**"distributions."**

The first thing you need to determine is if there is any

**symmetry**to the graph. If you were to visualize a vertical line going down the center, does each side look like a mirror image of the other? No real-life distribution will be perfectly symmetrical, but if it's close, it's worth mentioning.__Self-Test__): Which three of the above graphs look symmetric?

(

__Answer__): B, D, and E.

The next thing you might notice is that some graphs have peaks whereas others look pretty level. We call the peaks "

**modes**." If there's one peak, we say the graph has a

**unimodal distribution**. If there are two peaks, the graph has a

**bimodal distribution**.

(

__Self-Test__): Which three of the above graphs look unimodal? Which is bimodal?

(

__Answer__): A, B, and C are unimodal; D is bimodal.

FYI, a graph that's mostly level-looking, like graph E, is called

**uniform**.

Now take a look at distributions A and C. Do you see that each has a "tail" at one end? When a distribution is off-center (compared to graph B), we say it is skewed. The direction of the tail is the direction of the skewness. For example, graph A is

**skewed left**because the tail is on the left side of the graph. graph C is

**skewed right**.

When we describe a distribution we try to describe its

**shape**,

**center**, and

**spread**, plus anything

**unusual**about it, such as outliers. This will be the subject of my next post.

(

__Self-Test__): Which of the distributions pictured above

*might*have outliers?

(

__Answer__): Skewed graphs, like A and C, depending on the length of their tails. The longer the tail, the more likely there are outliers.

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