What should a histogram tell you

Outliers can be described as extremely low or high values that do not fall near any other data points. Sometimes outliers represent unusual cases. Other times they represent data entry errors, or perhaps data that does not belong with the other data of interest. Whatever the case may be, outliers can easily be identified using a histogram and should be investigated as they can shed interesting information about your data.

Rewind to the mids when scientists reported depleting ozone levels above Antarctica. The analysis they used automatically eliminated any Dobson readings below units because ozone levels that low were thought to be impossible. Minitab Blog. So there are 20 pies in this store. Then you can ask more a more nuance question. So using this histogram we can answer really interest question, which would say how many more pies do we have that have cherries than to cherries?

We have eight pies at cherries, three that have a We have five more pies in the 60 to 89 category that we do in the to categories A lot of questions that we can start to answer hopefully this gives you a sense of how you can interpret histograms. Creating a histogram. Create histograms. Up Next. The number of Bars for your Histogram will depend on the number of data points you collected.

Selecting the correct number of Bins is important as it can drastically affect the appearance of your data, which might lead you to the wrong conclusion. Below is a table from The Quality Toolbox that you can reference when selecting the proper number of Bars.

To do that you take the entire Range of the data Max data point minus Min data point and divide by the total number of Bins. Then you can divide your data Range 80 , by the total number of Bins, lets say 8 in this instance. They put together an Interactive Histogram that shows you how a Histogram changes when you play with the Width or Interval of each Bin.

So, the above instructions are how to create a manual Histogram graph, which you must know and understand for the CQE Exam. Below is an example of the Normal Distribution, in this distribution your data is evenly distributed and centered around your Mean value.

This type of distribution can often be interpreted that there is 1 primary source of variation that drives this distribution, however there can always be other smaller sources of variation that contribute to the total variation. Belwo is an example of the Bi-Modal Distribution. For Processes that display this distribution, it is normally understood that there are 2 independent sources of Variation that result in Peaks within the data.

Or, as in the data below, the data can reveal a shift in the process. Below is an example of the Multi-Modal Distribution. It is worth taking some time to test out different bin sizes to see how the distribution looks in each one, then choose the plot that represents the data best.

If you have too many bins, then the data distribution will look rough, and it will be difficult to discern the signal from the noise. On the other hand, with too few bins, the histogram will lack the details needed to discern any useful pattern from the data. Tick marks and labels typically should fall on the bin boundaries to best inform where the limits of each bar lies.

In addition, it is helpful if the labels are values with only a small number of significant figures to make them easy to read. This suggests that bins of size 1, 2, 2. A small word of caution: make sure you consider the types of values that your variable of interest takes. In the case of a fractional bin size like 2. A bin running from 0 to 2. As noted in the opening sections, a histogram is meant to depict the frequency distribution of a continuous numeric variable.

When our variable of interest does not fit this property, we need to use a different chart type instead: a bar chart. A variable that takes categorical values, like user type e. However, there are certain variable types that can be trickier to classify: those that take on discrete numeric values and those that take on time-based values.

Variables that take discrete numeric values e. Using a histogram will be more likely when there are a lot of different values to plot. When the range of numeric values is large, the fact that values are discrete tends to not be important and continuous grouping will be a good idea. One major thing to be careful of is that the numbers are representative of actual value.

A trickier case is when our variable of interest is a time-based feature. When values correspond to relative periods of time e. However, when values correspond to absolute times e. January 10, the distinction becomes blurry. When new data points are recorded, values will usually go into newly-created bins, rather than within an existing range of bins.

In addition, certain natural grouping choices, like by month or quarter, introduce slightly unequal bin sizes. For these reasons, it is not too unusual to see a different chart type like bar chart or line chart used. However, creating a histogram with bins of unequal size is not strictly a mistake, but doing so requires some major changes in how the histogram is created and can cause a lot of difficulties in interpretation. The technical point about histograms is that the total area of the bars represents the whole, and the area occupied by each bar represents the proportion of the whole contained in each bin.

When bin sizes are consistent, this makes measuring bar area and height equivalent. In a histogram with variable bin sizes, however, the height can no longer correspond with the total frequency of occurrences. In the center plot of the below figure, the bins from , , and end up looking like they contain more points than they actually do. Instead, the vertical axis needs to encode the frequency density per unit of bin size.