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3 years ago

8

In a random sample 70 people, it was found that 44 of them were fans of NY Yankees. What is the margin of error for the true pro

portion of all individuals who are fans of the NY Yankees?
0.0088
0.058
0.063
0.116
0.173
What's the answer

1 answer:

stich3 [128]3 years ago

5 0

Answer:

C

Step-by-step explanation:

44/70 which is about 0.063 Hope this helps :D Please mark brainliest

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Will mark BRAINLIEST! PLEASE HELP!

yulyashka [42]

Answer:

3 bob's burger= 23.67

2 starry night= 17.98

4 clear= 42

Step-by-step explanation:

3 x 7.89= 23.67

2 x 8.99= 17.98

4 x 10.50= 42

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3 years ago

The chu family can hike 3 miles per hour how long will it take them to hike 15 miles

s344n2d4d5 [400]

Answer:

5 hours

Step-by-step explanation:

if they hike 3 miles per 1 hour, and you divide 15 (miles) by 3 (miles) you get 5 hours because each hour they walk 3 miles

(so sorry if this is confusing!! its very hard to explain for me lol)

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3 years ago

Find x and y<br><br><br> NEED HELP FASTTTTT

garri49 [273]

Answer:

x=2 y=2

Step-by-step explanation:

Second: x=3 y=1.5

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3 years ago

On a social studies test, James scored a 2025, Jules earned an 81%, and Scott scored a 0.815. Who earned the highest grade? Just

Airida [17]

Answer:

Scott

Step-by-step explanation:

20/25=.8 .8=80%

.815*100=81.5%

81%

After finding a decimal and multiplying by 100, you get the percent so you can compare. 81.5% is the highest, so Scott had the highest score.

Hope this helps. Please put brainliest if you can!

7 0

3 years ago

Let X1, X2, ... , Xn be a random sample from N(μ, σ2), where the mean θ = μ is such that −[infinity] < θ < [infinity] and

Sliva [168]

Answer:

$l'(\theta) = \frac{1}{\sigma^2} \sum_{i=1}^n (X_i -\theta)$

And then the maximum occurs when $l'(\theta) = 0$ , and that is only satisfied if and only if:

$\hat \theta = \bar X$

Step-by-step explanation:

For this case we have a random sample $X_1 ,X_2,...,X_n$ where $X_i \sim N(\mu=\theta, \sigma)$ where $\sigma$ is fixed. And we want to show that the maximum likehood estimator for $\theta = \bar X$ .

The first step is obtain the probability distribution function for the random variable X. For this case each $X_i , i=1,...n$ have the following density function:

$f(x_i | \theta,\sigma^2) = \frac{1}{\sqrt{2\pi}\sigma} exp^{-\frac{(x-\theta)^2}{2\sigma^2}} , -\infty \leq x \leq \infty$

The likehood function is given by:

$L(\theta) = \prod_{i=1}^n f(x_i)$

Assuming independence between the random sample, and replacing the density function we have this:

$L(\theta) = (\frac{1}{\sqrt{2\pi \sigma^2}})^n exp (-\frac{1}{2\sigma^2} \sum_{i=1}^n (X_i-\theta)^2)$

Taking the natural log on btoh sides we got:

$l(\theta) = -\frac{n}{2} ln(\sqrt{2\pi\sigma^2}) - \frac{1}{2\sigma^2} \sum_{i=1}^n (X_i -\theta)^2$

Now if we take the derivate respect $\theta$ we will see this:

$l'(\theta) = \frac{1}{\sigma^2} \sum_{i=1}^n (X_i -\theta)$

And then the maximum occurs when $l'(\theta) = 0$ , and that is only satisfied if and only if:

$\hat \theta = \bar X$

6 0

3 years ago