In this video, we define two terms that are essential to multiple regression, collinearity and parsimony. Two predictor variables are said to be collinear when they are correlated with each other. Remember: predictors are also called independent variables, so they should be independent of each other. In other words, they should not be collinear with each other. Inclusion of collinear predictors, also called multicollinearity complicates model estimation. And what we mean by complicates model estimation is that the estimates coming out of the model may no longer be reliable. Let's take a look back at our para weiss scatter plots and remind ourselves that earlier we fit a model predicting poverty from female householder and then we added the variable white or the percentage of white residents living in that state to the existing model. We saw that there were very little gains from adding the second explanatory variable, because our r squared went up by just a tiny bit. And in fact, our adjusted r squared did not go up at all. So why might that be the case? Let's take a look to see how the variables white and female householder are related to each other. We can see that there isn't much scatter in this scatter plot displaying the relationship between white and female householder. And, the correlation coefficient between these variables is quite high, indicating a strong negative relationship between the two variables. What that means is that the variable white is highly correlated with the variable female householder, and therefore they're not independent of each other. If that is the case, we wouldn't want to add the variable white to our existing model that already has female householder, because it's going to bring nothing new to the table. Any information that could be gleaned from the variable white, is probably already being captured by the variable female householder because these two variables are highly associated with each other. In addition, using both of these variables in the model is going to result in multicollinearity which we said might also result in unreliable estimates of the coefficients from the model. And that discussion brings us to a new term, parsimony. We want to avoid adding predictors that are associated with each other, because often times the addition of such variable brings nothing new to the table. We prefer the simplest best mode, in other words, the parsimonious model. This is the model that has the highest predictive power, however, has the lowest number of variables. This idea comes from Occam's razor, which states that among competing hypotheses, the one with the fewest assumptions should be selected. In other words, among models that are equally good, we want to select the one that has the fewer variables. We've also heard that addition of collinear variables can result in biased estimates of the regression parameters. So not only do we prefer simple parsimonious models, but we also want to be very careful about adding a bunch of explanatory variables to a model. Because if those are co-linear with each other, the model estimates may no longer be reliable. Lastly, while it is impossible to avoid co-linearity from arising in observational data, experiments are usually designed to control for correlated predictors.