![]() ![]() ![]() A box plot of the annual revenue at each store can be used to determine the distribution of sales, including the minimum, maximum, and median values. Using a number or rate/ratio field on the y-axis.īox plots can answer questions about your data, such as: How is my data distributed? Are there any outliers in the dataset? What are the variations in the spread of several series in the dataset? ExamplesĪ market researcher is studying the performance of a retail chain. Outliers can reveal mistakes or unusual occurrences in data. They show the median, upper and lower quartiles, minimum and maximum There is one value about 278.38 so it is an outlier as well.Box plots provide a quick visual summary of the We probably should have checked to make sure that there aren't any outliers in the upper half of the data: When we make a box-and-whisker plot of this data, we represent 111 with a dot and only extend the lower whisker to the next smallest data value (182.4). It is! Since 111 is less than 166.57, 111 is officially an outlier. So, for the number in question (111) to qualify as an outlier in this example, it would have to be less than 166.57, which is the difference between Q1 (which is 208.5) and 41.93. Then we multiply that by 1.5 to get the number needed for our analysis of a possible outlier. However, we can't be sure until we check.įirst, we must calculate the IQR, which is Q3 – Q1. The lowest score (111) seems like it might be an outlier since it is so much smaller than the rest of the data. smaller than Q1 by at least 1.5 times the IQR.īelow are the individual final results for the men's large hill ski jumping event at the Winter Olympics.larger than Q3 by at least 1.5 times the interquartile range (IQR), or.In order to be an outlier, the data value must be: Our geometry test example did not have any outliers, even though the score of 53 seemed much smaller than the rest, it wasn't small enough. These are represented by a dot at either end of the plot. Outliers are values that are much bigger or smaller than the rest of the data. If you scored somewhere in the lower whisker, you may want to find a little more time to study. If your score was in the upper whisker, you could feel pretty proud that you scored better than 75% of your classmates. Using this plot we can see that 50% of the students scored between 69 and 87 points, 75% of the students scored lower than 87 points, and 50% scored above 79. Since there is an equal amount of data in each group, each of those sections represents 25% of the data. This plot is broken into four different groups: the lower whisker, the lower half of the box, the upper half of the box, and the upper whisker. Draw a box from Q1 to Q3 with a line dividing the box at Q2. Then extend "whiskers" from each end of the box to the extreme values. It should stretch a little beyond each extreme value. Create a number line that will contain all of the data values. Find the extreme values: these are the largest and smallest data values. Find the median of the data greater than Q2. Step 3: Find the median of the data less than Q2. Step 1: Order the data from least to greatest. Let's start by making a box-and-whisker plot (also known as a "box plot") of the geometry test scores we saw earlier:ĩ0, 94, 53, 68, 79, 84, 87, 72, 70, 69, 65, 89, 85, 83, 72 Extreme Values – the smallest and largest values in a data set.IQR – interquartile range, the difference from Q3 to Q1.Q3 – quartile 3, the median of the upper half of the data set.Q2 – quartile 2, the median of the entire data set.Q1 – quartile 1, the median of the lower half of the data set.There are a few important vocabulary terms to know in order to graph a box-and-whisker plot. It's a nice plot to use when analyzing how your data is skewed. The box-and-whisker plot doesn't show frequency, and it doesn't display each individual statistic, but it clearly shows where the middle of the data lies. Box-and-whisker plots are a handy way to display data broken into four quartiles, each with an equal number of data values. ![]()
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