The last glass is not full. How much lemonade is in the

last glass?

Answer:

**Answer:**

4 ounces

**Step-by-step explanation:**

We're looking for the remainder of ounces.

To find how many FULL glasses will be made, we divide 8 by 164 and look at the integer part of the number.

So 2 full glasses were made.

To find the remainder, we multiply 20 by 8 and subtract from 164.

Hope this helped!

Answer:

**Answer:**

4 ounces

**Step-by-step explanation:**

If you divide the 164 ounces by the 8 ounces per glass, you get 20.5 glasses filled. So, half of the last glass would be 4.

Simplify each equation 3(5x2+2x-4)-x7x2+2x-3)

80 orders in 10 days = 8 orders in days

Find an exact value of sin(17pi/12)

What is the angle (relative to the horizontal) of a 4 in 4 roof? Is it possible to have a 7 in 4 foot? Explain.

What is the result of adding these two equations?\begin{aligned} -5x-9y &= 3 \\\\ 5x-9y &= -2 \end{aligned} −5x−9y5x−9y =3=−2

80 orders in 10 days = 8 orders in days

Find an exact value of sin(17pi/12)

What is the angle (relative to the horizontal) of a 4 in 4 roof? Is it possible to have a 7 in 4 foot? Explain.

What is the result of adding these two equations?\begin{aligned} -5x-9y &= 3 \\\\ 5x-9y &= -2 \end{aligned} −5x−9y5x−9y =3=−2

**Answer:**

Estimate angle of elevation = 15.4°

**Step-by-step explanation:**

**Given:**

Start elevation = 9,400 ft

Ends near the summit = 14,255 ft

horizontal distance = 17,625 ft

**Find:**

Estimate angle of elevation.

**Computation:**

⇒ Total elevation distance = Ends near the summit - Start elevation

⇒ Total elevation distance = 14,255 - 9,400

⇒ Total elevation distance = 4,855 ft

⇒ Tan A = 4,855 / 17,625

⇒ Tan A = 0.2754

⇒ Tan A = 15.4°

**Estimate angle of elevation = 15.4°**

**Answer:**

**a. non response bias **

This is one of the usually cause when we use a non-random sample, sinc the probability of selection for all the individuals on the population is not the same when we use this, we are comitting non response bias since we are not taking in count some people in the possible target sample.

**Nonresponse bias** is "the bias that results when respondents differ in meaningful ways from nonrespondents. Nonresponse is often problem with mail surveys, where the response rate can be very low".

**Step-by-step explanation:**

**Random sample**

For this method we need the following two conditions:

(1) "Every element in our population has a nonzero probability of being selected as part of the sample."

(2)" We have accurate knowledge of this probability, known as the inclusion probability, for each element in the sampling frame".

**Non random sample**

It's the opposite of random sample and we have these problems associated:

(1) "It is relatively unusual to have a sampling frame available to you when you’re conducting market studies".

(2) "Ensuring that every individual in a population has a nonzero probability of being selected is just as difficult to accomplish; knowing every sampling unit’s exact inclusion probability is even more difficult. The individuals that cannot be selected as part of a sample are generally referred to as excluded units".

Assuming the following options:

**a. non response bias **

This is one of the usually cause when we use a non-random sample, sinc the probability of selection for all the individuals on the population is not the same when we use this, we are comitting non response bias since we are not taking in count some people in the possible target sample.

**Nonresponse bias** is "the bias that results when respondents differ in meaningful ways from nonrespondents. Nonresponse is often problem with mail surveys, where the response rate can be very low".

**b. parameter **

False we are looking for a cause related to non random sample. The parameter is just a value that we want to find but is not a cause related to the non random sample.

**c. statistics**

False, we can associate a cause of non random sample with the statistics. The term "statistics" is a big concept that involves a lot of methods and ways to analyze information, and is not the correct cause associated to the non-random sample.

**d. population**

False, we can associate the population as a cause of the non random sample. We use sampling methods in order to estimate some population parameters. But the population is not a cause of the non-random sample.

**Answer:**

relationships between two variables where their ratios are equivalent. Another way to think about them is that, in a proportional relationship, one variable is always a constant value times the other.

**Step-by-step explanation:**

Your answer

**Answer:**

$12

**Step-by-step explanation:**

**I hope this helps. Have a great day!**

**Answer:**

$31.80

**Step-by-step explanation:**

first convert 6% into a decimal which is 0.06. then multiply 30 by 0.06 to get 6% of 30 which is 1.80. then add 1.80 to 30 to get your final cost of 31.80

final cost: $31.80

Hope this helps!

**Answer:**

P ≈ 90739.60

**Step-by-step explanation:**

Using the *normal probability distribution and the central limit theorem*, it is found that there is a **0.0869 = 8.69% probability** that more than 20% of this sample is comprised of female employees.

In a normal distribution with **mean ** and **standard deviation **, the **z-score** of a **measure X** is given by:

- It
**measures**how many standard deviations the measure is from the mean.

- After finding the z-score, we look at the z-score table and find the
**p-value**associated with this z-score, which is the**percentile**of X.

- By the
**Central Limit Theorem**, for a proportion p in a sample of size n, the sampling distribution of sample proportion is approximately normal with mean and standard deviation , as long as and .

In this problem:

- 16% of executive officers were women with companies that have company headquarters in the Midwest, hence
**p = 0.16**.

- A random sample of 154 executive officers from these companies was selected, hence
**n = 154**.

The **mean **and the **standard error** are given by:

The **probability **that more than 20% of this sample is comprised of female employees is __1 subtracted by the p-value of Z when X = 0.2__, hence:

By the Central Limit Theorem

has a p-value of 0.9131.

1 - 0.9131 = 0.0869.

**0.0869 = 8.69% probability** that more than 20% of this sample is comprised of female employees.

To learn more about the *normal probability distribution and the central limit theorem*, you can take a look at brainly.com/question/24663213

**Answer: **0.0885

**Step-by-step explanation:**

**Given : **According to a recent Catalyst Census, 16% of executive officers were women with companies that have company headquarters in the Midwest.

i.e. p= 0.16

Sample size : n= 154

Now, the probability that more than 20% of this sample is comprised of female employees is given by :-

[∵ ]

[Using the standard z-value table]

**Hence, the required probability** = 0.0885