat a_2)

\frac{a^2}{3n}> \frac{a^2}{n(n+2)}

n(n+2) = n^2 + 2n > n +2n = 3n and that's satisfied for n>1.

8 0
4 years ago
6th grade math !!!!!!!!!!!!!!!!
dsp73

Answer:

4th one

Step-by-step explanation:

5 0
3 years ago
Read 2 more answers
The population of a city increases by 3.1% per year. If this year's population is 119,000, what will next year's population be,
fredd [130]

Answer:

<u>The next year population is 122,689</u>

Step-by-step explanation:

<u>The computation of the next year population is as follows:</u>

<u>= This year population × increase percentage</u>

<u>= 119,000 × (1 + 0.031)</u>

<u>= 119,000 × 1.031</u>

<u>= 122,689</u>

<u>Hence, the  next year population is 122,689</u>

<u>The above formula is applied so that the correct value of the population could come</u>

<u>and the same is relevant</u>

- Hope this helped! ^ω^

3 0
3 years ago
Which two factors of -48 sum to 2
Rufina [12.5K]
-6 and 8
-6+8=2
(-6)(8)=-48
5 0
3 years ago
WILL GIVE BRAINLIEST!
Mariana [72]
An hour is 60 mins long. 30 mins is half of it. If we take 12 miles in an hour, divide it by 2, it will be 6 miles in 30 mins, and if we take 8 miles, divide it by 2, it would be 4 miles in 30 mins. Since the questions telling us how far apart are north cyclists, we’re adding 6 and 4 to get 10 miles. In conclusion, both cyclists are 10 miles apart. I might be wrong, but that’s the best I can do.
5 0
3 years ago
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