Interpreting box plots2/27/2024 If you decide they are skewing your data too much, you can exclude them to focus on the otherwise normal patterns. 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. In this article I am going to discuss everything about box plots. 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. 25 of the sophomores spend between 48 & 60 minutes per night on homework. The TV box & whisker graph contains more data than the homework graph. The whiskers typically represent 1.5 times the min or max of the shaded Tableau box, or interquartile range (IQR). Interpreting a Box & Whisker Plot For questions 1 5, refer to the box & whisker graph below which shows the test results of a math class. It allows us to understand the nature of our data at a single glance. The top 25% is above the shaded Tableau box. Box plot packs all of this information about our data in a single concise diagram. The bottom 25% of the data is below the shaded Tableau box. Students will also use their inference skills to compare two box plots. Students walk through an investigation on interpreting center, spread, and the impact of outliers on various box plots. This shaded area is known as the Interquartile Range. This Desmos activity provides an interactive opportunity for students to work with data as represented between data and the box plot that results from it. 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. They provide a graphical rendition of statistical data based on the minimum, first quartile, median, third quartile, and maximum, also Outliers can be plotted. 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.
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