Chapter 7: Problem 25
The amounts dispensed by a cola machine follow the normal distribution with a mean of 7 ounces and a standard deviation of 0.10 ounces per cüp. How much cola is dispensed in the largest 1 percent of the cups?
Short Answer
Expert verified
About 7.233 ounces are dispensed in the largest 1% of cups.
Step by step solution
01
Understanding the Problem
The task is to find the amount of cola dispensed that falls into the largest 1% of the distribution. This refers to the value at the 99th percentile of the normal distribution with mean (\( \mu \)) = 7 ounces and standard deviation (\( \sigma \)) = 0.10 ounces.
02
Locate the Z-score
To find the amount dispensed in the largest 1%, we need to locate the Z-score that corresponds to the 99th percentile of the standard normal distribution. By using a standard normal distribution table, or a calculator, we find that Z ≈ 2.33.
03
Apply the Z-score Formula
Once we have the Z-score, we can use the formula for the value in a normal distribution: \[ X = \mu + Z \cdot \sigma \]Substitute \( \mu = 7 \) ounces, \( Z = 2.33 \), and \( \sigma = 0.10 \) ounces into the formula.
04
Calculate the Dispensed Amount
Substitute the known values into the formula: \[ X = 7 + 2.33 \cdot 0.10 \]Calculate:\[ X = 7 + 0.233 = 7.233 \] Thus, the amount of cola dispensed in the largest 1% of the cups is approximately 7.233 ounces.
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Key Concepts
These are the key concepts you need to understand to accurately answer the question.
Percentiles
In statistics, percentiles are a way to understand how a particular value compares to the rest of a dataset. When you look at a dataset as a whole, percentiles will help you determine how far or how high a certain value falls compared to others. For instance, the 50th percentile is also known as the median, meaning half of the data values fall below it.
- The 99th percentile indicates that 99% of the data values are below this point, and just 1% are above.
- Percentiles readily help in identifying edge cases or high flyers in your data.
Z-score
A Z-score is a measure of how many standard deviations a data point is away from the mean. In the context of a standard normal distribution, it helps you quantify the distance a certain value lies from the average or mean value. Z-scores play an essential role in various fields because they allow for comparison across different datasets, even if those datasets have different means and standard deviations.
To determine the Z-score corresponding to a specific percentile in a normal distribution:
To determine the Z-score corresponding to a specific percentile in a normal distribution:
- Standard normal distribution tables are used to find Z-scores for common percentile values. For example, a Z-score of 2.33 corresponds roughly to the 99th percentile.
- This means that about 1% of the distribution will have higher values than what corresponds to a Z-score of 2.33.
Mean
The mean is the average of a set of data points and is one of the central concepts in statistics. It provides a simple measure to understand where the center of the data distribution lies.
For example, if you add up all the values in a data set and then divide by the number of values, you've found the mean. This is incredibly useful in creating a basic understanding of the dataset's tendencies.
For example, if you add up all the values in a data set and then divide by the number of values, you've found the mean. This is incredibly useful in creating a basic understanding of the dataset's tendencies.
- The mean is denoted by the Greek letter \( \mu \) in statistical equations.
- A key property of the mean in a normal distribution is that it serves as the point around which the values are symmetrically distributed.
Standard Deviation
Standard deviation is a measure of how spread out the numbers in a data set are around the mean. It is crucial for assessing the variability within a dataset.
A dataset with a high standard deviation means that the data points are spread out over a wider range of values, while a low standard deviation signifies that the values are closely clustered around the mean.
A dataset with a high standard deviation means that the data points are spread out over a wider range of values, while a low standard deviation signifies that the values are closely clustered around the mean.
- In statistics, the standard deviation is denoted by the Greek letter \( \sigma \).
- An important property of the normal distribution is that approximately 68% of the data lies within 1 standard deviation of the mean, about 95% within 2 standard deviations, and roughly 99.7% within 3 standard deviations.