![stata bootstrap stata bootstrap](http://repec.sowi.unibe.ch/stata/webdoc/formatting/5.png)
To demonstrate residual resampling, I will use procedures in Base SAS and SAS/STAT. Step 1: Fit a model, save predicted and residual values Analyze the bootstrap distribution to estimate standard errors and confidence intervals for the parameters.
#Stata bootstrap how to#
The following steps show how to bootstrap residuals in a regression analysis: As in the previous article, this article uses the bootstrap to examine the sampling distribution and variance of the parameter estimates (the regression coefficients). Therefore, you can compare the results of the two bootstrap methods. If so, do not use this bootstrap method.Īlthough residual resampling is primarily used for designed experiments, this article uses the same data set as in the previous article: the weights (Y) and heights (X) of 19 students. Before you run a residual-resampling bootstrap, you should use regression diagnostic plots to check whether there is an indication of heteroskedasticity or autocorrelation in the residuals. However, the errors do not need to be normally distributed. Residual resampling assumes that the model is correctly specified and that the error terms in the model are identically distributed and independent. This article shows how to implement residual resampling in Base SAS and in the SAS/IML matrix language. This article describes the second choice, which is resampling residuals (also called model-based resampling).
![stata bootstrap stata bootstrap](https://img-blog.csdnimg.cn/img_convert/2491bbe5548b77be5493f1eb0a783684.png)
The first, case resampling, is discussed in a previous article. If you want to bootstrap the parameters in a statistical regression model, you have two primary choices.