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3 years ago

Perform the following operation, and report the answer to the correct number of significant figures. 3.47 x 6.7

Mathematics

1 answer:

kobusy [5.1K]3 years ago

6 0

Answer:

Step-by-step explanation:

3.47 = 3 sig. figs.

6.7 = 2 sig. figs.

Since these two numbers are being multiplied, you would round to the smallest number of sig. figs. of the two. In this case, it would be 2 significant figures.

3.47 * 6.7 = 23.249 (dont forget to round to 2 sig. figs. ) --> 23

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Write a word problem for the equation. <br> 1/2 (x-4) = 1/3x + 2

Ganezh [65]

Answer:

Half the age of Carlos four years ago is 2 years more than one-third his current age.

Step-by-step explanation:

We want to write a word problem for the equation.

$\frac{1}{2} (x - 4) = \frac{1}{3}x + 2$

Let x represent the present age of Carlos.

Four years ago, Carlos was x-4 years.

Half his age four years ago is ½(x-4)

Two years more than one-third his current age is ⅓x+2

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3 years ago

Determine if the following statement is true or false.

Bas_tet [7]

Answer:

False

Step-by-step explanation:

Negative and Negative always equals positive

Negative and positive always equal negative

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2 years ago

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Suppose that W1, W2, and W3 are independent uniform random variables with the following distributions: Wi ~ Uni(0,10*i). What is

nadya68 [22]

I'll leave the computation via R to you. The $W_i$ are distributed uniformly on the intervals $[0,10i]$ , so that

$f_{W_i}(w)=\begin{cases}\dfrac1{10i}&\text{for }0\le w\le10i\\\\0&\text{otherwise}\end{cases}$

each with mean/expectation

$E[W_i]=\displaystyle\int_{-\infty}^\infty wf_{W_i}(w)\,\mathrm dw=\int_0^{10i}\frac w{10i}\,\mathrm dw=5i$

and variance

$\mathrm{Var}[W_i]=E[(W_i-E[W_i])^2]=E[{W_i}^2]-E[W_i]^2$

We have

$E[{W_i}^2]=\displaystyle\int_{-\infty}^\infty w^2f_{W_i}(w)\,\mathrm dw=\int_0^{10i}\frac{w^2}{10i}\,\mathrm dw=\frac{100i^2}3$

so that

$\mathrm{Var}[W_i]=\dfrac{25i^2}3$

Now,

$E[W_1+W_2+W_3]=E[W_1]+E[W_2]+E[W_3]=5+10+15=30$

and

$\mathrm{Var}[W_1+W_2+W_3]=E\left[\big((W_1+W_2+W_3)-E[W_1+W_2+W_3]\big)^2\right]$

$\mathrm{Var}[W_1+W_2+W_3]=E[(W_1+W_2+W_3)^2]-E[W_1+W_2+W_3]^2$

We have

$(W_1+W_2+W_3)^2={W_1}^2+{W_2}^2+{W_3}^2+2(W_1W_2+W_1W_3+W_2W_3)$

$E[(W_1+W_2+W_3)^2]$

$=E[{W_1}^2]+E[{W_2}^2]+E[{W_3}^2]+2(E[W_1]E[W_2]+E[W_1]E[W_3]+E[W_2]E[W_3])$

because $W_i$ and $W_j$ are independent when $i\neq j$ , and so

$E[(W_1+W_2+W_3)^2]=\dfrac{100}3+\dfrac{400}3+300+2(50+75+150)=\dfrac{3050}3$

giving a variance of

$\mathrm{Var}[W_1+W_2+W_3]=\dfrac{3050}3-30^2=\dfrac{350}3$

and so the standard deviation is $\sqrt{\dfrac{350}3}\approx\boxed{116.67}$

# # #

A faster way, assuming you know the variance of a linear combination of independent random variables, is to compute

$\mathrm{Var}[W_1+W_2+W_3]$

$=\mathrm{Var}[W_1]+\mathrm{Var}[W_2]+\mathrm{Var}[W_3]+2(\mathrm{Cov}[W_1,W_2]+\mathrm{Cov}[W_1,W_3]+\mathrm{Cov}[W_2,W_3])$

and since the $W_i$ are independent, each covariance is 0. Then

$\mathrm{Var}[W_1+W_2+W_3]=\mathrm{Var}[W_1]+\mathrm{Var}[W_2]+\mathrm{Var}[W_3]$

$\mathrm{Var}[W_1+W_2+W_3]=\dfrac{25}3+\dfrac{100}3+75=\dfrac{350}3$

and take the square root to get the standard deviation.

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3 years ago

American households increasingly rely on cell phones as their exclusive telephone service. It is reported that 50.4% of American

OLga [1]

The variance of the binomial distribution of the number of households with landline service is 2.

<h3>What is the binomial probability distribution?</h3>

It is the probability of <u>exactly x successes on n repeated trials, with p probability</u> of a success on each trial.

The variance of the distribution is given by:

V(X) = np(1 - p)

In this problem, the parameters are given as follows:

n = 8, p = 0.504.

Hence the variance is given by:

V(X) = 8 x 0.504 x 0.496 = 2.

More can be learned about the binomial distribution at brainly.com/question/24863377

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2 years ago

A store bought a kitchen play set for $34.93 and sold it for $89.25. What was the markup percentage? Round your answer to the ne

Svetradugi [14.3K]

Answer:

155.5%

Step-by-step explanation:

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2 years ago

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