1. Numerical descriptors BPS chapter 2 © 2006 W.H. Freeman and Company

2. Objectives (BPS chapter 2) Describing distributions with numbers  Measures of center: mean and median  Measures of spread: quartiles and standard deviation  The five-number summary and boxplots  IQR and outliers  Choosing among summary statistics  Using technology  Organizing a statistical problem

3. The mean or arithmetic average The data to the right are heights (in inches) of 25 women. How would you calculate the average, or mean, height of these 25 women? Sum of heights is 1598.3 Divided by 25 women = 63.9 inches Measure of center: the mean

4. The mean (page 38)

5. Mathematical notation: Let’s try an example with fewer numbers….Dr. L’s Test Score Data…

6. The mean or arithmetic average Consider the following sample of test scores from one of Dr. L.’s recent classes (max score = 100): 65, 65, 70, 75, 78, 80, 83, 87, 91, 94 What is the mean score? 78.8 Measure of center: the mean

7. The Mean as a Center of Mass  What happens when we average two numbers? What does the mean tell us?  Let’s draw both a dot plot and a stem and leaf plot of the test score data and look at where the mean falls… 65, 65, 70, 75, 78, 80, 83, 87, 91, 94

8. Your numerical summary must be meaningful Here the shape of the distribution is wildly irregular. Why? Could we have more than one plant species or phenotype? The distribution of women’s height appears coherent and symmetric. The mean is a good numerical summary. Height of 25 women in a class

9. 58 60 62 64 66 68 70 72 74 76 78 80 82 84 Sometimes a single numerical summary does not give the whole picture.

10. Measure of center: the median The median is the midpoint of a distribution—the number such that half of the observations are smaller and half are larger. 1. Sort observations from smallest to largest. n = number of observations ______________________________ n = 24  n/2 = 12 Median = (3.3+3.4) /2 = 3.35 3. If n is even, the median is the mean of the two center observations  n = 25 (n+1)/2 = 26/2 = 13 Median = 3.4 2. If n is odd, the median is observation (n+1)/2 down the list

11. Back to our test score example: Consider the following sample of test scores from one of Dr. L.’s recent classes (max score = 100): 65, 65, 70, 75, 78, 80, 83, 87, 91, 94 What is the median score? 79 Measure of center: the median

12. Comparing the Mean & Median  Test Scores: 65, 65, 70, 75, 78, 80, 83, 87, 91, 94  Let’s Use our TI-83 Calculators to Find the Mean & Median!  Enter data into a list via Stat|Edit  Use Stat|Calc|1-Var Stats  What happens to the Mean and Median if the lowest score was 20 instead of 65?  What happens to the Mean and Median if a low score of 20 is added to the data set (so we would now have 11 data points?) What can we say about the Mean versus the Median?

13. Mean and median for skewed distributions Mean and median for a symmetric distribution Left skewRight skew Mean Median Mean Median Mean Median Comparing the mean and the median The mean and the median are the similar when a distribution is symmetric. The median is a measure of center that is resistant to skew and outliers. The mean is not.

14. The median, on the other hand, is only slightly pulled to the right by the outliers (from 3.4 to 3.6). The mean is pulled to the right by the outliers high outliers (from 3.4 to 4.2). Percent of people dying Mean and median of a distribution with outliers Without the outliers With the outliers

15. Disease X: Mean and median have similar values Mean and median of a symmetric distribution Multiple myeloma: and a right-skewed distribution The mean is pulled toward the skew. Impact of skewed data

16. M = median = 3.4 Q 1 = first quartile = 2.2 Q 3 = third quartile = 4.35 Measure of spread : quartiles (page 42) The first quartile, Q 1, is the value in the sample that has 25% of the data at or below it. The third quartile, Q 3, is the value in the sample that has 75% of the data at or below it.

17. The Five Number Summary (page 43)

18. The Boxplot (page 44)  A graphical representation of the five number summary.

19. M = median = 3.4 Q 3 = third quartile = 4.35 Q 1 = first quartile = 2.2 Largest = max = 6.1 Smallest = min = 0.6 A relatively symmetric data set Center and spread in boxplots

20. Boxplots for skewed data Boxplots remain true to the data and clearly depict symmetry or skewness. Which boxplot is of the data in the top histogram? In the bottom histogram?

21. IQR and outliers (page 46, 47) The interquartile range (IQR) is the distance between the first and third quartiles (the length of the box in the boxplot) IQR = Q 3 - Q 1 An outlier is an individual value that falls outside the overall pattern.  How far outside the overall pattern does a value have to fall to be considered an outlier?  Low outlier: any value < Q 1 – 1.5 IQR  High outlier: any value > Q 3 + 1.5 IQR

22. Let’s Find the Five Number Summary, IQR, Box Plot, and where Outliers would be for the Test Score Data: 65, 65, 70, 75, 78, 80, 83, 87, 91, 94 What do we notice about symmetry?

23. Measures of Spread: Standard Deviation (page 48)  Other Measures of Spread  Data Range (Max – Min)  IQR (75% Quartile minus 25% Quartile, i.e. the range of the middle 50% of data) Standard Deviation (Variance)  Measures how the data deviates from the mean….hmm…how can we do this?

24. Computing Variance and Std. Dev. by Hand and Via the TI83:  Recall the Sample Test Score Data: 65, 65, 70, 75, 78, 80, 83, 87, 91, 94  Recall the Sample Mean (X bar) was 78.8  We want to measure how the data deviates from the mean 65707580908595 6583 78.8 -13.8 4.2 What does the number 4.2 measure? How about -13.8?

25. The standard deviation is used to describe the variation around the mean. 1) First calculate the variance s 2. 2) Then take the square root to get the standard deviation s. Measure of spread: standard deviation (page 48)

26. Calculations … We’ll never calculate these by hand, so make sure you know how to get the standard deviation using your calculator. Mean = 63.4 Sum of squared deviations from mean = 85.2 Degrees freedom (df) = (n − 1) = 13 s 2 = variance = 85.2/13 = 6.55 inches squared s = standard deviation = √6.55 = 2.56 inches Women’s height (inches)

27. Standard Deviation  On the next slide are histograms of quiz scores (from 1 to 10) for the same class but taught by different professors.  Sort the classes from largest to smallest based on the mean quiz score.  Sort the classes from largest to smallest based on the standard deviation of the quiz score.  Which professor would you want to have for this class?

28. Quiz Scores (Same Class, Different Professors)

29. Sorted by Mean Prof AProf BProf CProf DProf EProf F Mean =555557 Std Dev =2.043.052.633.333.841.28 Sorted by Standard Deviation Prof FProf AProf CProf BProf DProf E Mean =755555 Std Dev =1.282.042.633.053.333.84 Which professor would you want to take for this class?

30. Software output for summary statistics: Excel — From Menu: Tools/Data Analysis/ Descriptive Statistics Give common statistics of your sample data. Minitab

31. Choosing among summary statistics  Because the mean is not resistant to outliers or skew, use it to describe distributions that are fairly symmetric and don’t have outliers.  Plot the mean and use the standard deviation for error bars.  Otherwise, use the median in the five-number summary, which can be plotted as a boxplot. Box plot Mean ± s.d.

32. What should you use? When and why? Arithmetic mean or median?  Middletown is considering imposing an income tax on citizens. City hall wants a numerical summary of its citizens’ incomes to estimate the total tax base.  In a study of standard of living of typical families in Middletown, a sociologist makes a numerical summary of family income in that city.  Mean: Although income is likely to be right-skewed, the city government wants to know about the total tax base.  Median: The sociologist is interested in a “typical” family and wants to lessen the impact of extreme incomes.

33. Organizing a statistical problem (page 53)  State: What is the practical question, in the context of a real-world setting?  Formulate: What specific statistical operations does this problem call for?  Solve: Make the graphs and carry out the calculations needed for this problem.  Conclude: Give your practical conclusion in the setting of the real-world setting.