We learned that the mean takes into account each value in the distribution, and we saw that it’s fairly easy to define the mean algebraically. These two properties make the mean far superior to the median. The median comes in handy when it’s not possible or appropriate to compute the mean.
In this lesson, we’ll explore a couple of cases where neither the mean nor the median is suitable for finding an average value, and we’ll learn an alternative summary metric: the mode. We’ll also learn and look at the location of the mean, median, and mode in skewed and symmetrical distributions. You’ll learn how the mode can be applied to the three kinds of variables: ordinal, nominal, and discrete variables.
While exploring how the mode can be used to summarize a distribution, we’ll work with a dataset that describes characteristics of houses sold between 2006 and 2010 in Ames, Iowa.
As you work through each concept, you’ll apply what you’ve learned from within your browser; there’s no need to use your own machine to do the exercises. The Python environment inside of this course includes answer checking to ensure you’ve fully mastered each concept before moving on to the next.
- Learn what the mode is.
- Learn how and when to use the mode.
- Learn how summary metrics are located in skewed an symmetrical distributions.
- The Mode for Ordinal Variables
- The Mode for Nominal Variables
- The Mode for Discrete Variables
- Special Cases
- Skewed Distributions
- Challenge: Symmetrical Distributions
- Next steps