![how to interpret box and whisker plot how to interpret box and whisker plot](https://img.yumpu.com/41180763/1/500x640/interpreting-box-and-whisker-plots-worksheet-bw2.jpg)
The interquartile range is calculated as IQR = Q₃ − Q₁.Īny data points past the whiskers ends are considered as outliers and represented with circles or diamonds. The lower adjacent value is the furthest data point that is within 1.5 times the interquartile range(IQR) of the lower end of the box, and the upper adjacent value is the furthest data that is within 1.5 times the IQR of the upper end of the box.
![how to interpret box and whisker plot how to interpret box and whisker plot](https://i.ytimg.com/vi/fAbZ4o_eZxo/maxresdefault.jpg)
Quartiles are a special case of a type of statistics called quantiles, which are numbers dividing data into quantities of equal size.Įxtending from both the ends of the box plot are called whiskers, which extends till the adjacent values. The Third Quartile(Q3) is the 75 th percentile of the data. The First Quartile(Q1) is the 25 th percentile value of the data. If the median is closer to the top, then the distribution is negatively skewed. The distribution is positively skewed if the median is closer to the bottom. If the median is not in the middle of the box, then the distribution is skewed. The horizontal line represents the median of the data. The box represents two inner quartiles where 50% of the data resides, and it ranges from the first quartile to the third quartile. The first thing you might notice in the preceding diagram is a box that contains a horizontal line. The following diagram represents the box plot. It is created by plotting the five-number summary of the dataset: minimum, first quartile, median, third quartile, and maximum. It is a very convenient way to visualize the spread and skew of the data. If you decide they are skewing your data too much, you can exclude them to focus on the otherwise normal patterns.Box plot, also known as box-and-whisker plot, helps us to study the distribution of the data and to spot the outliers effectively. They indicate some anomaly in the data, like a data error, or they indicate examples where the normal pattern breaks down for a good reason, and understanding why can lead to major new insights. They are definitely worth investigating, and can often be the most useful pieces of data in your set. These are points that don’t follow the rest of the distribution. The points at the very end represent outliers, if there are any. So, the bottom whisker is 1.5x the min of the IQR, and the top whisker is 1.5x the max of the IQR. The whiskers typically represent 1.5 times the min or max of the shaded Tableau box, or interquartile range (IQR). The top 25% is above the shaded Tableau box. The bottom 25% of the data is below the shaded Tableau box. This shaded area is known as the Interquartile Range. The shaded area on each set of dots contains the middle 50% of all the data. The line in the middle of the shaded Tableau box, or the dividing point between the two colors, is the median or midpoint of all the data values in the range.
![how to interpret box and whisker plot how to interpret box and whisker plot](https://public.tableau.com/static/images/BW/BW_0/FundingperFTE/1_rss.png)
The 22 year old making $15,000 probably didn’t go to college and is working more of a minimum wage type job. The $100k per year 22 year old might be a data scientist, fresh out of school with a dual major in statistics and business. We can see that the average salary is just shy of $40,000, but that we have some outliers at $100,000 and $15,000. In the chart above, we can see the distribution of salaries of people in their 20’s (the first column in the chart). They show ranges of data, or distributions, across one or multiple segments. Tableau box plots are a simple way of accomplishing that. But, what if we want to see salary ranges per age range? That becomes a much harder problem to visualize. If we wanted to stratify salary ranges, we could take the same approach. We could use a histogram or bar chart to show how many people fall into each of the age buckets. Let’s say we wanted to see the breakout of ages for our employees. It allows you to compare a range of values across several segments.