1. Displaying Quantitative Data AP STATS NHS Mr. Unruh

5. Slide 4- 5 Dotplots A dotplot is a simple display. It just places a dot along an axis for each case in the data. The dotplot to the right shows Kentucky Derby winning times, plotting each race as its own dot. You might see a dotplot displayed horizontally or vertically.

9. Slide 4- 9 Constructing a Stem-and-Leaf Display First, cut each data value into leading digits (“stems”) and trailing digits (“leaves”). Use the stems to label the bins. Use only one digit for each leaf—either round or truncate the data values to one decimal place after the stem.

10. Slide 4- 10 Stem-and-Leaf Example Compare the histogram and stem-and-leaf display for the pulse rates of 24 women at a health clinic. Which graphical display do you prefer?

13. Dealing With a Lot of Numbers… Summarizing the data will help us when we look at large sets of quantitative data. Without summaries of the data, it’s hard to grasp what the data tell us. The best thing to do is to make a picture… Bar charts and Pie Charts are for qualitative data Slide 4- 13

14. Histograms: Displaying the Distribution of Price Changes (cont.) A histogram plots the bin counts as the heights of bars (like a bar chart). Here is a histogram of the monthly price changes in Enron stock: Slide 4- 14

15. Slide 4- 15 Histograms: Displaying the Distribution of Price Changes (cont.) A relative frequency histogram displays the percentage of cases in each bin instead of the count. In this way, relative frequency histograms are faithful to the area principle. Here is a relative frequency histogram of the monthly price changes in Enron stock:

16. Slide 4- 16 Stem-and-Leaf Displays Stem-and-leaf displays show the distribution of a quantitative variable, like histograms do, while preserving the individual values. Stem-and-leaf displays contain all the information found in a histogram and, when carefully drawn, satisfy the area principle and show the distribution.

17. Examining a Distribution Look for the following: Shape - Does the distribution have one or more peaks (modes) or is it unimodal? Outliers – an individual value that falls outside the overall pattern. Center – What number is the data centered around? Spread - Is the distribution approximately symmetric or is it skewed in one direction? Is it skewed to the right (right tail longer) or left?

18. Slide 4- 18 Think Before You Draw, Again Remember the “Make a picture” rule? Now that we have options for data displays, you need to Think carefully about which type of display to make. Before making a stem-and-leaf display, a histogram, or a dotplot, check the Quantitative Data Condition: The data are values of a quantitative variable whose units are known.

19. Slide 4- 19 Describing a distribution of data When describing a distribution, make sure to always tell about four things: 1.) Shape 2.) Outliers 3.) Center 4.) Spread

20. Slide 4- 20 Humps and Bumps 1.Does the histogram have a single, central hump or several separated bumps? Humps in a histogram are called modes. A histogram with one main peak is dubbed unimodal; histograms with two peaks are bimodal; histograms with three or more peaks are called multimodal.

21. Slide 4- 21 What is the Shape of the Distribution? 1.Does the histogram have a single, central hump or several separated bumps? 2.Is the histogram symmetric? 3.Do any unusual features stick out?

22. Slide 4- 22 Humps and Bumps (cont.) A bimodal histogram has two apparent peaks:

23. Slide 4- 23 Humps and Bumps (cont.) A histogram that doesn’t appear to have any mode and in which all the bars are approximately the same height is called uniform:

24. Slide 4- 24 Symmetry 2.Is the histogram symmetric? If you can fold the histogram along a vertical line through the middle and have the edges match pretty closely, the histogram is symmetric.

25. Slide 4- 25 Symmetry (cont.) The (usually) thinner ends of a distribution are called the tails. If one tail stretches out farther than the other, the histogram is said to be skewed to the side of the longer tail. In the figure below, the histogram on the left is said to be skewed left, while the histogram on the right is said to be skewed right.

26. Slide 4- 26 Where is the Center of the Distribution? If you had to pick a single number to describe all the data what would you pick? It’s easy to find the center when a histogram is unimodal and symmetric— it’s right in the middle. On the other hand, it’s not so easy to find the center of a skewed histogram or a histogram with more than one mode. For now, we will “eyeball” the center of the distribution. In the next chapter we will find the center numerically.

27. Slide 4- 27 Anything Unusual? 3.Do any unusual features stick out? Sometimes it’s the unusual features that tell us something interesting or exciting about the data. You should always mention any stragglers, or outliers, that stand off away from the body of the distribution. Are there any gaps in the distribution? If so, we might have data from more than one group.

28. Slide 4- 28 Anything Unusual? (cont.) The following histogram has outliers—there are three cities in the leftmost bar:

29. Slide 4- 29 Comparing Distributions (cont.) Compare the following distributions of ages for female and male heart attack patients:

30. Slide 4- 30 Comparing Distributions Often we would like to compare two or more distributions instead of looking at one distribution by itself. When looking at two or more distributions, it is very important that the histograms have been put on the same scale. Otherwise, we cannot really compare the two distributions. When we compare distributions, we talk about the shape, center, and spread of each distribution.

31. Slide 4- 31 Timeplots: Order, Please! For some data sets, we are interested in how the data behave over time. In these cases, we construct timeplots of the data.

32. Slide 4- 32 *Re-expressing Skewed Data to Improve Symmetry Figure 4.11

33. Histogram of the average age of U.S. Presidents at Inaugaration

34. The purpose of an Exploratory Data Analysis is to organize data and identify patterns/departures. PLOT YOUR DATA - Choose an appropriate graph Look for overall pattern and departures from pattern Shape {mound, bimodal, skewed, uniform} Outliers {points clearly away from body of data} Center {What number “typifies” the data?} Spread {How “variable” are the data values?}