1answer.
Ask question
Login Signup
Ask question
All categories
  • English
  • Mathematics
  • Social Studies
  • Business
  • History
  • Health
  • Geography
  • Biology
  • Physics
  • Chemistry
  • Computers and Technology
  • Arts
  • World Languages
  • Spanish
  • French
  • German
  • Advanced Placement (AP)
  • SAT
  • Medicine
  • Law
  • Engineering
My name is Ann [436]
3 years ago
11

Help me please

Mathematics
2 answers:
torisob [31]3 years ago
7 0

Answer:

26 ft

Step-by-step explanation:

Mama L [17]3 years ago
3 0
The answer is 26 ft
You might be interested in
Is 10 thousands, 5 hundreds greater than or less than 1,050 tens
Olegator [25]
Its greater than 1,050 tens.
4 0
3 years ago
Read 2 more answers
Explain how to write and solve an equation to find an unknown side length of a rectangle when given the perimeter
ratelena [41]
P=2(L+W)
if given one side and the perimiter
(P/2)-L=W
(P/2)-W=L
4 0
2 years ago
Can you answer these questions?
vitfil [10]
Both of them are the first answer choice
4 0
3 years ago
Read 2 more answers
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
3 years ago
(4x)m+(5x+3)m+(5x+1)m=102 <br> Find the sides of the triangle. Perimeter =102
11Alexandr11 [23.1K]

Here is the set up:

Side 1 is 4x.

Side 2 is 5x + 3

Side 3 is 5x + 1

The perimeter is 102.

Perimeter = side 1 + side 2 + side 3

102 = 4x + 5x + 3 + 5x + 1

102 = 14x + 4

Take it from here.

5 0
2 years ago
Other questions:
  • i need an equation and i am stuck on this one. there is a pattern but i cant figure out what! help me please!!
    15·2 answers
  • Determine which ordered pair is a solution of y = -5x + 10.
    14·1 answer
  • What must be true about two integers thst combine to equal zero?
    7·1 answer
  • Michael invested 5,000 in an account that has a 5.5% annual interest rate. What equation best describes investment after T years
    13·1 answer
  • Use the theoretical method to determine the probability of the outcome or event given below.
    10·1 answer
  • The box plot shows the calorie count in a few meals from a fast food chain. What is the least number of calories in a meal?
    12·2 answers
  • Complete the table to find the value of nonzero number raised to the power of 0.
    8·2 answers
  • Henry examined the table that represents values satisfying the function f(x) shown below.
    11·1 answer
  • Solve the following:<br> 6(2x+3)-3(x-2)=6
    11·2 answers
  • James has 3 blue candies and 3 green candies. 3 people come in and take 2 candies. What is the probability that they do not take
    10·1 answer
Add answer
Login
Not registered? Fast signup
Signup
Login Signup
Ask question!