# Univariate and Bivariate Data

Univariate: one variable,

Bivariate: two variables

**Univariate** means "one variable" (one type of data)

### Example: Travel Time (minutes): **15, 29, 8, 42, 35, 21, 18, 42, 26**

The variable is **Travel Time**

### Example: Puppy Weights

You weigh the pups and get these results:

**2.5, 3.5, 3.3, 3.1, 2.6, 3.6, 2.4**

The variable is **Puppy Weight**

We can do lots of things with univariate data:

- Find a central value using mean, median and mode
- Find how spread out it is using range, quartiles and standard deviation
- Make plots like Bar Graphs, Pie Charts and Histograms

**Bivariate** means "two variables", in other words there are two types of data

With bivariate data we have **two** sets of related data we want to **compare**:

### Example: Sales vs Temperature

An ice cream shop keeps track of how much ice cream they sell versus the temperature on that day.

The two variables are **Ice Cream Sales** and **Temperature**.

Here are their figures for the last 12 days:

Ice Cream Sales vs Temperature | |

Temperature °C | Ice Cream Sales |
---|---|

14.2° | $215 |

16.4° | $325 |

11.9° | $185 |

15.2° | $332 |

18.5° | $406 |

22.1° | $522 |

19.4° | $412 |

25.1° | $614 |

23.4° | $544 |

18.1° | $421 |

22.6° | $445 |

17.2° | $408 |

And here is the same data as a Scatter Plot:

Now we can easily see that **warmer weather** and **more ice cream sales** are linked, but the relationship is not perfect.

So with bivariate data we are interested in **comparing** the two sets of data and finding any **relationships**.

We can use Tables, Scatter Plots, Correlation, Line of Best Fit, and plain old common sense.