I am trying to determine if one of the numbers in regression summary provide the weight of the important of each variable?

Here is an example:

Say a sales manager wants to determine how much of a reps time should be allocated on the variables that predict whether a sale is made. Let's say the dependent variable is whether a sales is made, and the independent variables are Sales Training, Number of Prospecting Calls Made, and Sales Presentation. Is there any way to look at the regression summary to get an idea of the impact of each variables so you could see percentages such as Sales Training accounts for 50% of the prediction, Number of Prospect Calls 40% and Sales Presentation 10%.

Would the t value provide that? If I divided the t value of an individual variable by the sum of the t values for all the variables would it give me a percentage that would roughly tell me the importance of variable? I'm hoping you can point me in the right direction.

The partial coefficient of variation of partial correlation coefficient may be what you are looking for. It doesn't completely answer your question though. In almost all fields the coefficient of variation is misused. It gives an estimated percentage of variation that is explained by one or more variables, and thus it is not a coefficient of determination, so to speak.

An illustrative example of this problem is to consider body length. Variation in length is probably explained by genes to a degree of, say, 0.9. And the rest of the variation is explained by non biological factors. But this doesn't per se mean that length is determined by a degree of 0.9 by genes. It is still possible to alter your length extensively by altering non biological factors. For example, cut off your legs or stand below an elevator that smashes into you.

What do I want to say with this? Well, don't misinterpret a coefficient of variation by saying that "Y is determined by X to a degree of 90 percent".

If there wouldn't have been any variation in genes between humans then the coefficient of variation between genes and body length would be zero. Albeit this, length would still be determined to a great extent by genes.

Englund,
Thanks. I am not looking for an exact number. In your example where you are giving genes a 90% weight, where are you pulling that estimate from?

Keep your eye out for colinearity between your "independant" variables. The more correlated the independant variables are, the less robust this sort of interpriatation is.