The use of multiple regression, when compared to simple bivariate regression, allows for more sophisticated and interesting analyses. The most important feature is the ability of the analyst (that’s you!) to statistically control for the effects of all other IVs when estimating any BB. In essence, we clean" the estimated relationship between any XX and YY of the influence of all other XsXs in the model. Hypothesis testing in multiple regression requires that we identify the independent variation in each XX, but otherwise the estimated standard error for each BB is analogous to that for simple regression.
So, maybe it’s a little more complicated. But look at what we can observe! Our estimates from the examples in this chapter show that age, income and education are all related to political ideology, but even when we control for their effects, ideology retains a potent influence on the perceived risks of climate change. Politics matter.