![]() ![]() In the end we have regression coefficients that estimate an independent variable’s effect on a specified quantile of our dependent variable. The math under the hood is a little different, but the interpretation is basically the same. That’s where quantile regression comes in. We could estimate the median, or the 0.25 quantile, or the 0.90 quantile. There is variability in the weights of 1st year UVa males and it appears height explains some of that variability.īut we don’t have to always estimate the conditional mean. We may find there is a positive relationship and that the mean weight of males 5’10” is higher than the mean weight of males 5’9″. But we could in theory take a random sample and discover there is a relationship between weight and height. For example the mean weight of 1st year UVa males is some unknown value. In other words, we’re pretty sure the mean of our variable of interest differs depending on other variables. When we think of regression we usually think of linear regression, the tried and true method for estimating a mean of some variable conditional on the levels or values of independent variables. ![]()
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