What does univariate and bivariate mean in more detail?


In the context of this exercise, what does univariate and bivariate mean in regards to datasets?



Univariate datasets are essentially one-dimensional datasets, which have only one variable. For example, a dataset with Temperatures.

These types of datasets are used for distributions, which you may have worked with in the other lessons using histograms.

When plotting univariate datasets, KDE plots are usually preferred over histograms, because they do not use bins, and have a smoother and more accurate curve.


Bivariate datasets are two-dimensional datasets, having two variables. For example, we may have a dataset with the variables: Temperature and Amount of Ice Cream Sold. The variables can be dependent on each other, or they can even be independent from each other.

Usually, we use bivariate datasets to determine if there is a relation between the two variables. We typically visualize this using scatterplots, which are used to determine associations between the values.


Aren’t all datasets bivariate by definition? For example, every table has two values, x and y. If we measured temperature in an area over time in a dataset, that would be bivariate because it has two values, Time (the x value) and Temperature (the y value). Wouldn’t this mean that a univariate dataset is just an array or a list? e.g ([Temperature: 97, 88, 89, 92, 90])