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Not your grandparents' regression analysis
This month's column is not for everyone. It is about regression analysis-the workhorse of statistical methods in the planning field. The column is a bit technical, so just read this column for the gist, and don't sweat the details. The point I want to highlight is that even experts can make mistakes in regression analysis. So enlist some help when you need it.
Regression analysis is a statistical method for estimating relationships between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the value of the dependent variable changes when any one of the independent variables is shifted while the other independent variables are held constant. For example: How does the price of homes vary with proximity to transit, when the floor area of the unit is held constant?
My guess is that almost everyone holding a master's degree in planning has been exposed to least squares regression (and possibly only least squares) in a statistics, quantitative methods, or research methods course. When I learned regression analysis in the early 1970s, we were taught that a good model meets three criteria:
* It has a high R2, meaning that the model explains most of the variation in the dependent variable.
* It has statistically significant regression coefficients for its independent variables (meaning that the variables are likely...