A boxplot indicates the quartiles of the data set. In this case we can see that the median or second quartile is 15. We can immediately eliminate

as a choice, since its median is 13.75, not to mention its smallest value is 10 rather than 1. The other choices all go from 1 to 26 like the boxplot indicates.

Now we need to find the data set with the correct first and third quartile. The boxplot indicates that our first quartile is 10.5. We can find Q1 by finding the median of the lower half, not including the median:

has Q1 of 12.5, found by taking the mean of 12 and 13. We can eliminate this choice.

has a Q1 of 6, found by taking the mean of 5 and 7. We can eliminate this choice.

has a Q1 of 10.5, which is exactly what we want. We can also verify that the median of the top half is 20.5.

In order to get this question right, we need to be able to distinguish between the first and the third quartile.

The third quartile, 19, is the median of the upper half of the data.

The first quartile, 9, is the median of the lower half of the data.

This means that the "correct" wrong statement is "the third quartile is 9," since 9 is the first quartile.

To convert the temperatures into the z-scores, subtract the mean temperature (61.7) from the daily temperatures and divide this by the given standard deviation (13). The daily z-scores are .02, .53, -.81, -1.8, 1.4, .86, and -.14. Recall that z-scores tell you how many standard deviations away from the mean a given observation is. 

Only Thursday and Friday are greater than one.

The first step in this problem is calculating the z-score. 

The next step is to look up 4 in the z-table. The value from the table is .

A -score indicates whether a particular value is typical for a population or data set.  The closer the -score is to 0, the closer the value is to the mean of the population and the more typical it is.  The -score is calculated by subtracting the mean of a population from the particular value in question, then dividing the result by the population's standard deviation. 

A Z-score indicates whether a particular value is typical for a population or data set.  The closer the Z-score is to 0, the closer the value is to the mean of the population and the more typical it is.  The Z-score is calculated by subtracting the mean of a population from the particular value in question, then dividing the result by the population's standard deviation.

To find the z-score, follow the formula

or

A z score is unique to each value within a population.

To find a z score, subtract the mean of a population from the particular value in question, then divide the result by the population's standard deviation.

We need to calculate Natalie's z-scores for both her Spanish and math exams. Calculating z-scores is as follows:

Her z-score for the Spanish exam is , which equals 1.25, while her z-score for the math exam is , which equals 1.50. Since both z-scores are positive, Natalie did above average on both tests, but since her z-score for the math exam is higher than her z-score for the Spanish exam, Natalie did better on her math exam when compared to the rest of her peers taking the exam.

To calculate the z-score, first we need to find the mean of the data set. By adding together and dividing by 26, we get 81.15.

To calculate your z-score and discover how close your score is to the mean in terms of standard deviations, use this formula:

where x is your data point, 86, is the mean, 81.15, and is the standard deviation, which we are told is 8.41.