Adding Meaning to Regression

In any data analysis we should look for ability to predict and for connections to a broader comparative context. Our equations must not predict absurdities, even under extreme circumstances, if we want to be taken seriously as scientists. Poorly done linear regression analysis often does lead to absurd predictions. Fixed exponent and exponential patterns seem more prevalent in social nature than linear patterns. Before applying regression to two variables, graph them against each other, showing the borders of the conceptually allowed space and possible logical anchor points. Transform the data until anchor points and data points do fit a straight line which does not pierce conceptual ceilings or floors. During regression, consider symmetric regression, because Ordinary Least Squares y-on-x and x-on-y differ from each other and their slopes depend on the degree of scatter. After regression, look at the numerical values of parameters and ask what they tell us in a comparative context. When considering multivariable regression, pay more than lip service to Occam's Razor.