1. Descriptive Statistics
Chapter 2
Descriptive Statistics
Larson/Farber 4th ed.

2. Chapter Outline 2.1 Frequency Distributions and Their Graphs
2.2 More Graphs and Displays
2.3 Measures of Central Tendency
2.4 Measures of Variation
2.5 Measures of Position
Larson/Farber 4th ed.

3. Section 2.5
Measures of Position
Larson/Farber 4th ed.

4. Section 2.5 Objectives Determine the quartiles of a data set
Determine the interquartile range of a data set
Create a box-and-whisker plot
Interpret other fractiles such as percentiles
Determine and interpret the standard score (z-score)
Larson/Farber 4th ed.

5. Quartiles
Fractiles are numbers that partition (divide) an ordered data set into equal parts.
Quartiles approximately divide an ordered data set into four equal parts.
First quartile, Q1: About one quarter of the data fall on or below Q1.
Second quartile, Q2: About one half of the data fall on or below Q2 (median).
Third quartile, Q3: About three quarters of the data fall on or below Q3.
Larson/Farber 4th ed.

6. Example: Finding Quartiles
The test scores of 15 employees enrolled in a CPR training course are listed. Find the first, second, and third quartiles of the test scores
Solution:
Q2 divides the data set into two halves.
Lower half
Upper half Q2
Larson/Farber 4th ed.

7. Solution: Finding Quartiles
The first and third quartiles are the medians of the lower and upper halves of the data set.
Lower half
Upper half Q1 Q2 Q3
About one fourth of the employees scored 10 or less, about one half scored 15 or less; and about three fourths scored 18 or less.
Larson/Farber 4th ed.

8. Interquartile Range Interquartile Range (IQR)
The difference between the third and first quartiles.
IQR = Q3 – Q1
Larson/Farber 4th ed.

9. Example: Finding the Interquartile Range
Find the interquartile range of the test scores. Recall Q1 = 10, Q2 = 15, and Q3 = 18
Solution:
IQR = Q3 – Q1 = 18 – 10 = 8
The test scores in the middle portion of the data set vary by at most 8 points.
Larson/Farber 4th ed.

10. Box-and-Whisker Plot Box-and-whisker plot
Exploratory data analysis tool.
Highlights important features of a data set.
Requires (five-number summary):
Minimum entry
First quartile Q1
Median Q2
Third quartile Q3
Maximum entry
Larson/Farber 4th ed.

11. Drawing a Box-and-Whisker Plot
Find the five-number summary of the data set.
Construct a horizontal scale that spans the range of the data.
Plot the five numbers above the horizontal scale.
Draw a box above the horizontal scale from Q1 to Q3 and draw a vertical line in the box at Q2.
Draw whiskers from the box to the minimum and maximum entries.
Whisker
Maximum entry
Minimum entry
Box
Median, Q2 Q3 Q1
Larson/Farber 4th ed.

12. Example: Drawing a Box-and-Whisker Plot
Draw a box-and-whisker plot that represents the 15 test scores. Recall Min = 5 Q1 = 10 Q2 = 15 Q3 = 18 Max = 37
Solution: 5 10 15 18 37
About half the scores are between 10 and 18. By looking at the length of the right whisker, you can conclude 37 is a possible outlier.
Larson/Farber 4th ed.

13. Percentiles and Other Fractiles
Summary
Symbols
Quartiles
Divides data into 4 equal parts
Q1, Q2, Q3
Deciles
Divides data into 10 equal parts
D1, D2, D3,…, D9
Percentiles
Divides data into 100 equal parts
P1, P2, P3,…, P99
Larson/Farber 4th ed.

14. Example: Interpreting Percentiles
The ogive represents the cumulative frequency distribution for SAT test scores of college-bound students in a recent year. What test score represents the 72nd percentile? How should you interpret this? (Source: College Board Online)
Larson/Farber 4th ed.

15. Solution: Interpreting Percentiles
The 72nd percentile corresponds to a test score of This means that 72% of the students had an SAT score of 1700 or less.
Larson/Farber 4th ed.

16. The Standard Score Standard Score (z-score)
Represents the number of standard deviations a given value x falls from the mean μ.
Larson/Farber 4th ed.

17. Example: Comparing z-Scores from Different Data Sets
In 2007, Forest Whitaker won the Best Actor Oscar at age 45 for his role in the movie The Last King of Scotland. Helen Mirren won the Best Actress Oscar at age 61 for her role in The Queen. The mean age of all best actor winners is 43.7, with a standard deviation of 8.8. The mean age of all best actress winners is 36, with a standard deviation of Find the z-score that corresponds to the age for each actor or actress. Then compare your results.
Larson/Farber 4th ed.

18. Solution: Comparing z-Scores from Different Data Sets
Forest Whitaker
0.15 standard deviations above the mean
Helen Mirren
2.17 standard deviations above the mean
Larson/Farber 4th ed.

19. Solution: Comparing z-Scores from Different Data Sets
The z-score corresponding to the age of Helen Mirren is more than two standard deviations from the mean, so it is considered unusual. Compared to other Best Actress winners, she is relatively older, whereas the age of Forest Whitaker is only slightly higher than the average age of other Best Actor winners.
Larson/Farber 4th ed.

20. Section 2.5 Summary Determined the quartiles of a data set
Determined the interquartile range of a data set
Created a box-and-whisker plot
Interpreted other fractiles such as percentiles
Determined and interpreted the standard score (z-score)
Larson/Farber 4th ed.