(-6y + 8y4 − 5)(y2 − 11y)

Domain: [tex]x \ge 0[/tex]

Domain in interval notation:   [0, infinity)

This is because the domain is the set of all allowed x inputs. In this case, x = 0 is the smallest and we include this endpoint. Any x value x = 0 or larger is part of the domain (since the graph stretches' on forever to the right)

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Range:  [tex]y \ge 2[/tex]

Range in interval notation:   [2, infinity)

For similar reasoning as the domain, but now we consider the possible y output values. Here y = 2 is the smallest and we include it. Note the square bracket for interval notation means "include this endpoint". The curved parenthesis means "exclude this endpoint"

Answer:

x>5/2

Step-by-step explanation:

Answer:

25, 25, 22.5, 22

Step-by-step explanation:

It gives you a hint; K = y/x. They give you what x and y are so use that to solve for the last row.

We are given all of these values:

x -> 2, 3, 4, 5

y -> 50, 75, 90, 110

Use the formula given and solve for K.

50/2 = 25

75/3 = 25

90/4 = 22.5

110/5 = 22

Best of Luck!

Answer:

72

Step-by-step explanation:

brainliest please

Answer:

42.09

Step-by-step explanation:

3.45 x 12.2 = 42.09 (checked with a calculator)

Answer:

P(X< 40) = 0.014367

Step-by-step explanation:

From the given information:

Suppose X denotes the strength of each steel strand & X follows the lognormal distribution with a mean value [tex]\mu_x[/tex] = 50 kips & coefficient of variation CV = 10%

Using the formula for the coefficient of variation CV

[tex]cv = \dfrac{\sigma_x}{\mu_x}[/tex]

[tex]0.10 = \dfrac{\sigma_x}{50}[/tex]

[tex]\sigma_x = 50 (0.10)[/tex]

[tex]\sigma_x = 5[/tex]

By applying the mean and mean & variance from the lognormal distribution, we have:

[tex]\dfrac{\sigma_x^2}{\mu_x}= \dfrac{\{exp(2 \mu + \sigma^2)\} (exp(\sigma^2))-1}{exp( \mu + \dfrac{\sigma^2}{2} \}}[/tex]

[tex]\dfrac{5^2}{50}= \dfrac{\{exp(2( \mu + \dfrac{\sigma^2}{2})) \} (exp(\sigma^2))-1}{exp( \mu + \dfrac{\sigma^2}{2} \}}[/tex]

[tex]\dfrac{5^2}{50}= exp \{ 2 ( \mu + \dfrac{\sigma^2}{2}) - ( \mu + \dfrac{\sigma^2}{2}) \} \{ ( exp ( \sigma^2))-1 \}[/tex]

[tex]\dfrac{5^2}{50}=exp \{(\mu + \dfrac{\sigma^2}{2}) \} {(exp (\sigma^2))-1 \}[/tex]

[tex]\dfrac{5^2}{50}=50 \{ (exp (\sigma^2 )) -1 \}[/tex]

[tex]\dfrac{25}{50 \times 50 }= ( exp (\sigma^2 )) -1[/tex]

[tex]exp( \sigma^2) = \dfrac{25}{50 \times 50}+ 1[/tex]

[tex]exp( \sigma^2) = (\dfrac{25+2500}{2500 })[/tex]

[tex]exp( \sigma^2) =1.01[/tex]

[tex]\sigma^2 = In (1.01)[/tex]

[tex]\mathbf {\sigma^2 = 0.00995}[/tex]

However, the next process is to replace the value of [tex]\sigma^2[/tex] into the logman distributions mean.

[tex]\mu_x = exp ( \mu + \dfrac{\sigma^2}{2})[/tex]

[tex]50 = exp ( \mu + \dfrac{\sigma^2}{2})[/tex]

[tex]50 = exp ( \mu + \dfrac{0.00995}{2})[/tex]

[tex]In(50) = ( \mu + \dfrac{0.00995}{2})[/tex]

3.912023 = μ +0.004975

μ = 3.907048

Now, we know our mean to be  3.907048 and the variance to be 0.00995.

Therefore, the probability that the weakest strand will have a strength of fewer than 40 kips can be computed as follows:

P(X< 40) = P(In(x) < In(40))

[tex]P(X<40) = P(\dfrac{In(x) - \mu}{\sigma} < \dfrac{In(40) - \mu}{\sigma})[/tex]

since;

[tex]\sigma^2 = 0.00995 \\ \\ \sigma = \sqrt{0.00995} \\ \\ \sigma = 0.0998[/tex]

[tex]P(X<40) = P(Z < \dfrac{In(40) - 3.907048}{0.0998})[/tex]

P(X< 40) = P(Z < -2.18712)

Using the Excel formula =(=NORMDIST (-2.18712)

P(X< 40) = 0.014367

Answer:It would be c

Step-by-step explanation:because every 1 shaded rectangle comes 3 shaded triangles so the ratio would be 3:1 and it would not be 1:3 because you go the way the question asks first if that makes any sense

Answer:

c

Step-by-step explanation:

Answer:

-1

Step-by-step explanation:

♡♤□●€£€■□●○{€》《●○○●}□¤●■¥♤■□■♤}●●□¤□■■■□{}€€□□□■€♤《■■●●□■■■■

Answer:

32.55

Step-by-step explanation:

d = 32.552266

For:

(X1, Y1) = (-12.3, 15.1)

(X2, Y2) = (17.5, 2)

Distance Equation Solution:

d=(17.5−(−12.3))2+(2−15.1)2−−−−−−−−−−−−−−−−−−−−−−−−−√

d=(29.8)2+(−13.1)2−−−−−−−−−−−−−−−√

d=888.04+171.61−−−−−−−−−−−−−√

d=1–√059.65

d=32.552266

could be confusing.

Answer:

Cannot prove as congruent

Step-by-step explanation:

AAA cannot be used to prove whether a triangle is congruent. Only SSS, SAS, ASA, and AAS can be used.

Answer:

((c)) man so simple. bye

Answer:

x = 14

General Formulas and Concepts:

Pre-Algebra

Order of Operations: BPEMDAS

Algebra I

Geometry

Step-by-step explanation:

Step 1: Define

3(x + 7)° = 63°

3(x + 7)° + 63° + y° = 180°

Step 2: Solve for x

seventy five pounds

Step-by-step explanation:

because the colors make him heavy duh

Answer:

the answer is ......

Step-by-step explanation:

9514 1404 393

Answer:

Step-by-step explanation:

There is some disagreement over whether the "imaginary part" includes the factor "i" or not. Here, we assume it is simply the coefficient of i.

  Re(z) = -19 . . . . the part that does not have a factor of i

  Im(z) = -24 . . . . the coefficient of i

Answer:

0.62

Step-by-step explanation:

divide 56 by 90

Hope this helps :)

Answer:

0.622222222222

Step-by-step explanation:

Answer:

A

Step-by-step explanation:

i just took the lab assessment

Answer:

(-5,7) that will be your answer.

Answer:

Doug will have $9,651 after 6 years.

Step-by-step explanation:

it's hard to explain it step-by-step, but you can watch a video on it if you want to know how to do it.

Answer:

Can someone help, please?

-1/2(7z+4)+1/5(5z-15)

Answer:

21

Step-by-step explanation:

bc i said

Answer:

The first one is c the second is b

The answer is -3/-1/2

Tap on a clip to paste it in the text box.

1274 divided by 14 equals 91, see attachment below

Answer:

In this case, we do not have the table, so i will answer in a general way:

First, let's describe the transformation.

Vertical shift.

If we have a function f(x), a vertical shift of N units is written as:

g(x) = f(x) + N

This will move the graph of f(x) up or down a distance of N units.

if N is positive, then the shift is upwards

if N is negative, then the shift is downwards.

Then a shift of 3 units up, is written as:

g(x) = f(x) + 3

This would be translated to the table in the next way:

You need to add 3 to all the values of y (or f(x)) in the table:

So if for example, the original table was:

x            f(x)

0             5

1              7

2             10

3              15

The table of the transformation will be:

x              g(x)

0              5 + 3 = 8

1               7 + 3 = 10

2              10 + 3 = 13

3               15 + 3 = 18

Answer:

The probability is 0.5263

Step-by-step explanation:

Probability of employed women being unmarried = 25% = 0.25 ( let this be p)

Probability of employed women ever being married = 1-0.25 = 0.75 (let this be q)

Now, we want to calculate the probability that two or less have never been married

We can use the Bernoulli approximation of the Binomial distribution here;

In this case, we have three cases

We have cases of;

Exactly two + Exactly one + None

For exactly two;

10 C 2 0.25^2 0.75^8 = 0.282

For exactly one

10 C 1 0.25^1 0.75^9 = 0.188

For none

10C 0 0.25^0 0.75^10 = 0.0563

Adding these 3 to get the probability of two or fewer, we have;

0.0563 + 0.188 + 0.282 = 0.5263

c. The distribution of all possible values that can be assumed by some statistic, computed from samples of the same size randomly drawn from the same population.

c. The distribution of all possible values that can be assumed by some statistic, computed from samples of the same size randomly drawn from the same population.

A population is the group of people from which a statistical sample is taken in statistics. Therefore, a population is any collection of people who have something in common. A statistically significant portion of a population, rather than the full population, may also be referred to as a sample.

When surveying a big number of people, it is just impracticable to examine everyone who fits the description you are looking at using a population parameter. It is often easier to study a smaller sample of participants, but by applying inferential statistics, it is possible to get a good approximation of the population parameter.

thus, statistical population is the distribution of all of the statistics calculated from all possible samples of the same size.

Therefore, The statistical population, which is calculated from samples of the same size randomly selected from the same population, is the distribution of all potential values that can be assumed by a statistic.

Learn more about statistical population here:

https://brainly.com/question/8222250

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