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3 years ago

Show number line -3, -8, -2 and 5

Mathematics

1 answer:

Elina [12.6K]3 years ago

5 0

__-8__-7__-6__-5__-4__-3__-2__-1__0__1__2__3__4__5__6__7__8__

this is the best I could get to a number line hope this helps you:)

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If Andre sold 16 magazine subscriptions this week, would he reach his goal? Explain your reasoning.

Alenkasestr [34]

Step-by-step explanation:

what's his goal I will give answer in comments after you tell me

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Find the average range. Round the mean and the average range to the nearest tenth. (Show all your work on scratch paper, then en

NISA [10]

3, 12, 15, 25, 30, 40The range is the difference between the greatest and smallest values:
range = 40 - 3 = 37

Thee average is the sum of elements divided by the number of elements:
average = (<span>3 + 12 + 15 + 25 + 30 + 40</span>)/6
average = 125/6 = 20.83

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3 years ago

Which of the following options have the same value as 60% of 94?

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Answer:

$60\% \: of \: 94 \\ = \frac{60}{100} \times 94 \\ = \frac{3}{5} \times \frac{94}{1} \\ = 18.4 \times 3 \\ = 55.2$

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Billy has been to three more than four times as many baseball games as Brendan. Brendan has been to 11 baseball games. Complete

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3 years ago

A random variable X follows the uniform distribution with a lower limit of 670 and an upper limit of 750.a. Calculate the mean a

DENIUS [597]

You can compute both the mean and second moment directly using the density function; in this case, it's

$f_X(x)=\begin{cases}\frac1{750-670}=\frac1{80}&\text{for }670\le x\le750\\0&\text{otherwise}\end{cases}$

Then the mean (first moment) is

$E[X]=\displaystyle\int_{-\infty}^\infty x\,f_X(x)\,\mathrm dx=\frac1{80}\int_{670}^{750}x\,\mathrm dx=710$

and the second moment is

$E[X^2]=\displaystyle\int_{-\infty}^\infty x^2\,f_X(x)\,\mathrm dx=\frac1{80}\int_{670}^{750}x^2\,\mathrm dx=\frac{1,513,900}3$

The second moment is useful in finding the variance, which is given by

$V[X]=E[(X-E[X])^2]=E[X^2]-E[X]^2=\dfrac{1,513,900}3-710^2=\dfrac{1600}3$

You get the standard deviation by taking the square root of the variance, and so

$\sqrt{V[X]}=\sqrt{\dfrac{1600}3}\approx23.09$

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3 years ago