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4 years ago

What is 787,206 rounded to the nearest ten

Mathematics

2 answers:

horsena [70]4 years ago

8 0

Answer:

787,210

Step-by-step explanation:

insens350 [35]4 years ago

4 0

Answer:

787,210

Step-by-step explanation:

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Just learn this throughout a quick lesson, can someone help?

suter [353]

Answer:

The Pythagorean theorem is a^2+b^2=c^2 where c is the length of the longest, diagonal side (diagonal means opposite the right angle/small square).

Step-by-step explanation:

If you're looking for a short side (i.e. next to the right angle), square the longest one and subtract the square of the other known.

If you're looking for a long side (i.e. opposite the right angle), square both you know and add them together.

In both cases, take the Square Root with your calculator, or by trial and error, find a number that squares to make your current number.

[1] A square of the number x is x times x.

6 0

4 years ago

what is the diameter of 1e-5 meters? PLEASE GIVE ME THE RIGHT ANSWER I WILL MARK U AS BRAINLIEST... :)

joja [24]

I'm not sure exactly what you're asking but 1×10^-5 is .00001. I could be interpreting your question wrong in which case I'm sorry.

4 0

3 years ago

Let X1 and X2 be independent random variables with mean μand variance σ².

My name is Ann [436]

Answer:

a) $E(\hat \theta_1) =\frac{1}{2} [E(X_1) +E(X_2)]= \frac{1}{2} [\mu + \mu] = \mu$

So then we conclude that $\hat \theta_1$ is an unbiased estimator of $\mu$

$E(\hat \theta_2) =\frac{1}{4} [E(X_1) +3E(X_2)]= \frac{1}{4} [\mu + 3\mu] = \mu$

So then we conclude that $\hat \theta_2$ is an unbiased estimator of $\mu$

b) $Var(\hat \theta_1) =\frac{1}{4} [\sigma^2 + \sigma^2 ] =\frac{\sigma^2}{2}$

$Var(\hat \theta_2) =\frac{1}{16} [\sigma^2 + 9\sigma^2 ] =\frac{5\sigma^2}{8}$

Step-by-step explanation:

For this case we know that we have two random variables:

$X_1 , X_2$ both with mean $\mu = \mu$ and variance $\sigma^2$

And we define the following estimators:

$\hat \theta_1 = \frac{X_1 + X_2}{2}$

$\hat \theta_2 = \frac{X_1 + 3X_2}{4}$

Part a

In order to see if both estimators are unbiased we need to proof if the expected value of the estimators are equal to the real value of the parameter:

$E(\hat \theta_i) = \mu , i = 1,2$

So let's find the expected values for each estimator:

$E(\hat \theta_1) = E(\frac{X_1 +X_2}{2})$

Using properties of expected value we have this:

$E(\hat \theta_1) =\frac{1}{2} [E(X_1) +E(X_2)]= \frac{1}{2} [\mu + \mu] = \mu$

So then we conclude that $\hat \theta_1$ is an unbiased estimator of $\mu$

For the second estimator we have:

$E(\hat \theta_2) = E(\frac{X_1 + 3X_2}{4})$

Using properties of expected value we have this:

$E(\hat \theta_2) =\frac{1}{4} [E(X_1) +3E(X_2)]= \frac{1}{4} [\mu + 3\mu] = \mu$

So then we conclude that $\hat \theta_2$ is an unbiased estimator of $\mu$

Part b

For the variance we need to remember this property: If a is a constant and X a random variable then:

$Var(aX) = a^2 Var(X)$

For the first estimator we have:

$Var(\hat \theta_1) = Var(\frac{X_1 +X_2}{2})$

$Var(\hat \theta_1) =\frac{1}{4} Var(X_1 +X_2)=\frac{1}{4} [Var(X_1) + Var(X_2) + 2 Cov (X_1 , X_2)]$

Since both random variables are independent we know that $Cov(X_1, X_2 ) = 0$ so then we have:

$Var(\hat \theta_1) =\frac{1}{4} [\sigma^2 + \sigma^2 ] =\frac{\sigma^2}{2}$

For the second estimator we have:

$Var(\hat \theta_2) = Var(\frac{X_1 +3X_2}{4})$

$Var(\hat \theta_2) =\frac{1}{16} Var(X_1 +3X_2)=\frac{1}{4} [Var(X_1) + Var(3X_2) + 2 Cov (X_1 , 3X_2)]$

Since both random variables are independent we know that $Cov(X_1, X_2 ) = 0$ so then we have:

$Var(\hat \theta_2) =\frac{1}{16} [\sigma^2 + 9\sigma^2 ] =\frac{5\sigma^2}{8}$

7 0

3 years ago

Gabriel ran for 1 mile. Then he started jogging. He jogged for 250 feet. How many total feet did he run and jog?

Marizza181 [45]

1 mile is 5280 feet so when adding both 5,280 and 250 feet you get 5,530.

8 0

4 years ago

Ben works at a mobile phone store, where he earns a flat $80 for each 8-hour shift. He also earns a commission of $20 for each p

horrorfan [7]

Answer:

Step-by-step explanation:

he makes 80 dollars for a normal shift

20 dollars for every phone he sells 20m

and e is the total amount

e=20m+80

4 0

4 years ago