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zavuch27 [327]
2 years ago
7

I don’t know how to do this because my teacher doesn’t teach us anything.

Mathematics
1 answer:
jek_recluse [69]2 years ago
6 0

Answer: Choice D)

He needs to square each deviation in the numerator.

==========================================================

Explanation:

The sample standard deviation involves these steps

  1. Find the sample mean xbar.
  2. Subtract the xbar value from each data point. This gets us the list of deviations or how far each person is from the mean.
  3. Square each difference in the previous step
  4. Add up the squares from step 3. This is referred to as the Sum of the Squared Errors (SSE)
  5. Divide the SSE over n-1. If Toby was doing a population standard deviation, then he'd divide over n instead of n-1. However in much of statistics, we don't know much about a population and instead rely on sample statistics.
  6. The result of step 5 gets the variance. Apply the square root to the variance to get the standard deviation.

The step 3 highlighted in bold is where Toby made an error. He subtracted xbar from each person correctly, but forgot to square those deviations. The squaring is done to ensure we don't have any negative deviations.

This is what his work should look like when calculating the sample standard deviation

s = \sqrt{\frac{(6-7)^2 + (5-7)^2 + (9-7)^2 + (8-7)^2}{3}}

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Answer:

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Step-by-step explanation:

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3 years ago
The amount of potato chips an 18-ounce bag contains follows a normal distribution with a mean of 18.5 ounces and a standard devi
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Answer:

100% probability that the sample mean weight of these 100 bags is less than 18.6 ounces

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the zscore of a measure X is given by:

Z = \frac{X - \mu}{\sigma}

The Z-score 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. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

In this question, we have that:

\mu = 18.5, \sigma = 0.2, n = 100, s = \frac{0.2}{\sqrt{100}} = 0.02

What is the probability that the sample mean weight of these 100 bags is less than 18.6 ounces

This is the pvalue of Z when X = 18.6. So

Z = \frac{X - \mu}{\sigma}

By the Central Limit Theorem

Z = \frac{X - \mu}{s}

Z = \frac{18.6 - 18.5}{0.02}

Z = 5 has a pvalue of 1

100% probability that the sample mean weight of these 100 bags is less than 18.6 ounces

7 0
3 years ago
Keith has 500 in a savings account at the beginning of the summer he wants to have at least 200 at the end of the summer he with
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500 - 25w >= 200

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Answer: Keith can withdraw $25 per week for at most 12 weeks.

8 0
4 years ago
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Answer:

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Answer:

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Step-by-step explanation:

Hope this is correct, if not feel free to let me know. I'm sorry in advance if this is wrong.

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