1 / 15

Comparing Groups: Histograms, Boxplots, and Timeplots

Learn how to compare groups using histograms, boxplots, and timeplots. Discover the power of graphical displays in understanding patterns and trends in quantitative data.

engelhardt
Download Presentation

Comparing Groups: Histograms, Boxplots, and Timeplots

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 4 Stories Quantitative Data Tell

  2. Comparing Groups • It is almost always more interesting to compare groups. • With histograms, note the shapes, centers, and spreads of the two distributions. • What does this graphical display tell you?

  3. Comparing Groups (cont.) • Boxplots offer an ideal balance of information and simplicity, hiding the details while displaying the overall summary information. • We often plot them side by side for groups or categories we wish to compare. • What do these boxplots tell you?

  4. What About Outliers? • If there are any clear outliers and you are reporting the mean and standard deviation, report them with the outliers present and with the outliers removed. The differences may be quite revealing. • Note: The median and IQR are not likely to be affected by the outliers.

  5. 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.

  6. A time plot of interest

  7. Shifting Data (cont.) • The following histograms show a shift from men’s actual weights to kilograms above recommended weight (by subtracting 74 kg from each):

  8. Shifting Data (cont.) • What is center of the two distributions? • What is the IQR of the two distributions?

  9. Rescaling Data • Rescaling data: • When we divide or multiply all the data values by any constant value, all measures of position (such as the mean, median and percentiles) and measures of spread (such as the range, IQR, and standard deviation) are divided or multiplied by that same constant value.

  10. Rescaling Data (cont.) • The men’s weight data set measured weights in kilograms. If we want to think about these weights in pounds, we would rescalethe data (multiply by 2.2):

  11. Rescaling Data (cont.) • Where is the center of each distribution? • What is the range of each distribution?

  12. Rescaling Data (cont.) • What does the boxplot indicate about all measures?

  13. Avoid inconsistent scales, either within the display or when comparing two displays. Label clearly so a reader knows what the plot displays. Good intentions, bad plot: Beware of outliers. What Can Go Wrong? (cont.)

  14. What have we learned? • We’ve learned the value of comparing data groups and looking for patterns among groups and over time. • We’ve seen that boxplots are very effective for comparing groups graphically. • We’ve experienced the value of identifying and investigating outliers. • We’ve graphed data that has been measured over time against a time axis and looked for long-term trends both by eye and with a data smoother.

  15. What have we learned? (cont.) • We’ve learned that the story data can tell may be easier to understand after shifting or rescaling the data. • Sometimes we shift data by adding or subtracting the same amount, changing the center, but not the spread. • Sometimes we scale the data by multiplying or dividing by a constant, which changes all our summary statistics.

More Related