Multicollinearity
{"Image DocumentName": "Multicollinearity", "Image ImageDescription": "Multicollinearity occurs when one or more of your independent variables are related to one another. The coefficient for each independent variable shows how much an increase of one in its value will change the dependent variable, holding all other independent variables constant. But what if you cannot hold them constant? If you have two houses that are exactly the same, and you add a bedroom to one of them, the value of the house may go up by, say,  10,000. But you have also added to its square footage. How much of that  10,000 is a result of the extra bedroom and how much is a result of the extra square footage? If the variables are very closely related, and/or if you have only a small number of observations, it can be difficult to separate these effects. Your regression gives you the coefficients that best describe your set of data, but the independent variables may not have a good p-value if multicollinearity is present. Sometimes it may be appropriate to remove a variable that is related to others, but it may not always be appropriate. In the home value example, both the number of bedrooms and the square footage are important on their own, in addition to whatever combined effects they may have. Removing them may be worse than leaving them in. This does not necessarily mean that the model as a whole is hurt, but it may mean that the model should not be used to draw conclusions about the relationship of individual independent variables with the dependent variable.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}