Glencoe Math: Course 3, Volume 2
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Glencoe Math: Course 3, Volume 2 View details
5. Measures of Variation
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Exercise 1 Page 712

The mean absolute deviation is the average of the absolute values of the differences between the mean and each value in the data set. Start by calculating the mean of the given set of numbers.

Mean Absolute Deviation:
Interpretation: The amount of milligrams of caffeine in certain types of tea is, on average, units away from the mean.

Practice makes perfect

The mean absolute deviation (MAD) is the average of the absolute values of the differences between the mean and each value in the data set. We will start by calculating the mean of the given set of numbers.

Mean

The mean of a data set or is calculated by finding the sum of all of the values in the set and then dividing by the number of values in the set. In this case, there are values. Let's now look at the table with given values.

Amount of Caffeine in Tea (milligrams)
Now, we will find the sum of the given values.
The mean of the set is

Mean Absolute Deviation

As previously stated, the MAD of a set of data is the average of the absolute values of the differences between the mean and each value in the data set.
In this formula, are the values in the set of data, is the mean, and is the number of values. We already know that and Let's use a table to find the sum of the absolute values of the differences.
Sum of Values
Finally, we need to divide by
A MAD of indicates that the data, on average, are units away from the mean. In the context of the problem, this means that the amount of caffeine in milligrams in certain types of tea is, on average, units away from the mean of milligrams. There are no outliers that influence this value.

Extra

Calculating Outliers

To check if there are any outliers, let's start by defining some important characteristics of data sets.

  • Lower Quartile (Q): is the median of the lower half of the data set.
  • Upper Quartile (Q): is the median of the upper half of the data set.
  • Interquartile Range: is the difference between the upper and lower quartiles
Let's find the upper quartile, the lower quartile, and the interquartile range for the given data set. Do not forget to write the values in numerical order!
We can use the upper and lower quartiles to find the interquartile range, or IQR.
An outlier is an extremely high or extremely low value when compared with the rest of the values in the set. To check for them, we look for data values that are beyond the upper or lower quartiles by more than times the interquartile range.
Let's find the minimum and maximum for a value not to be an outlier.
Any value between and is not an outlier. Thus, the data set has no outliers.