Ella has 164 ounces of lemonade. She fills glasses with8 ounces of lemonade each until all the lemonade is gone.
The last glass is not full. How much lemonade is in the
last glass?

Answers

Answer 1
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.

164/8=20.5

So 2 full glasses were made.

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

20\cdot8=160\n\n164-160=4

Hope this helped!

Answer 2
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.


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You are hiking up a mountain peak. You begin hiking at a trailhead whose elevation is about 9400 feet. The trail ends near the summit at 14,255 feet. The horizontal distance between these two points is about 17,625 feet. Estimate the angle of elevation from the trailhead to the summit.

Answers

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°

which of the following is among the likely causes of a non-random sample? a. non response bias b. parameter c. statistics d. population

Answers

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.

Proportional relationships

Answers

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:

A store charges 6% GCT on all sales. What is the total cost of a shirt marked at $30? *2 points
Your answer

Answers

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

30*0.06=1.80

30+1.80=31.80

final cost: $31.80

Hope this helps!

Piper is going to invest in an account paying an interest rate of 6.1 % compounded continuously

Answers

Answer:

P ≈ 90739.60

Step-by-step explanation:

According to a recent Catalyst Census, 16% of executive officers were women with companies that have company headquarters in the Midwest. A random sample of 154 executive officers from these companies was selected. What is the probability that more than 20% of this sample is comprised of female employees?

Answers

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.

Normal Probability Distribution

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

Z = (X - \mu)/(\sigma)

  • 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 \mu = p and standard deviation s = \sqrt{(p(1 - p))/(n)}, as long as np \geq 10 and n(1 - p) \geq 10.

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:

\mu = p = 0.16

s = \sqrt{(p(1 - p))/(n)} = \sqrt{(0.16(0.84))/(154)} = 0.0295

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:

Z = (X - \mu)/(\sigma)

By the Central Limit Theorem

Z = (X - \mu)/(s)

Z = (0.2 - 0.16)/(0.0295)

Z = 1.36

Z = 1.36 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 :-

P(p>0.20)=P(z>\frac{0.20-0.16}{\sqrt{(0.16(1-0.16))/(154)}})

[∵ z=\frac{\hat{p}-p}{\sqrt{(p(1-p))/(n)}}]

=P(z>1.35)\approx1-P(z\leq1.35)=1-0.9115=0.0885  [Using the standard z-value table]

Hence, the required probability = 0.0885