1. Introduction Measures of center and variability can be used to describe a data set. The mean and median are two measures of center. The mean is the average value of the data. The median is the middle-most value in a data set. These measures are used to generalize data sets and identify common or expected values. Interquartile range and mean absolute deviation describe variability of the data set. Interquartile range is the difference between the third and first quartiles. The first quartile is the median of the lower half of the data set. 1 4.1.1: Summarizing Numerical Data Sets

2. Introduction, continued The third quartile is the median of the upper half of the data set. The mean absolute deviation is the average absolute value of the difference between each data point and the mean. Measures of spread describe the variance of data values (how spread out they are), and identify the diversity of values in a data set. Measures of spread are used to help explain whether data values are very similar or very different. 2 4.1.1: Summarizing Numerical Data Sets

3. Key Concepts Data sets can be compared using measures of center and variability. Measures of center are used to generalize data sets and identify common or expected values. Two measures of center are the mean and median. 3 4.1.1: Summarizing Numerical Data Sets Finding the Mean 1.Find the sum of the data values. 2.Divide the sum by the number of data points. This is the mean.

4. Key Concepts, continued The mean is useful when data sets do not contain values that vary greatly. Median is a second measure of center. 4 4.1.1: Summarizing Numerical Data Sets Finding the Median 1.First arrange the data from least to greatest. 2.Count the number of data points. If there is an even number of data points, the median is the average of the two middle-most values. If there is an odd number of data points, the median is the middle- most value.

5. Key Concepts, continued The mean and median are both measures that describe the expected value of a data set. Measures of spread describe the range of data values in a data set. Mean absolute deviation and interquartile range describe variability. 5 4.1.1: Summarizing Numerical Data Sets

6. Key Concepts, continued The mean absolute deviation takes the average distance of the data points from the mean. This summarizes the variability of the data using one number. 6 4.1.1: Summarizing Numerical Data Sets Finding the Median Absolute Deviation 1.Find the mean. 2.Calculate the absolute value of the difference between each data value and the mean. 3.Determine the average of the differences found in step 2. This average is the mean absolute deviation.

7. Key Concepts, continued 7 4.1.1: Summarizing Numerical Data Sets Finding the Interquartile Range 1.Arrange the data from least to greatest. 2.Count the number of data points in the set. 3.Find the median of the data set. The median divides the data into two halves: the lower half and the upper half. 4.Find the middle-most value of the lower half of the data. The data to the left represents the first quartile, Q 1. 5.Find the middle-most value of the upper half of the data. The data to the right is the third quartile, Q 3. 6.Calculate the difference between the two quartiles, Q 3 – Q 1. The interquartile range is the difference between the third and first quartiles.

8. Key Concepts, continued The interquartile range finds the distance between the two data values that represent the middle 50% of the data. This summarizes the variability of the data using one number. 8 4.1.1: Summarizing Numerical Data Sets

9. Common Errors/Misconceptions confusing the terms mean and median, and how to calculate each measure forgetting to order data from least to greatest before calculating the median, quartiles, and interquartile range incorrectly finding the absolute value of the difference between each data value and the mean 9 4.1.1: Summarizing Numerical Data Sets

10. Guided Practice Example 3 A website captures information about each customer’s order. The total dollar amounts of the last 8 orders are listed in the table to the right. What is the mean absolute deviation of the data? 10 4.1.1: Summarizing Numerical Data Sets OrderDollar amount 121 215 322 426 524 621 717 822

11. Guided Practice: Example 3, continued 1.To find the mean absolute deviation of the data, start by finding the mean of the data set. 11 4.1.1: Summarizing Numerical Data Sets

12. Guided Practice: Example 3, continued 2.Find the sum of the data values, and divide the sum by the number of data values. 12 4.1.1: Summarizing Numerical Data Sets

13. Guided Practice: Example 3, continued 3.Find the absolute value of the difference between each data value and the mean: |data value – mean|. |21 – 21| = 0 |15 – 21| = 6 |22 – 21| = 1 |26 – 21| = 5 |24 – 21| = 3 |21 – 21| = 0 |17 – 21| = 4 |22 – 21| = 1 13 4.1.1: Summarizing Numerical Data Sets

14. Guided Practice: Example 3, continued 4.Find the sum of the absolute values of the differences. 0 + 6 + 1 + 5 + 3 + 0 + 4 + 1 = 20 14 4.1.1: Summarizing Numerical Data Sets

15. Guided Practice: Example 3, continued 5.Divide the sum of the absolute values of the differences by the number of data values. The mean absolute deviation of the dollar amounts of each order set is 2.5. This says that the average cost difference between the orders and the mean order is $2.50. 15 4.1.1: Summarizing Numerical Data Sets ✔

16. Guided Practice: Example 3, continued 16 4.1.1: Summarizing Numerical Data Sets

17. Guided Practice Example 4 A company keeps track of the age at which employees retire. It is considered an early retirement if the employee retires before turning 65. The age of the 11 employees who took early retirement this year are listed in the table below. Are there any striking deviations in the data? 17 4.1.1: Summarizing Numerical Data Sets

18. Guided P ractice: Example 4, continued 18 4.1.1: Summarizing Numerical Data Sets EmployeeAge at early retirement 156 255 360 451 553 658 756 864 959 1042 1148

19. Guided Practice: Example 4, continued 1.First find the interquartile range. 19 4.1.1: Summarizing Numerical Data Sets

20. Guided Practice: Example 4, continued 2.Order the data set from least to greatest. 42 48 51 53 55 56 56 58 59 60 64 20 4.1.1: Summarizing Numerical Data Sets

21. Guided Practice: Example 4, continued 3.Find the median of the data set. If there is an odd number of data values, find the middle-most value. If there is an even number of data values, find the average of the two middle-most values. There are 11 data values. The sixth data value is the middle-most value, and therefore is the median. The median of this data set is 56. 42 48 51 53 55 56 56 58 59 60 64 21 4.1.1: Summarizing Numerical Data Sets median

22. Guided Practice: Example 4, continued 4.Find the first quartile. The first quartile is the median of the lower half of the data set, or the values less than the median value. The first five data values are the lower half of the data set: 42, 48, 51, 53, and 55. The median of the first five data values is the middle-most value of these five values. The first quartile is the third value, 51. 42 48 51 53 55 56 56 58 59 60 64 22 4.1.1: Summarizing Numerical Data Sets medianQ1Q1

23. Guided Practice: Example 4, continued 5.Find the third quartile. The third quartile is the median of the upper half of the data set, or the values greater than the median value. The last five data values are the upper half of the data set: 56, 58, 59, 60, and 64. The median of the last five data values is the middle-most value of these five values. The third quartile is the third value, 59. 42 48 51 53 55 56 56 58 59 60 64 23 4.1.1: Summarizing Numerical Data Sets medianQ1Q1 Q3Q3

24. Guided Practice: Example 4, continued 6.Find the difference between the third and first quartiles: third quartile – first quartile, or Q 3 – Q 1. 59 – 51 = 8 The interquartile range is 8. 24 4.1.1: Summarizing Numerical Data Sets

25. Guided Practice: Example 4, continued 7.Look for striking deviations in the data. Think about the typical retirement age, which is 65. Also consider the interquartile range, which is 8. Retiring at the age of 42 is young and far away from the mean of 56. The age 42 would be considered a striking deviation because it is far away from the other data values. 25 4.1.1: Summarizing Numerical Data Sets ✔

26. Guided Practice: Example 4, continued 26 4.1.1: Summarizing Numerical Data Sets