While “big data” is one of the biggest buzzwords, we occasionally come cross data with very few data points. This dataset is from a study of the incidence rates of commonly performed medical procedures In Australia, which is available only at the state level. We have five states in Australia thus five data points. So what can we do when we only have five data points? One of the novelty approaches is to fit the data with a regression model. However given the challenging nature of the data with small size, no guarantee can be placed on the satisfactory of the linearity and homoscedasticity assumption of the linear regression, in turns, the inference from the standard linear model theory is no longer valid. Double bootstrap is used to provide solution to the valid statistical inference for the best linear approximation for the relationship between variables in an assumption-lean regression setting.