Comparing the Results to the Options

Oct 13, 2025 by recoveryshake.com 41 views

Introduction

In a department, a supervisor wants to find the mean number of miles that the employees live from work. The supervisor has collected data on the number of miles each employee lives from work and has calculated the mean and standard deviation of the data. The mean number of miles is given as $\bar{x}=29$ and the standard deviation is given as $s=3.6$ . The supervisor wants to know which mileage is within a $z$ -score of -1.5.

Understanding the Problem

To solve this problem, we need to understand what a $z$ -score is and how it is calculated. A $z$ -score is a measure of how many standard deviations an element is from the mean. It is calculated using the formula:

z = \frac{x - \bar{x}}{s}

where $x$ is the value of the element, $\bar{x}$ is the mean, and $s$ is the standard deviation.

Calculating the $z$ -Score

We are given that the $z$ -score is -1.5. We can use this information to calculate the value of $x$ using the formula:

-1.5 = \frac{x - 29}{3.6}

Solving for $x$

To solve for $x$ , we can multiply both sides of the equation by 3.6:

-1.5 \times 3.6 = x - 29

-5.4 = x - 29

Next, we can add 29 to both sides of the equation to isolate $x$ :

x = -5.4 + 29

x = 23.6

Interpreting the Results

The value of $x$ that we calculated is 23.6. This means that the mileage that is within a $z$ -score of -1.5 is 23.6 miles.

Comparing the Results to the Options

We are given four options: A. 21 miles, B. 24 miles, C. 36 miles, and D. 41 miles. We can compare our result to these options to see which one is correct.

Conclusion

Based on our calculation, the mileage that is within a $z$ -score of -1.5 is 23.6 miles. This is closest to option B. 24 miles.

Discussion

This problem requires the use of the $z$ -score formula to calculate the value of $x$ . The $z$ -score is a measure of how many standard deviations an element is from the mean. In this case, we are given the $z$ -score and the mean and standard deviation of the data, and we need to calculate the value of $x$ .

Example

Suppose we have a dataset with a mean of 25 and a standard deviation of 2. We want to find the value of $x$ that is within a $z$ -score of -2. We can use the formula:

-2 = \frac{x - 25}{2}

Solving for $x$ , we get:

x = -2 \times 2 + 25

x = 21

This means that the mileage that is within a $z$ -score of -2 is 21 miles.

Applications

This problem has many applications in real-life situations. For example, in quality control, we may want to know which products are within a certain range of the mean. In finance, we may want to know which investments are within a certain range of the mean return.

Limitations

This problem assumes that the data is normally distributed. If the data is not normally distributed, the $z$ -score formula may not be accurate.

Future Research

Future research could involve exploring other methods for calculating the value of $x$ when given the $z$ -score, mean, and standard deviation. It could also involve exploring the applications of the $z$ -score formula in real-life situations.

References

[1] Moore, D. S., & McCabe, G. P. (2013). Introduction to the practice of statistics. W.H. Freeman and Company.
[2] Larson, R. E., & Farber, B. A. (2018). Elementary statistics: Picturing the world. Cengage Learning.

Conclusion

In conclusion, the mileage that is within a $z$ -score of -1.5 is 23.6 miles. This is closest to option B. 24 miles. The $z$ -score formula is a useful tool for calculating the value of $x$ when given the $z$ -score, mean, and standard deviation. However, it assumes that the data is normally distributed. Future research could involve exploring other methods for calculating the value of $x$ and the applications of the $z$ -score formula in real-life situations.

Introduction

In our previous article, we discussed how a supervisor can find the mean number of miles that the employees in a department live from work. We also calculated the value of $x$ that is within a $z$ -score of -1.5. In this article, we will answer some frequently asked questions (FAQs) related to this topic.

Q&A

Q: What is a $z$ -score?

A: A $z$ -score is a measure of how many standard deviations an element is from the mean. It is calculated using the formula:

z = \frac{x - \bar{x}}{s}

where $x$ is the value of the element, $\bar{x}$ is the mean, and $s$ is the standard deviation.

Q: How do I calculate the value of $x$ when given the $z$ -score, mean, and standard deviation?

A: To calculate the value of $x$ , you can use the formula:

x = \bar{x} + z \times s

where $\bar{x}$ is the mean, $z$ is the $z$ -score, and $s$ is the standard deviation.

Q: What is the difference between a $z$ -score and a standard deviation?

A: A standard deviation is a measure of the spread of a dataset, while a $z$ -score is a measure of how many standard deviations an element is from the mean.

Q: Can I use the $z$ -score formula to calculate the value of $x$ when the data is not normally distributed?

A: No, the $z$ -score formula assumes that the data is normally distributed. If the data is not normally distributed, you may need to use a different method to calculate the value of $x$ .

Q: How do I interpret the results of the $z$ -score formula?

A: The results of the $z$ -score formula will give you the value of $x$ that is within a certain range of the mean. You can use this information to make decisions or take actions based on the data.

Q: Can I use the $z$ -score formula to calculate the value of $x$ when there are multiple means and standard deviations?

A: Yes, you can use the $z$ -score formula to calculate the value of $x$ when there are multiple means and standard deviations. However, you will need to use a different formula to calculate the $z$ -score for each mean and standard deviation.

Q: How do I choose the right $z$ -score value?

A: The choice of $z$ -score value will depend on the specific problem or situation you are trying to solve. You may need to use a different $z$ -score value depending on the level of precision or accuracy you need.

Q: Can I use the $z$ -score formula to calculate the value of $x$ when the data is missing or incomplete?

A: No, the $z$ -score formula assumes that the data is complete and accurate. If the data is missing or incomplete, you may need to use a different method to calculate the value of $x$ .

Conclusion

In conclusion, the $z$ -score formula is a useful tool for calculating the value of $x$ when given the $z$ -score, mean, and standard deviation. However, it assumes that the data is normally distributed and complete. You should carefully consider the limitations and assumptions of the $z$ -score formula before using it to calculate the value of $x$ .

Discussion

This Q&A article provides answers to some frequently asked questions related to the $z$ -score formula. The $z$ -score formula is a useful tool for calculating the value of $x$ when given the $z$ -score, mean, and standard deviation. However, it assumes that the data is normally distributed and complete. You should carefully consider the limitations and assumptions of the $z$ -score formula before using it to calculate the value of $x$ .

Example

Suppose we have a dataset with a mean of 25 and a standard deviation of 2. We want to find the value of $x$ that is within a $z$ -score of -2. We can use the formula:

x = 25 + (-2) \times 2

Solving for $x$ , we get:

x = 25 - 4

x = 21

This means that the mileage that is within a $z$ -score of -2 is 21 miles.

Applications

This Q&A article has many applications in real-life situations. For example, in quality control, we may want to know which products are within a certain range of the mean. In finance, we may want to know which investments are within a certain range of the mean return.

Limitations

This Q&A article assumes that the data is normally distributed and complete. If the data is not normally distributed or is missing or incomplete, you may need to use a different method to calculate the value of $x$ .

Future Research

References

[1] Moore, D. S., & McCabe, G. P. (2013). Introduction to the practice of statistics. W.H. Freeman and Company.
[2] Larson, R. E., & Farber, B. A. (2018). Elementary statistics: Picturing the world. Cengage Learning.