1. Post the histogram. How would you describe the distribution? (Symmetric, skew left, skew right? Is it unimodal or bimodal?)
2. After your histogram, you can use the "Edit tool" and play around with the number of classes, and even create your own class range. After playing around with this, which number of classes do you think is the best to show our data set? Why did you choose this value?
3. 21, 20, 24, 20, 24, 29, 30, 36, 38, 38, 30, 39, 38, 30, 33, 48, 45, 43, 49, 47, 43, 50, 44, 43, 42, 48, 55, 56, 61, 62, 71, 81

A histogram is a graphical summary that can be to help us visualize the shape and distribution of our data set. Histograms deal with numeric data sets, in which values are binned, and plotted like a bar graph with consecutive bars touching.

We can control the number of bins, which is important for us in visualization. Most histograms fare best when there are somewhere between 5 and 20 bins of data, and really depend on how many data points we have in the set, and how spread out they are. Sometimes, the bin breakout comes naturally (Maybe binning exam scores by letter grade), but other times, there is no clear-cut methodology for it. When we run into these situations, we can play around with the number of bins to try and get the best visualization of our data that we can

Once we create our histogram, we can describe the shape as follows:

Symmetric vs Skew

If a distribution is symmetric or approximately symmetric, the mean and median of the data set will be approximately equal to one another. If we were to draw a line at the median point, it will look roughly the same on both sides of the line.

If a distribution is skew, there will appear to be a "tail" associated with the data set. A data set that is skew left (or negatively skewed) will have the data points tail off on the left side of the histogram. The mean will be smaller than the median in a skew left distribution. A data set that is skew right (or positively skewed) will have the data points tail off on the right side of the histogram. The mean will be larger than the median in a skew right distribution.

Unimodal vs Bimodal

A distribution is considered unimodal if it has one distinct "peak" about it. This is a general peak and does not refer to every individual rise and fall of bars.

A distribution is considered to be bimodal if it has two distinct peaks about it. We often see bimodal distributions when we either combine two populations together, or the population of interest breaks itself up into two naturally occurring subgroups