The goal of this master’s thesis is to give an introduction to the bootstrap (bootstrapping), especially for the linear regression model. The method is used for assessing uncertainty. It is useful when inference is to be based on a complex procedure for which theoretical results are unavailable or not useful for the sample sizes met in practice, where a standard model is suspect but it is unclear with what to replace it, or when ’quick and dirty’ answer is required. It can also be used to verify the usefulness of standard approximations for parametric models, and to improve them if they seem to give inadequate inferences. In the introduction you can find the description of the resampling methods (the jacckinife, the bootstrap, cross-validation and randomization test), the main principle of the bootstrap and a motivation example (estimate of the trimmed mean of the population). In the first chapter parametric, nonparametric and semiparametric bootstrap are explained with the given examples. The validity of the method and reducing error are argumented. In the second chapter the application on the regression model is shown. The methods of resampling the residuals and resampling the errors are described. The methods the examples are also given. In the end there is a conclusion about when to use bootstrap and when it is not recommended.