All Prism can do is warn you about the problem. Multicollinearity is an intrinsic problem of multiple regression, and it can frustrate your ability to make sense of the data. Since the X variables are intertwined, multicollinearity will make it difficult to interpret the multiple regression results.

It is hard to separate the effects of weight and gender if the men weigh more than the women. It is hard to separate the effects of age and weight, if the older subjects tend to weigh more than the younger subjects.

The blood pressure example - model blood pressure as a function of age, weight and gender - is more typical. That example is a bit absurd, since the two variables are identical except for units. In this example, neither variable has any incremental effect on the model. The problem is that the P values only assess the incremental effect of each variable. When you see these results, you might mistakenly conclude that weight does not influence blood pressure at all since both weight variables have very high P values. After you include weight-in-pounds to the model, the goodness-of-fit is not improved by including the variable weight-in-kilograms. But the P value for weight-in-kilograms would also be high for the same reason. Since the equation has already taken into account the effect of weight-in-kilograms on blood pressure, adding the variable weight-in-pounds to the equation adds nothing. The P value for weight-in-pounds would be very high - after including the other variables in the equation, this one adds no new information. Then you'd look at the individual P values. The P value for the overall fit is likely to be low, telling you that blood pressure is linearly related to age and weight. The two X variables measure exactly the same thing - the only difference is that the two variables have different units. Now imagine that you've entered weight-in-pounds and weight-in-kilograms as two separate X variables. Imagine that you are running multiple regression to predict blood pressure from age and weight. To understand multicollinearity, first consider an absurd example. It is important to understand the concept of multicollinearity, as it can interfere with proper interpretation of multiple regression results.

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