1. D ID YOU SIGN UP FOR M Y S TAT L AB ? Slide 3- 1 1. Yes 2. No

2. U PCOMING I N C LASS Homework #2 due Sunday (9/1) at 10:00pm Quiz #1 in class 8/28 (open book) Part 1 of the Data Project due (9/4) Slide 4- 2

3. C HAPTER 4 Displaying and Summarizing Quantitative Data

4. D EALING W ITH A L OT OF N UMBERS … 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… Histograms, dot plots, stem & leaf display, box plot, time plot

5. US MEAT CONSUMPTION PER POUND, IN LBS. Slide 4- 5

6. O BESITY IN A MERICA, B Y THE N UMBERS Slide 4- 6

7. O BESITY IN A MERICA, B Y THE N UMBERS Slide 4- 7

8. H ISTOGRAMS : E ARTHQUAKE M AGNITUDES The chapter example discusses earthquake magnitudes First, slice up the entire span of values covered by the quantitative variable into equal-width piles called bins. The bins and the counts in each bin give the distribution of the quantitative variable. Slide 4- 8

9. H ISTOGRAMS : E ARTHQUAKE M AGNITUDES ( CONT.) A histogram plots the bin counts as the heights of bars (like a bar chart). Here is a histogram of earthquake magnitudes Slide 4- 9

10. H ISTOGRAMS : E ARTHQUAKE MAGNITUDES ( 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 earthquake magnitudes: Slide 4- 10

11. D OTPLOTS Slide 4- 11 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.

12. C ONSTRUCTING A S TEM - AND -L EAF D ISPLAY 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. Slide 4- 12

13. S TEM - AND -L EAF D ISPLAYS 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. Slide 4- 13

14. S HAPE, C ENTER, AND S PREAD When describing a distribution, make sure to always tell about three things: shape, center, and spread… Slide 4- 14

15. W HAT IS THE S HAPE OF THE D ISTRIBUTION ? 1.Does the histogram have a single, central hump or several separated humps? 2.Is the histogram symmetric? 3.Do any unusual features stick out? Slide 4- 15

16. H UMPS 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. Slide 4- 16

17. S YMMETRY 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. Slide 4- 17

18. S YMMETRY ( 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. Slide 4- 18

19. A NYTHING U NUSUAL ? 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. Slide 4- 19

20. Slide 4- 20

21. W HAT C AN G O W RONG ? Don’t make a histogram of a categorical variable—bar charts or pie charts should be used for categorical data. Don’t look for shape, center, and spread of a bar chart. Slide 4- 21

22. W HAT C AN G O W RONG ? ( CONT.) Choose a bin width appropriate to the data. Changing the bin width changes the appearance of the histogram: Slide 4- 22

23. W HERE IS THE CENTER ? Mean Median Mode Slide 4- 23

24. C ENTER OF A D ISTRIBUTION - M EAN When a distribution is unimodal and symmetric, most people will point to the center of a distribution. If we want to calculate a number, we can average the data. We use the Greek letter sigma to mean “sum” and write: The formula says that to find the mean, we add up the observations and divide by n (the number of observations).

25. C ENTER OF A D ISTRIBUTION – M EAN ( CONT ) The mean feels like the center because it is the point where the histogram balances: Slide 4- 25

26. C ENTER OF A D ISTRIBUTION – M EDIAN The median is the value with exactly half the data values below it and half above it. It is the middle data value (once the data values have been ordered) that divides the histogram into two equal areas. It has the same units as the data. Slide 4- 26

27. I F YOU HAVE A SKEWED DISTRIBUTION The mean or balancing point will be weighted more by outliers

28. M EAN OR M EDIAN ? ( CONT.) In symmetric distributions, the mean and median are approximately the same in value, so either measure of center may be used. For skewed data, though, it’s better to report the median than the mean as a measure of center. Slide 4- 28

29. W HICH IS LARGER MEAN OR MEDIAN Slide 4- 29 1. Mean> median b/c skewed to right 2. Median > mean b/c skewed to right 3. Mean and median are equal b/c symmetric

30. M EASURES OF SPREAD Range Interquartile range (IQR) Standard Deviation Slide 4- 30

31. S PREAD : H OME ON THE R ANGE Always report a measure of spread along with a measure of center when describing a distribution numerically. The range of the data is the difference between the maximum and minimum values: Range = max – min A disadvantage of the range is that a single extreme value can make it very large and, thus, not representative of the data overall. Slide 4- 31

32. S PREAD : T HE I NTERQUARTILE R ANGE The interquartile range (IQR) lets us ignore extreme data values and concentrate on the middle of the data. To find the IQR, we first need to know what quartiles are… Slide 4- 32

33. S PREAD : T HE I NTERQUARTILE R ANGE ( CONT.) The lower and upper quartiles are the 25 th and 75 th percentiles of the data, so… The IQR contains the middle 50% of the values of the distribution, as shown in Figure 4.13 from the text: Slide 4- 33

34. S PREAD : T HE I NTERQUARTILE R ANGE ( CONT.) Quartiles divide the data into four equal sections. One quarter of the data lies below the lower quartile, Q1 One quarter of the data lies above the upper quartile, Q3. The difference between the quartiles is the IQR, so IQR = Q3 - Q1 Slide 4- 34

35. W HAT A BOUT S PREAD ? T HE S TANDARD D EVIATION A more powerful measure of spread than the IQR is the standard deviation, which takes into account how far each data value is from the mean. A deviation is the distance that a data value is from the mean. Since adding all deviations together would total zero, we square each deviation and find an average of sorts for the deviations.

36. W HAT A BOUT S PREAD ? T HE S TANDARD D EVIATION ( CONT.) The variance, notated by s 2, is found by summing the squared deviations and (almost) averaging them: The variance will play a role later in our study, but it is problematic as a measure of spread—it is measured in squared units!

37. W HAT A BOUT S PREAD ? T HE S TANDARD D EVIATION ( CONT.) Slide 4- 37 The standard deviation, s, is just the square root of the variance and is measured in the same units as the original data.

38. T HINKING A BOUT V ARIATION Since Statistics is about variation, spread is an important fundamental concept of Statistics. Measures of spread help us talk about what we don’t know. When the data values are tightly clustered around the center of the distribution, the IQR and standard deviation will be small. When the data values are scattered far from the center, the IQR and standard deviation will be large. Slide 4- 38

39. W HAT MEASURES OF CENTER AND SPREAD ARE MOST APPROPRIATE ? Slide 4- 39 1. Median and St. Dev. 2. Median and IQR 3. Mean and St. Dev. 4. Mean and IQR

40. F ROM THE DISPLAY FIND THE Slide 4- 40 1. Median 2. IQR

41. Slide 4- 41

42. F OR N EXT T IME … Chapter 5 – comparing distributions