Normal distribution percentage problem.

In summary, the conversation discusses how to determine the minimum wage of the top 5% of employees at a company based on their average wage of $3.25 an hour and a standard deviation of $0.60. The conversation mentions using the z-score formula to find the 95th percentile, and suggests using a table or calculator to help with calculations.
  • #1
MACHO-WIMP
43
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Homework Statement


A company pays its employees an average wage of $3.25 an hour with a standard deviation of sixty cents. If the wages are approximately normally distributed, determine
the minimum wage of the employees who are paid the highest 5%.

Homework Equations


z=(x-μ)/σ

The Attempt at a Solution


I did:
.95=((x-3.25)/.6) which equals .57=x-3.25
therefore x=3.82. I feel like I didn't do this correctly and I can't finish the rest of my homework if I don't know what I'm doing. Thanks.
 
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  • #2
Isn't that equation for standardizing your values? 95% has nothing to do with z-scores.
 
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  • #3
tal444 said:
Isn't that equation for standardizing your values? 95% has nothing to do with z-scores.

Oh, shoot, you're right. Well how would I find out what 95% of the wages cause I don't have the slightest clue.
 
  • #4
Your z-value is +-σ from the mean. What is 95% in terms of σ on a normal distribution? Also, do you use calculators to help you in your class? If you do, this question should be quite straightforward.
 
  • #5
MACHO-WIMP said:
Oh, shoot, you're right. Well how would I find out what 95% of the wages cause I don't have the slightest clue.

If z_95 is the 95th percentile of the standard normal (which is available in tables or on some calculators) you need [tex] z_{95} = \frac{x-3.25}{0.6}.[/tex] Note: you should divide by 0.6 not 6, since 0.6 is the standard deviation in dollars.

RGV
 
  • #6
tal444 said:
Your z-value is +-σ from the mean. What is 95% in terms of σ on a normal distribution? Also, do you use calculators to help you in your class? If you do, this question should be quite straightforward.

No, my teacher wants us only using this table she gave us.
 

FAQ: Normal distribution percentage problem.

What is a normal distribution percentage problem?

A normal distribution percentage problem refers to a statistical problem in which the data follows a normal distribution, or bell-shaped curve. This means that most of the data falls near the mean or average, with fewer data points on the outer edges. The percentage aspect refers to finding the percentage of data falling within a certain range or standard deviation from the mean.

How do you calculate the percentage of data falling within a certain range in a normal distribution?

In a normal distribution, approximately 68% of the data falls within one standard deviation from the mean, 95% falls within two standard deviations, and 99.7% falls within three standard deviations. To calculate the percentage of data falling within a specific range, you can use a z-score table or a statistical software to find the area under the curve within that range.

What is the formula for calculating the z-score in a normal distribution percentage problem?

The z-score, also known as the standard score, is used to determine the position of a data point within a normal distribution. It is calculated by subtracting the mean from the data point and dividing by the standard deviation. The formula is (x-μ)/σ, where x is the data point, μ is the mean, and σ is the standard deviation.

How is a normal distribution percentage problem used in real-world applications?

A normal distribution percentage problem is commonly used in statistics and data analysis to understand and interpret data. It is used to determine how closely a set of data follows a normal distribution and to make predictions about future data points. It is also used in quality control and process improvement to identify and address any variations from the expected or desired outcome.

What are some common misconceptions about normal distribution percentage problems?

One common misconception is that all data follows a normal distribution. In reality, many real-world data sets do not perfectly follow a normal distribution. Another misconception is that the mean is always the most important value in a normal distribution. While the mean is an important measure of central tendency, the standard deviation also plays a crucial role in understanding the spread of the data. Additionally, it is important to note that the percentages of data falling within a certain range are approximations and may not be exact in every case.

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