When we are trying to determine the relationship between two variables, one of the relationships might be the equation of a straight line [i.e., y = (f)x.] With the least squares method, the team is using the linear equation. The linear equation represents the points found on the scatter diagram. In essence, the team is using the least squares criterion, meaning that the line fitted to the paired data points must be such that the sum of the squares of the vertical distances from the points to the line is as small as possible.
Use: We might want to predict the y values (the probabilities of rain) from collected x values (various humidity levels). We refer to the x variable (humidity) as the explanatory variable and the y variable (rain) as the response variable. In this particular case, we want to determine whether varying levels of humidity is having an effect on the probability of rain. It might not make sense to switch them around. In other words, it might not make sense that we are trying to determine whether the probability of rain is affecting humidity (although, that might be of interest to somebody.) So it does matter which one of the variables is the x, and which is the y.
