# 10.1 - Role of the Covariate

To illustrate the role the covariate has in the ANCOVA, let’s look at a hypothetical situation wherein investigators are comparing salaries of male vs. female college graduates. A random sample of 5 individuals for each gender is compiled, and a simple one-way ANOVA is performed:

Males | Females |

78 | 80 |

43 | 50 |

103 | 30 |

48 | 20 |

80 | 60 |

\[H_0: \mu_{\text{Males}}=\mu_{\text{Females}}\]

SAS coding for the One-way ANOVA (ancova_example_sascode.txt)

Here is the output we get:

To perform one-way ANOVA test in Minitab, you can first open the data (ancova_example_data.txt) and enter this into a Minitab worksheet.

Go to Stat > ANOVA > One Way…

In the pop-up window that appears, select salary as the Response and gender into Factor as shown below.

Click OK, and then here is the Minitab output that you get.

Because the *p*-value > α (.05), they can’t reject the H_{0}.

A plot of the data shows the situation:

However, they recognize that the length of time that someone has been out of college is likely to influence how much money they are making. So they also included a question asking how many years they have been out of college (ranging from 1 to 5 years for this sample):

Females | Males | ||

Salary | years | Salary | years |

80 | 5 | 78 | 3 |

50 | 3 | 43 | 1 |

30 | 2 | 103 | 5 |

20 | 1 | 48 | 2 |

60 | 4 | 80 | 4 |

We can see that indeed, there is a general trend for people to earn more the longer they are out of college. The fundamental idea of including a covariate is to take this trending into account and effectively ‘control for’ the number of years they have been out of college. In other words, we hope to include the covariate in the ANOVA so that the comparison between Males and Females can be made without the complicating factor of years out of college.