The researchers compared two methods for estimating parameters in multiple regression analysis: bootstrap and jackknife resampling. They found that the bootstrap method had smaller bias and standard error values, as well as narrower confidence intervals, compared to the jackknife method. The coefficients estimated using the bootstrap method were also very similar to those obtained using the least squares method. Overall, the study showed that the bootstrap resampling method outperformed the jackknife method in this analysis.