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Sidana [21]
4 years ago
9

Which shows the graph of the solution set of 3y – 2x > –18?

Mathematics
2 answers:
miv72 [106K]4 years ago
7 0
Were are the graphs?
dmitriy555 [2]4 years ago
6 0

Answer:

3y-2x>-18

Step-by-step explanation:

We are given with a linear equation  3y-2x> -18 . We have to draw this inequality.

In order to draw this inequality , we have to first draw the graph of

3y-2x= -18

Let us do it , by converting the equation into intercept form, and find the x and y intercepts.

Divide both side, each term by -18 , we get

\frac{3y}{-18}-\frac{2x}{-18}= \frac{-18}{-18}

\frac{y}{-6}-\frac{x}{-9}= 1

\frac{y}{-6}+\frac{x}{9}= 1

\frac{x}{9}+\frac{y}{-6}= 1

Hence our x intercept = 9

y intercept = -6

Hence the line passes through the coordinates (9,0) and (0,-6). We now plot them on graph and draw our line.

Now we have to check which region to shade.

Our inequality is given as 3y-2x>-18

Let us see whether (0,0) satisfies this inequality. For that we need to substitute them in our equation.

3(0)-2(0)>-18

0-0>-18

0>-18

Which is true .

Hence , we shade the region which is containing (0,0) . Also our line need to be broken as it is containing > sign in it.

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Student scores on exams given by a certain instructor have mean 74 and standard deviation 14. This instructor is about to give t
Lapatulllka [165]

Answer:

P(\bar X >80)=P(Z>2.143)=1-P(z

Step-by-step explanation:

Normal distribution, is a "probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean".

Let X the random variable that represent the Student scores on exams given by a certain instructor, we know that X have the following distribution:

X \sim N(\mu=74, \sigma=14)

The sampling distribution for the sample mean is given by:

\bar X \sim N(\mu,\frac{\sigma}{\sqrt{n}})

The deduction is explained below we have this:

E(\bar X)= E(\sum_{i=1}^{n}\frac{x_i}{n})= \sum_{i=1}^n \frac{E(x_i)}{n}= \frac{n\mu}{n}=\mu

Var(\bar X)=Var(\sum_{i=1}^{n}\frac{x_i}{n})= \frac{1}{n^2}\sum_{i=1}^n Var(x_i)

Since the variance for each individual observation is Var(x_i)=\sigma^2 then:

Var(\bar X)=\frac{n \sigma^2}{n^2}=\frac{\sigma}{n}

And then for this special case:

\bar X \sim N(74,\frac{14}{\sqrt{25}}=2.8)

We are interested on this probability:

P(\bar X >80)

And we have already found the probability distribution for the sample mean on part a. So on this case we can use the z score formula given by:

z=\frac{\bar X -\mu}{\frac{\sigma}{\sqrt{n}}}

Applying this we have the following result:

P(\bar X >80)=P(Z>\frac{80-74}{\frac{14}{\sqrt{25}}})=P(Z>2.143)

And using the normal standard distribution, Excel or a calculator we find this:

P(Z>2.143)=1-P(z

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Subtract the part from the total:

102.50 - 15 = 87.50

Now divide the remainder by rate:

87.50 / 25 = 3.5 hours

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