March 2007
Seminar Schedule
:: Regional Seminars
Risk and Decision Assessment using @RISK and the DecisionTools Suite
March 12-13, Denver
March 19-20, Ithaca
March 26-27, Calgary
April 2-3, Washington DC
April 17-18, Chicago

Project Risk Assessment
Using @RISK for Project

March 6-7, Los Angeles

:: Live Web Training
Risk and Decision Assessment using @RISK, Part I
April 23-24

Risk and Decision Assessment using @RISK, Part II
March 8-9
April 26-27

full schedule and registration

Free Live Webcasts
Value Optimization in
a World of Choices
March 15

Seasonal Plant
Optimization Project

April 5

full schedule and registration

Ask Amy
Expert Answers to
Technical Questions

Dear Amy,

How do I deal with Multicollinearity using StatTools? — O.K.

Dear O.K.,

If you recall, a regression equation indicates the effect of explanatory variables on the response variables, provided that the other variables in the equation remain constant. Another way of stating this is that the coefficient represents the effect of this explanatory variable on the response variable in addition to the effects of the other variables in the equation. Therefore, the relationship between an explanatory variable X and the response variable Y depends on which other X's are included or not included in the equation.

This is especially true when there is a linear relationship between two or more explanatory variables, in which case we have multicollinearity.

continue below

Ask Amy continued from above

“Multicollinearity” is defined as “the presence of a fairly strong linear relationship between two or more explanatory variables”, and it can make estimation difficult. Consider the following example. It is a very simple example, but it definitely serves the purpose of demonstrating the warnings of and how to deal with and recognize multicollinearity.

download this example file
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