If the data are not symmetric, then the data are either left-skewed or right-skewed. If the data are skewed, then the mean may not provide a good estimate for the center of the data and represent where most of the data fall. In this case, you should consider using the median to evaluate the center of the data, rather than the mean. Suppose you have a data set that contains the salaries of people who work at your organization. It would be interesting to know where the minimum and maximum values fall, and where you are relative to those values.
However, you can still observe an approximation for the range and see how spread out the data are. And you can answer questions such as "Is there a little bit of variability in my organization's salaries, or a lot? Outliers can be described as extremely low or high values that do not fall near any other data points.
Sometimes outliers represent unusual cases. The tool will create a histogram using the data you enter. A common pattern is the bell-shaped curve known as the "normal distribution. Note that other distributions look similar to the normal distribution. Statistical calculations must be used to prove a normal distribution. It's important to note that "normal" refers to the typical distribution for a particular process. For example, many processes have a natural limit on one side and will produce skewed distributions.
The skewed distribution is asymmetrical because a natural limit prevents outcomes on one side. For example, a distribution of analyses of a very pure product would be skewed, because the product cannot be more than percent pure.
Other examples of natural limits are holes that cannot be smaller than the diameter of the drill bit or call-handling times that cannot be less than zero. These distributions are called right- or left-skewed according to the direction of the tail. The bimodal distribution looks like the back of a two-humped camel. The outcomes of two processes with different distributions are combined in one set of data. Interpreting a histogram. Practice: Create histograms.
Practice: Read histograms. Next lesson. Current timeTotal duration Google Classroom Facebook Twitter. Video transcript Let's say you have a cherry pie store. Some pies might have over cherries, while other pies might have fewer than 50 cherries. So what you're curious about is what is the distribution, how many of different types of pies do you have? So, to do that, you set up a histogram.
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