Suppose we calculate some variable of interest, y, as a function of some other variable x. We call y the dependent variable and x the independent variable. For example, consider the data set below, taken from a simple experiment involving a vehicle, its velocity versus time is tabulated. In this case, velocity is a function of time, thus velocity is the dependent variable and the time is the independent variable.

Time [s] | Velocity [m/s] |
---|---|

0 | 20 |

10 | 39 |

20 | 67 |

30 | 89 |

40 | 111 |

50 | 134 |

60 | 164 |

70 | 180 |

80 | 200 |

In its simplest form regression analysis involves fitting the best straight line relationship to explain how the variation in a dependent variable, y, depends on the variation in an independent variable, x. In our example above, once the relationship (in this case a linear relationship) has been estimated we can produce a linear equation in the following form:

And once an analytic equation such as the one above has been determined, dependent variables at intermediate independent values can be computed.