This is your last opportunity for a blog entry. Your deadline for posting a reply is April 30th. Please prepare a powerpoint presentation of your project and post it here. This is an optional blog post. If you use a dataset, you would like to publish your results in the future, please do not post your presentation here (just send me a private e-mail with the Subject line: Blog post)
In Wainer’s analysis entitled “How to Display Data Badly”, he emphasized 12 separate ways in which data can be presented in a misleading and inefficient way in order to encourage better practices in data display. Wainer first discusses the consequences of showing as few data points as possible. Not including data points that are essential to analysis can lead to incorrect conclusions. He then discusses the importance of using good technique when one plots data, by using appropriate grid lines and scale. His next two tips are related to visual metaphors. He points out how important shading, size, and time scales can be, as well as the order of the visual metaphors. Wainer warns against graphing data out of context. Important data can often be left out if the presenter chooses to focus on a specific interval that does not include these data points. He also cautions against changing scales in mid-axis as this can have a profound impact on the way data is interpreted by making large changes in data look less significant and vice versa. Another important point that Wainer discusses is that one should not emphasize the trivial aspects of the data while ignoring the most important findings. There are several ways to do this with the techniques he previously discussed, and should be avoided. He refers to jiggling the baseline any time one makes comparison to the control or base unclear. Another tip he gives is to try to label graphs and tables by trend or some other related factor as opposed to simply listing alphabetically or in another way that also confuses comparison. He gives a few cautions in terms of labeling, warning his readers to always make sure they are labeling legibly, completely, correctly, and unambiguously. Wainer also takes the time to discuss the potential negatives involved in the inclusion of extraneous detail into the data, such as an overwhelming number of decimal points or a huge amount of variables in one graph. Lastly, he encourages his readers to learn from example. If a graph looks particularly good and represent data exceptionally, then don’t diverge from this method! Overall, Wainer’s points are all very valid in creating an effective guide for how one can accomplish presenting data well.
I obtained this data set, which details CO2 emission levels from petroleum use of 35 european union countries over the span of five years, from the U.S. Energy Information Association.
Let’s make a summer reading list for fun statistics books. Your list shouldn’t include textbooks on statistics, biostatistics or medical statistics. Once the name of the book is posted, you can’t repost the same name. Please include a book review (if you can) for your book suggestion. You have time until 11:00pm on April 25th to submit a post on this subject. Here’s an example
The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century by David Salsburg
Review by: Chris Olsen
Review can be found in
Journal of the American Statistical Association, Vol. 97, No. 458 (Jun., 2002), p. 650 Published by: American Statistical Association
Besides from the class handouts, please post SAS tutorial website/resources (cite the source) that has examples of the TH Computer Lab class material such as SAS functions (Contents, Freq, Mean, Univariate,…). Due date for this post is April 11th 2013. Here’s an example http://www.ats.ucla.edu/stat/sas/library/SASProcs_mf.htm. Once the source is posted, you are not allowed to post the same source again.